Accurate Fall Detection and Localization for Elderly People Based on Neural Network and Energy-Efficient Wireless Sensor Network

Abstract

The degree of danger from a fall for aging persons is frequently decided by the location of the fall, time of fall detection, duration and time of transfer and rescue services. [...]automatic detection of elderly people’s falls along with the locations of the incident is important so that medical rescue staff can be dispatched immediately and so that the family of the elderly can be informed about the incident through a specific wireless network or mobile telephone. [...]the power consumption of the sensor node is improved in the present study with the use of a data-driven algorithm (DDA) along with a low-power wireless communication module (i.e., Zigbee) and a standalone microcontroller. [...]the CN supported by a monitoring system such as PC, tablet, and notepad can estimate the location of the fallen subject to be sent to the caregivers. 3.3. [...]the fall detection accuracy is satisfactory, and results indicate that the proposed FDS is energy efficient and can be used for accurate fall detection.

1. Introduction

The worldwide population of elderly who are more than 65 years old is expected to grow to 1 billion in 2030, and the percentage of individuals aged 20–64 years will become 35% of the population [1]. Therefore, monitoring the vital signs of elderly people is essential. In vital sign observation applications, patients and elderly people wear sensors that manage their vital parameters to detect emergency conditions and enable caregivers to respond efficiently. A fall is one of the key factors that can lead to injuries and decrease quality of life, at times resulting in the death of elderly persons. People’s rate of falling increases with their age. Falls occur frequently in medical health care centers, hospitals, or houses, with approximately 30% of falls causing injury. Falls in hospitals occur in the rooms of the patients (84%) and during transfer from one place to another (19%). Furthermore, the majority of falls occur in areas adjacent to chairs and beds [2]. Most people who experience falls need special care in a nursing home or hospital, thereby restricting their life activities. The hazard issues of fall or slight fall, especially of the elderly, can be aggravated by chronic diseases, such as osteoporosis, delirium, and dementia [3]. The degree of danger from a fall for aging persons is frequently decided by the location of the fall, time of fall detection, duration and time of transfer and rescue services. Therefore, automatic detection of elderly people’s falls along with the locations of the incident is important so that medical rescue staff can be dispatched immediately and so that the family of the elderly can be informed about the incident through a specific wireless network or mobile telephone.

The development of microelectromechanical technologies allows the integration of different sensors, and a wireless network is commonly used. Wireless sensor networks (WSNs) comprise a number of tiny and small sensor nodes which are deployed over several applications to monitor the physical environment (e.g., temperature, humidity, vibration, pressure, etc.) [4], patients vital signs (e.g., heart rate, body temperature, blood pressure, diabetes, motion, fall detection, etc.) [5], fog-supported smart city [6], fog-supported peer-to-peer WSN [7], etc. WSN have played a significant role in medical applications for monitoring elderly patients’ vital signs. Physiological parameters can be monitored in the elderly to evaluate their vital signs, such as blood pressure and sugar, heart functions, postures, fall, and the location of a fall incident. Among these parameters, fall detection and localization are considered in this study. The power consumption problem of the proposed fall detection system (FDS) is also addressed. Several fall detection and localization systems have been developed recently. Fall detection can be categorized into three systems, namely, (i) vision-based, (ii) ambient sensor-based, and (iii) wearable sensor-based [8]. Vision-based systems employ a computer to capture images and videos of fall events. This method can be subdivided into three types: single RGB camera, 3D-based methods utilizing depth cameras, and 3D-based methods employing several cameras [9]. This system monitors the shape and position of the subjects, which depends on image processing preprocessing and pattern recognition techniques. Although this system is convenient and non-obtrusive for elders, it is more expansive than the other two types because it needs many cameras. In addition, the RGB camera needs to be calibrated to allow a 3D reconstruction of the body, resulting in a time-consuming and computationally intensive procedure [10].

Ambient sensor-based systems utilize external sensors such as acoustic, electromyography, vibration, infrared, and pressure sensors. The sensors of the ambient sensor-based methods are used in the area around the subject of concern. Sensors are implanted in the surroundings and track the movements of the elderly. Pressure sensors are usually employed in this method because they are unobtrusive, low cost, and sensitive. The main shortcomings of these methods are low fall detection accuracy (less than 90%) [11], a tendency to generate false alarms when pressure sensors are used because the sensor senses the objects around the body, and the direct effect of distance between the body and sensor location on fall detection accuracy. Indoor environments should contain several sensors to obtain high accuracy, thereby increasing costs. With the development of microelectromechanical technologies in recent years, wearable sensor-based systems have become commonly used for fall detection. The sensors of the last method are attached to the subject of interest. The wearable sensor-based method usually depends on accelerometer sensors, which are connected to the body and provide an extraordinary level of obtrusiveness [8]. Accelerometers are popular wearable sensors embedded in FDSs to detect the position and movement of the subject [12]. The advantages of wearable accelerometer sensors are their small size, lower cost than external sensors, ability to be easily carried by the body [13], and ability to measure acceleration in three coordinates or angles of incidence. Thus, among the above three methods, the wearable sensor-based method using accelerometer sensors is considered in this study.

In elderly indoor localization, accurate estimation of node locations poses a challenge. One of the prevailing methods is a WSN integrated with several sensors to detect and localize the patient’s fall. Localization and power consumption are essential challenges in WSN, where elderly people who carry a battery power system may transfer from one place to another according to their activities. Thus, a localization technique that can independently measure or determine the location of the elderly is necessary. Various range-based and range-free localization techniques are employed in WSN localization. Range-based techniques are employed to identify the distances and angles among nodes in WSN. Some examples of range-based localization techniques are angle of arrival (AoA), time difference of arrival (TDoA), time of arrival (ToA) [14], received signal strength indicator (RSSI) [15], global positioning system (GPS) [16], and acoustic energy [17]. Conversely, range-free localization techniques are less accurate than range-based localization and are less cost effective. They rely on the transmission link among mobile nodes and beacon nodes (called beacons nodes) in the network to determine node positions, and they do not provide information about distance and angle. In this study, a sensor node or FDS is carried by elderly people with restricted energy sources and minimal equipment requirement. Range-free localization techniques that depend on received signal strength indicator (RSSI) measurements are adopted because they do not require additional hardware, thereby minimizing power consumption, cost, size, and system complexity.

In this paper, the fall and location of subjects are determined by an FDS wearable device. The FDS, which includes a digital tilt sensor, accelerometer sensor, shock sensor, microcontroller, Zigbee wireless protocol, and power unit, is wrapped around the waist of the subject. The waist is selected as the location because it allows high discrimination between activities with the accelerometer system [18]. Digital tilt, shock, and accelerometer sensors are utilized to detect the fall incident of the subject. The location of the incident is estimated by using the RSSI of three Zigbee beacons for line-of-sight (LOS) and non-line-of-sight (NLOS) indoor environments. The RSSI of the three Zigbee beacons is used because no additional hardware is required [19]. The beacons are used to determine the location of the subject (i.e., location of fall incident) based on an artificial neural network (ANN) technique. Location accuracy is a crucial factor for WSN applications [20] because it can reduce the power consumption of the WSN nodes. When the location or distance among the WSN nodes is measured accurately, the transmitted power of the sensor node’s transceiver radio module can be modulated to reduce power consumption, while still retaining connectivity, thereby prolonging battery life [21]. However, this approach is not considered in the current work. The fall alarm and raw RSSI data are sent to the coordinator node (CN) for elderly location estimation and immediate rescue. Thus, the survival probability of elderly people who have fallen can be increased, with a population effect of increased life expectancy.

Practical wearable devices carried by patients, elderly people, and athletes are battery powered. Thus, the power consumption of these devices must be considered. The radio frequency (RF) element generally consumes the most power in a wearable device [22]. Several studies on wearable telehealth and telecare systems state that their suggested systems were energy efficient. Most of these studies employed only low-power hardware, adopting a low-power communication module or a microcontroller. However, they neither considered power reduction techniques to improve the power consumption of their systems nor focused on realizing full system functionality. Accordingly, the power consumption of the sensor node is improved in the present study with the use of a data-driven algorithm (DDA) along with a low-power wireless communication module (i.e., Zigbee) and a standalone microcontroller. With the use of low-power components and DDA, the FDS battery lifetime is extended. In DDA, the FDS can enter sleep mode when no information of an elderly fall (i.e., the normal case) is ready for transmission to the CN. Once a fall happens, the sensor node wakes up for a short time to send the fall alarm and location information (i.e., RSSI values) to the CN and then returns to sleep mode to conserve energy. Instead of continuously monitoring the patients’ vital signs, the FDS transmits data at sparse events.

The contributions of this paper are as follows:

1. Fall detection of elderly people is accurately detected based on FDS carried by the patient

2. The location of the fall is determined with minimum localization error based on ANN technique in indoor environments for LOS and NLOS scenarios.

3. The power consumption of the FDS carried by the subject is minimized based on DDA.

4. Fall detection accuracy achieved using sensor-based fall detection algorithm (S-BFDA), localization error based on ANN technique, and FDS power consumption based on DDA are compared with those of state-of-the-art systems.

2. Related Works

Different approaches or schemes have been used to determine or detect the fall of the elderly considering several application scenarios. Three categories of elderly FDSs are available, as highlighted in the introduction. Wearable sensor-based approaches have an advantage over the other two approaches because such approaches involve attaching the device to the subject which then moves with them. Such systems need sensors such as gyroscopes and accelerometers. Smartphones can be efficiently employed for elderly fall detection because most mobile smartphones include these sensors [23]. Many studies on fall detection applications show the applicability of Zigbee or Bluetooth radio solutions in wearable wireless sensor devices. One study [3] designed and implemented a care system that can detect and localize the fall of a human. The proposed system architecture consisted of a portable device such as a mobile phone or a tablet, the Zigbee wireless protocol as an access point, a console, a server, and an end-user mobile phone. The portable device automatically detects the fall of a subject and sends an alert message to the nursing center via the Zigbee access point. The proposed system achieved an average fall detection accuracy of 99.9%. In addition, the system could determine the location of the fall incident based on the triangulation method to help medical staff find the patient quickly. The proposed system was efficient in terms of fall accuracy and power consumption, as the system could work continuously for 1 month without the need for a battery change and could send fall and location information more than 500 times.

In Reference [24], the author proposed a wireless body area network (WBAN) for fall detection applications and optimized the energy efficiency and throughput of the Zigbee communication protocol. A fall detection algorithm was implemented based on an accelerometer sensor worn on the subject’s waist and could detect eight different fall cases. Results demonstrated that 100% sensitivity and specificity could be expected when the fall occurred in the vertical position (i.e., falls sliding against a wall ending in vertical position). The power consumption of the WBAN was minimized and was 10 times less based on the sleep/wake mode of the Zigbee, which transmitted data every 5 s and slept the rest of the time. In Reference [25], an outdoor healthcare monitoring system that could monitor electrocardiogram (ECG) signal and fall events of elderly people was designed and implemented. The falling position of the elderly was determined through GPS, and fall events could be detected based on gravitational acceleration and altitude difference. The ECG signal was used alongside GPS to increase the accuracy of FDS. The healthcare system consisted of an ECG module, a GPS module, a microprocessor embedded in a field programmable gate array (FPGA) board, and the Zigbee module. The Zigbee transmitted the fall event and localization information to the receiving Zigbee connected with the computer server. The information in the computer was then transferred to the healthcare center via Ethernet. Results showed 95% fall detection accuracy with ECG and 80% localization accuracy with GPS. The outdoor healthcare monitoring system consumed more power when it continuously monitored for subject fall and position. The system was suitable for outdoor applications; however, GPS would not work in indoor environments [26].

A combination of accelerometer and electromyography was considered in Reference [27] to monitor the daily activity of the elderly and patients and to detect their falls. The proposed method achieved 98% recognition accuracy. Force sensor fusion and accelerometer decision were merged to efficiently to detect the fall of elderly people. The force sensor could detect, locate, and track elderly people, as well as identify their activities such as standing, sitting, walking, falling, lying down, and the transitions between their activities. Zigbee wireless protocol was employed to send the information of the force and accelerometer sensors to the neighboring node and any agent node. However, the system was costly and consumes a large amount of power because the accelerometer and force sensors are installed under tiles in large numbers. In Reference [28], a wireless patient monitoring system was proposed to monitor the patient fall, ECG signal, and indoor location of the fall incident. An accelerometer was used to monitor the patient fall, and indoor positioning was determined based on Zigbee wireless mesh networks. The proposed system consisted of sensor node, router node, and sink node. When a fall occurred, the vital signs of the patient were transmitted to the sink node through the Zigbee network. The Zigbee was configured to enter sleep mode in normal conditions to save energy and woke up during an abnormal event to transmit the patient’s vital signs. With the sleep/wake strategy, the Zigbee could work continuously for 30 h using an 800 mAh battery.

Fall detection was distinguished from the daily activities of the subject in Reference [29] by using six artificial intelligence techniques, namely, support vector machine (SVM), k-nearest neighbor (k-NN), Bayesian decision making, least squares method (LSM), ANN, and dynamic time warping. Six sensor units were used to detect falls. Each unit supported a single intelligent technique and included three sensors (i.e., accelerometer, magnetometer/compass, and gyroscope). Sensor measurements were transmitted to the remote PC through Zigbee wireless protocol. The authors deduced that the k-NN and LSM achieved the best results of 99% in terms of accuracy, specificity, and sensitivity. In Reference [30], a sensor node that could precisely detect human movement and fall was developed to meet size, weight, and responsiveness requirements by employing data from gyroscopes, accelerometers, and magnetometers. Zigbee wireless protocol was used to create a WBAN and then transmit the collected data from body sensors to a remote server (RS) when the subject was inside the home. The access points inside the home were created through Zigbee to reduce the power consumption of the network. Conversely, when the subject is outside his or her home, the data could be transmitted from WBAN to RS via Bluetooth using a smartphone device.

In Reference [31], a fall detection wearable device was proposed based on an accelerometer sensor, FPGA, and Zigbee wireless protocol. A low-cost algorithm was implemented in FPGA to detect the fall event. Zigbee sends raw data about the fall to an outside computing board for data analysis every 100 ms. The power consumption of the wearable device is minimized with the sleep/wake scheme, where Zigbee is activated only when a fall event happens. Otherwise, it is deactivated to conserve the energy of the wearable device. The power consumption of Zigbee was approximately 190 and 9 mW in active and deactivate modes, respectively. In Reference [32], a wearable elderly fall detection prototype was designed and presented. A novel algorithm was implemented inside a microcontroller to detect the fall of a subject. A wearable system relay on the sensor node includes a three-axis accelerometer, a microcontroller, and the Zigbee wireless protocol. Subject fall detection data could be transmitted to the gateway (i.e., portable computer) via Zigbee. Experimental results indicate that the system had 96.25% accuracy, sensitivity of 98.75%, and specificity of 94.38%. However, the power consumption of the sensor node increased once the sampling rate was set to 100 Hz.

Recently, the authors of Reference [33] studied the best location of fall detection sensors (i.e., gyroscope, accelerometer, and magnetometer) on a patients’ body to ensure a minimum number of sensor nodes in the proposed system, and employed Zigbee for data transmission. The authors concluded that the waist is the best location for a single fall detection sensor, achieving 99.96% sensitivity based on the k-NN algorithm. In Reference [34], a homecare system was designed and implemented for fall detection and localization in a hospital. A tri-axial accelerometer was employed for fall detection, and the fall algorithm was embedded in a microcontroller for analyzing the accelerometer measurements. The proposed homecare system wakes up every second (taking advantage of the internal interruption of the microcontroller) to transmit the fall and location information to nursing staff through the IEEE 802.15.4 standard.

The k-NN algorithm was presented in Reference [35] to develop a smartphone device for fall detection and warning. The proposed system consists of a smartphone and a wearable motion sensor platform integrated with a gyroscope, a three-axis accelerometer, a microcontroller, and a Bluetooth module. The proposed system was tied to a custom T-shirt worn by the elderly people to measure the angular velocity and acceleration of their daily living activities in real time. The velocity and acceleration data were sent via Bluetooth to a smartphone, which engages a program relay on the kNN algorithm to examine data and sense falls in the background. The smartphone then sent messages to the health center via the GSM network. A high fall detection accuracy of 97.7% was obtained, and specificity and sensitivity are 99% and 94%, respectively. In Reference [36], the physiological parameters (i.e., pulse rate, blood pressure, and temperature) of elderly people were monitored in addition to their falls. Falls were detected by a wrist sensor device, which included an accelerometer, a microcontroller, and a Bluetooth module. Physiological and fall information are sent to a tablet PC via Bluetooth to inform the medical staff about the conditions of the elderly people. Irregular conditions were automatically recognized, and warning messages were transmitted to the health center in real time. Experimental results demonstrated that the fall detection accuracy was 80%. Sensitivity and specificity were 87.5% and 75%, respectively, for a hard fall from standing position to the floor. However, the wrist sensor device was expected to consume a considerable amount of power because it continuously monitors physiological parameters and elderly falls during the test period.

An FDS based on Bluetooth module, microcontroller (ATmega88, Atmel, San Jose, CA, USA), and accelerometer (ADXL345B, Analog Devices, MA, USA) was designed and implemented by Oporto and Vilcahuaman [37]. The Bluetooth module sent an alert signal about the fall event through a mobile telephone that then transmitted an SMS to a health center or ambulance to request medical assistance. Low-power components were selected to reduce the overall power consumption of the FDS. A gyroscope and a three-axis accelerometer were integrated into a wearable sensor system (WSS) in Reference [38] to detect the fall of elderly adults. The WSS includes sensor modules (i.e., gyroscope and three-axis accelerometer), a microcontroller unit module, and a Wi-Fi module. The WSS was located on the chest of the subject to gather real-time motion information on daily activities such as walking, stepping, running, and falling. A detection algorithm was developed to precisely identify the activities of the elderly. The WSS transmitted the subject fall information to the receiving computer for real-time analysis of angular velocity and acceleration. Experimental results indicated that the proposed algorithm detects the subject’s fall relative to other daily activities with a sensitivity of 96.3% and a specificity of 96.2%. However, the power consumption of the module (i.e., Wifly RN131) was still high and consumed 40 mA in receiving mode and 210 mA in transmitting mode [39].

Currently, smartphone devices that comprise a wide set of implanted components include several sensors such as accelerometers, digital compasses, microphones, cameras, GPS, and gyroscope components. Wearable equipment and the acceleration measurement for fall detection can be supported because of the gradual decrease in smartphone cost. Accordingly, several smartphone devices have been used for fall detection over the last few years, such as in References [40,41,42,43,44,45,46,47,48]. However, in Reference [40], extra power was consumed by the Android monitoring system, as indicated by battery drain analysis in an assessment of the feasibility of FDSs [40]. The location of the subject was estimated based on tag and cellular localization [49] inside a hospital because localization accuracy is less critical in homecare systems. The objective of localization is to determine the presence of the subject in a particular area inside the hospital. Results indicated that fall sensitivity and specificity were 98.33% and 97.77%, respectively. The homecare monitoring system functioned for 3 weeks or approximately 490 h and could constantly monitor two elderly persons round the clock.

In Reference [50], a novel fall detection algorithm was developed to monitor movements and to distinguish a fall from regular daily activities of patients using a wearable device. In case the patient falls, the wearable device automatically transmits the patient’s location and requests caregivers for assistance. The software and hardware investigation of the wearable device essentially relied on a single accelerometer, microcontroller, GPS, and GSM module. An efficient fall detection quaternion algorithm was designed for outdoor applications with minimal resources. The wearable device was designed to work for 2 days only. Other approaches used to detect fall incidents are the use of a camera and SVM [51,52,53]. In Reference [51], the proposed FDS differs from others because it can work even in dark conditions. SVM was employed to determine if fall motion occurs. On the basis of the SVM method, the proposed method outperformed state-of-the-art methods in terms of accuracy (97.6%), with sensitivity and specificity of 95.3% and 100%, respectively.

Some studies such as References [54,55,56] adopted multiple-input multiple-output array [50] and Doppler radar [51,52] to sense and identify elderly fall and no-fall situations. However, the radar method has the following limitations and challenges: (i) it responds to any movement from other humans or non-human sources; (ii) the radar is exposed to jitter, which generates time-dependent additional noise and false person tracking; (iii) the detected person must be within the position of the antenna beam width; (iv) the reflected signal caused by the target is blocked by furniture; and (v) the person detection distance might represent a restriction, where the reflected signal is weak when the distance is increased [56]. A novel approach was presented in Reference [57]. It used fuzzy logic to identify the range and type of elderly fall, which includes position before fall, fall direction, fall velocity, and post-fall inactivity. Fuzzy logic facilitates elderly fall detection and categorizes it into one of the potential fall patterns. In addition, fuzzy logic could reduce the restrictions in parameter evaluation to introduce flexible and smooth decisions. Experimental results show that the proposed approach could sense dynamic and beacon falls and distinguish actual falls from wrongly identified falls.

The work presented here differs from these previous studies because it combines the measurements of tilt sensor, accelerometer sensor, and shock sensor to decide whether a fall event is happening or not. If the fall is real, then the FDS sends an alert signal to the CN to ensure that proper medical care is delivered instantly. The elderly fall incident is accurately estimated based on established artificial intelligence techniques such as ANN. The ANN was considered because of its minimum localization error as well as for its efficiency and speed compared to others localization techniques such as fingerprinting techniques. Where the fingerprinting techniques require memory cost, the setup is time consuming and any variation in network configuration like the adding of a new beacon, will imply forming a new database, meaning this technique is not so flexible. The FDS includes a simple standalone microcontroller (based on ATmega328P) and Zigbee wireless protocol in addition to the above three sensors. All these elements are embedded in a single sensor node. The option of a standalone microcontroller is considered to reduce the power consumption of the proposed system. The power consumption is significantly reduced by implementing a DDA, where the Zigbee transmits the data in the case of the subject falling and sleeps the rest of the time. Moreover, the size, weight, and complexity of the proposed system are minimized.

3. Methodology 3.1. Wireless Sensor Network for Elderly Fall Detection

The proposed elderly fall and localization WSN topology is applied in two indoor environment scenarios, namely, LOS and NLOS. A test area with a size of 40 × 30 m² is the location for the trial implementation of the system, as shown in Figure 1. The WSN consists of six nodes; three beacon nodes (N1, N2, and N3), a sensor node (i.e., FDS), a router node (RN), and CN or sink node. Each beacon node includes Zigbee wireless protocols (XBee Series 2) powered from the main source, eliminating concerns about power consumption. The beacon nodes N1, N2, and N3 are fixed at the coordinates (0, 0), (0, 30), and (40, 15), respectively, to allow the collection of different RSSI values for ANN training, testing, and validation as shown in Figure 1. The beacon nodes are fixed on a surface 1.5 m above the ground to avoid the effect of the boundary of the first Fresnel Zone, h_O (h_O = 0.5 λd ). Where, in the current work, λ is the wavelength of the transmitted signal of the adopted 2.4 GHz wireless protocol and d is the distance between the antennae of the beacons and the sensor node (carried by the subject). The ground-reflected signal is available in the first Fresnel zone when h_O is greater than or equal to the antenna height. The ground-reflected path has a significant effect on the power of the received signal [58]. For 2.45 GHz, h_O is 0.125 m at a distance of 0.5 m between beacons and the subject, 0.958 m at a distance of 30 m, and 1.24 m at a distance of 40 m. Consequently, the height of the beacons nodes must be greater than 1.24 m, and thus the signal from the beacons can arrive intact at the sensor node.

The FDS shown in Figure 2 is wrapped around the waist of the subject, as shown in the snapshot in Figure 3a. The FDS comprises a tilt sensor (mercury switch), accelerometer sensor (ADXL 355B), shock sensor, microcontroller ATmega328P as a barebones unit to reduce the power consumption of the FDS, Zigbee wireless protocol (XBee Series 2), and a power unit (i.e., 3.7 V/1000 mAh rechargeable battery), as illustrated in Figure 3b. The RN is fixed on the ceiling in the middle of the hall to ensure communication with the FDS and the CN. All the distances between nodes in the network lie within the range of the XBee Series 2, which can achieve a 100 m communication range between two nodes [59]. The RN passes all fall and location information of the subject to the CN. The CN monitors the activities (fall and location) of the elderly person and sends the data to the caregiver during emergency cases. The CN can be connected to the gateway to transmit the information of the elderly person to remote locations through the Internet of Things platform, as shown in Figure 1.

The FDS is shown in Figure 2 and Figure 3, and introduces a simple hardware implementation which includes minimal interface and connection between sensors to the microcontroller and the wireless protocol. The digital tilt sensor and the shock sensor each have three connection pins for a physical interface with the microcontroller: supply voltage, ground, and output. Two pins are utilized to power the sensors with +3.3 V and the third pin is the digital and analog serial output signal for a tilt sensor and shock sensor, respectively. However, the accelerometer sensor requires two extra pins compared to the tilt and shock sensors. The microcontroller of the sensor node of the FDS is connected with the XBee S2 module via two pins, one for the data bus and the other for activating the XBee S2. The design of the router node is very simple, needing only to pass data from the sensor node to the coordinator node through the pin for the receive data (Rx) to the pin of the transmit data (Tx) using one wire. The XBee S2 of the coordinator node is connected to the PC though an Arduino microcontroller using the USB cable. It is clear that the structure of the FDS is uncomplicated.

3.2. Indoor Environment

The simulation was performed in the test area, as shown in Figure 4a,b. The two figures show the WSN topology explained in the previous section. The elderly subject carried the FDS and moved in a rectangular path inside the test area. Two scenarios, namely, LOS and NLOS, were considered in this study. The LOS scenario is shown in Figure 4a. The test area was proposed to be empty to ensure direct barrier-free communication between nodes in the network. The same area and subject path of LOS was adopted in the NLOS scenario illustrated in Figure 4b for a fair comparison of localization error. However, many obstacles or barriers such as walls, windows, and doors were proposed in the NLOS environment, as shown in Figure 4b. The FDS collected the fall information and raw data of the RSSI from three beacon nodes and transmitted them to the CN via RN. Consequently, the CN supported by a monitoring system such as PC, tablet, and notepad can estimate the location of the fallen subject to be sent to the caregivers.

3.3. RSSI Generation

RSSI was generated based on the log-normal shadowing model (LNSM). LNSM was employed because it is a traditional wireless path loss model [60]. Other studies have considered this technique for channel modeling in outdoor and indoor surroundings to estimate the distance between the nodes in WSN [61]. Other studies considered this model for channel modeling in outdoor and indoor surroundings to estimate the distance between the nodes in WSN [61]. On the basis of the path loss model in Equation (1), the path loss of the received signals from beacon nodes N1, N2, and N3 toward the sensor node (i.e., FDS) was obtained as shown in Figure 5a–c, respectively, for LOS and NLOS. The figures show that the path loss increases with the distance between the beacon nodes and the sensor node. The path loss of the signal in NLOS environments is greater than that in LOS environments because the barriers present in NLOS are absent in LOS.

_Wl=_Wo+10αlo_g10(l/_lo)+γin dB

where the W_l is the path loss power at a distance of l meter, W_o is the path loss power at reference distance l_o (1 m is recommended in most studies such as References [21,62,63]), and γ is the Gaussian random variable in decibels with zero-mean and standard deviation. The basic notations used in the current paper are summarized in Table 1.

Similarly, the RSSI can be generated based on the LNSM (Equations (2) and (3))

RSSI= _WT−_Wl

RSSI= _WT−_Wo−10αlo_g10(l/_lo)−γin dBm

where W_T is the transmitted power of the beacon node, and 2 dBm is adopted for XBee Series 2.

A higher transmitted power was selected to ensure communication between the beacon nodes and the sensor node (i.e., FDS) in the adopted configuration of WSN. The RSSI raw data were collected through the moving path of the subject inside the test area. A total of 280 RSSI samples from 280 locations between each beacon node and the sensor node were recorded. A total of 840 RSSI samples were collected to train, test, and validate the ANN for subject localization without any pre-processing for the raw data. Figure 6a–c show the RSSI with respect to the distance between the sensor node and beacon nodes N1, N2, and N3, respectively. The NLOS environment clearly affected the path loss of the wireless link more than the LOS environment did. Figure 5 and Figure 6 show convergence between the LOS and NLOS plots at close distances. However, the divergence between these plots increased with the distance because several barriers or obstacles in the path of the signal caused signal degradation.

3.4. Localization Technique

Localization techniques based on ANN can develop a complex mathematical correlation between the input and output variables. The input variables in the elderly fall detection application are the RSSI data, and the output or target variables are the elderly fall incident location x and y. ANN was used to automatically learn nonlinear approximation algorithms. Therefore, ANN is suitable for the current application where the collected RSSIs are nonlinear data. In the current work, a backpropagation (BP) neural network-based Levenberg-Marquardt (LM) algorithm was adopted for training, testing, and validating the collected data of RSSI and the elderly fall incident location. The BP-based LM was selected because it provides the smallest localization error, as confirmed in References [26,63,64], efficiency, and high convergence speed. However, it needs a large operating memory [65]. In training, testing, and validation of ANN, 840 samples of RSSI raw data were used as follows: 70% (588 samples) for training, 15% (126 samples) for testing, and 15% (126 samples) for validation as recommended in References [26,66,67] to accurately determine the elderly fall and incident location for LOS and NLOS environments. Before training, testing, and validating ANN data, the numbers of inputs, outputs, hidden layers with its neurons, and learning rate must be determined. In this paper, three inputs (RSSI_N1, RSSI_N2, and RSSI_N3) and two outputs (x and y) were employed.

Several hidden layers and neurons were executed to allow the ANN to provide less time consumption and minimum fitness function (i.e., mean squared error (MSE)). Fitness function and time consumption were compromised to obtain the best number of hidden layers and neurons in ANN. The ANN was trained, tested, and validated to obtain the MSE performance for 5, 10, 15, and 20 neurons in each hidden layer for LOS and NLOS as shown in Figure 7 and Figure 8, respectively. Several neurons in each hidden layer are implemented to allow the ANN to choose the neurons that can perform the minimum MSE and elapsed time. Figure 7d and Figure 8d show that 20 neurons provide the best performance for the ANN because they achieve a MSE of approximately 9.447 × 10⁻⁶ (validation) and 3.447 × 10⁻³ (validation) after 100 epochs for LOS and NLOS, respectively. With regards to the other neurons, 5, 10, and 15 neurons demonstrate high MSE. On the other hand, one hidden layer was trained, tested, and validated for 5, 10, 15, and 20 neurons to investigate the performance of ANN. Figure 9a,b show the performance of MSE of validation data for the LOS and NLOS using one hidden layer, respectively. The figures exhibit lower MSE performance than two hidden layers, where they achieve a MSE of 0.00195 and 0.1177 at 100 epochs for LOS and NLOS, respectively. As a result, two hidden layers, each of which includes 20 neurons, are considered in this study because it achieves lower MSE. The learning rate (LR) (i.e., LR = 0.535) was selected to achieve the minimum fitness function for indoor environments as proven in Reference [26]. One-hundred iterations were chosen to reduce the time consumption of ANN. According to these parameters, the ANN architecture, shown in Figure 10, was adopted to determine the elderly fall location. The mean absolute error (MAE) was utilized as a fitness function of the ANN algorithm to evaluate the localization error in x and y locations as illustrated in Equations (4)–(6).

MA_Ex=1n∑i=1n |_xactual−_xestimated|

MA_Ey=1n∑i=1n |_yactual−_yestimated|

MAE=MA_Ex+MA_Ey2

where n is the number of tested samples (i.e., 280 samples for each input), x_actual and y_actual are the actual locations for the x and y coordinates, and x_estimated and y_estimated are the estimated locations for x and y coordinates obtained from ANN. The details of the ANN process based on the previous description and equations are presented in the flow diagram in Figure 11.

3.5. Fall Detection and Data-Driven Algorithm

S-BFDA and DDA are merged into one algorithm (Algorithm 1) and launched inside the microcontroller to reduce the power consumption and computational process of the sensor node microcontroller. Algorithm 1 shows the S-BFDA as dashed-line boxes and DDA as dash–dot boxes. The S-BFDA is based on the threshold of the (i) tilt sensor, (ii) angle of incidence, and (iii) shock to the ground when their peak values are above or below the threshold. The tilt sensor sends an interrupt digital signal (Logic 1) to activate the microcontroller once the elderly fall happens in one of the following situations: (i) backward fall, (ii) forward fall, (iii) right-side fall, and (v) left-side fall. The microcontroller in turn activates the accelerometer and shock sensors by supplying them with power and sends a digital control signal to awaken the Zigbee. On the basis of Cartesian coordinates xyz for the subject shown in Figure 12, the accelerometer sensor can be used to measure the angle of fall θ(t) of the subject by applying Equation (7) [68,69].

θ(t)=co^s−1(_ax(t)_ax ^(t)2+_ay ^(t)2+_az ^(t)2)

where a_x(t), a_y(t), and a_z(t) are the accelerations along the x, y, and z axes, respectively.

The shock sensor detects the elderly shock to the ground, and the tilt sensor detects the inclination of the subject during the fall. However, real-time fall detection may be compromised by false-alarm data from lying and falling positions. The S-BFDA is proposed to overcome this problem by fusing the tilt, angle, and shock measurements. Accordingly, the fall detection accuracy is satisfactory, and results indicate that the proposed FDS is energy efficient and can be used for accurate fall detection. In the proposed DDA, the components of the sensor node (i.e., sensors and Zigbee) are deactivated except for the microcontroller, which operates in low-power mode. The microcontroller continues to work in low-power mode to monitor the elderly fall on the basis of the tilt sensor. If the angle (which is derived from the accelerometer reading Equation (7)), shock, and tilt values exceed their threshold levels, an alert signal is sent to the CN via RN. In other words, upon a change in logic from ZERO to ONE for tilt and shock sensors and an angle of more than 45°, the microcontroller activates the Zigbee wireless protocol to transmit the fall event. The Zigbee simultaneously measures the RSSI values of the three beacon nodes. Once the measurements are completed, the FDS transmits an alert signal, including the fall of the elderly and the collected RSSI values, to the CN via the RN. When the transmission process is complete, the FDS returns to sleep mode to save energy. The strategy of switching between wake and sleep modes will significantly improve the power consumption of the sensor node. The sensor node transmits data infrequently and briefly and sleeps most of the time.

[Image omitted. See PDF.]

Figure 1.Topology of WSN of the subject fall and localization system.

[Image omitted. See PDF.]

Figure 2.FDS architecture.

[Image omitted. See PDF.]

Figure 3.Proposed FDS (a) fastened to the subject individual’s waist, (b) its components.

[Image omitted. See PDF.]

Figure 4.Adopted indoor environments (a) LOS and (b) NLOS.

[Image omitted. See PDF.]

Figure 5.Path loss between beacon nodes and sensor node for LOS and NLOS (a) N1-sensor node, (b) N2-sensor node, and (c) N3-sensor node.

[Image omitted. See PDF.]

Figure 6.RSSI between beacon nodes and sensor node for LOS and NLOS: (a) N1-sensor node, (b) N2-sensor node, and (c) N3-sensor node.

[Image omitted. See PDF.]

Figure 7.Performance of MSE of training, testing, and validation data for the LOS environments for (a) 5, (b) 10, (c) 15, and (d) 20 neurons.

[Image omitted. See PDF.]

Figure 8.Performance of MSE of training, testing, and validation data for the NLOS environments for (a) 5, (b) 10, (c) 15, and (d) 20 neurons.

[Image omitted. See PDF.]

Figure 9.Performance of MSE of validation data for the (a) LOS and (b) NLOS environments using one hidden layer.

[Image omitted. See PDF.]

Figure 10.Architecture of adopted ANN.

[Image omitted. See PDF.]

Figure 11.Flow diagram of ANN.

[Image omitted. See PDF.]

Figure 12.Geodetic coordinates of the elderly.

[Image omitted. See PDF.]

Figure 13.Different postures: (a) standing, (b) backward fall, (c) right-side fall, and (d) left-side fall.

[Image omitted. See PDF.]

Figure 14.Plot of tilt, shock, and angle measurements based on MakerPlot software.

[Image omitted. See PDF.]

Figure 15.Regression coefficient (R) of the ANN algorithm for (a) training, (b) testing, (c) validation, and (d) all data for the LOS environments.

[Image omitted. See PDF.]

Figure 16.Performance of MSE of training, testing, and validation data for the LOS environments.

[Image omitted. See PDF.]

Figure 17.Histogram errors for the ANN model of training, testing, and validation data for LOS environments.

[Image omitted. See PDF.]

Figure 18.Actual and estimated locations for indoor LOS environments.

[Image omitted. See PDF.]

Figure 19.Elderly fall location errors at LOS environments for the (a) x-axis and (b) y-axis.

[Image omitted. See PDF.]

Figure 20.Regression coefficient (R) of the ANN algorithm for (a) training, (b) testing, (c) validation, and (d) all data for the NLOS environments.

[Image omitted. See PDF.]

Figure 21.Performance of MSE of training, testing, and validation data for the NLOS environments.

[Image omitted. See PDF.]

Figure 22.Histogram errors for the ANN model of training, testing, and validation data for NLOS environments.

[Image omitted. See PDF.]

Figure 23.Actual and estimated locations for indoor NLOS environments.

[Image omitted. See PDF.]

Figure 24.Elderly fall location errors at NLOS environments for (a) x-axis and (b) y-axis.

[Image omitted. See PDF.]

Figure 25.Battery capacity against estimated battery life for the FDS.

[Image omitted. See PDF.]

Figure 26.Comparison between MAE of the proposed ANN technique and previous techniques.

[Image omitted. See PDF.]

Figure 27.Comparison between battery life of the proposed S-BFDA and previous system.

[Image omitted. See PDF.]

Table 1.Notations and definitions.

Symbols Definitions Symbols Definitions

Wl Path loss power IAvg_TL Average current consumption of the tilt sensor

Wo Path loss power at reference distance = 1 m IAvg_ACC Average current consumption of the accelerometer

WT Transmitted power of the beacon node IAvg_SH Average current consumption of the shock sensor

α Path loss exponents IAvg_MR Average current consumption of the microcontroller

γ Standard deviation IAvg_ZB Average current consumption of the ZigBee

l Distance under test DC Duty cycle

lo Reference distance (1 m in this study) Iactive_TL Active current consumption of the tilt sensor

RSSI Received signal strength indicator Isleep_TL Sleep current consumption of the tilt sensor

xactual Actual location for the x coordinate Iactive_ACC Active current consumption of the accelerometer

xestimated Estimated location for the x coordinate Isleep_ACC Sleep current consumption of the accelerometer

yactual Actual location for the y coordinate Iactive_SH Active current consumption of the shock sensor

yestimated Estimated location for the y coordinate Isleep_SH Sleep current consumption of the shock sensor

MAEx Mean absolute error for the x coordinate Iactive_MR Active current consumption of the microcontroller

MAEy Mean absolute error for the y coordinate Isleep_MR Sleep current consumption of the microcontroller

MAE Mean absolute error Iactive_ZB Active current consumption of the ZigBee

n Number of tested RSSI samples Isleep_ZB Sleep current consumption of the ZigBee

θ(t) Angle of fall Ttotal Total time of duty cycle

ax(t) Accelerations along the x-axis tx Transmission time of ZigBee

ay(t) Accelerations along the y-axis tsw Time transient from sleep to active modes

az(t) Accelerations along the z-axis l data packet length of ZigBee

Iavg Average current consumption of FDS S ZigBee data rate

[Image omitted. See PDF.]

Table 2.Current and time consumption of each component of the FDS.

Parameter Tilt Sensor Accelerometer Sensor Shock Sensor Microcontroller @ 4 MHz ZigBee (XBee S2)

Iactive (mA) 0.4 0.27 0.5 2.5 37.5

Isleep (mA) 0 0 0 0.09 0.56

tactive (min) 1 1 1 1 1

*tsleep (min) 1439 1439 1439 1439 1439

Ttotal (min) 1440 1440 1440 1440 1440

DC 6.4 × 10−4 6.4 × 10−4 6.4 × 10−4 6.4 × 10−4 6.4 × 10−4

Isubtotal (µA) 0.258 0.174 0.322 91.553 583.804

Average current consumption based DDA = 676 µA

Total current consumption in conventional operations = 41.17 mA

*tsleep = Ttotal − tactive, all components working at +3.3 Volts.

[Image omitted. See PDF.]

Table 3.Fall detection accuracy of the proposed sensor-based approach.

Environments Fall Detected (FD) Fall Not Detected (FND) False Alarm (%) (False Alarm = FDN/FD) Accuracy (%) (Accuracy = FD/TE)

LOS 40 0 0 100

NLOS 37 3 8.1 92.5

TE: Total event = 40.

[Image omitted. See PDF.]

Table 4.Comparison Between the accuracy of the adopted ANN technique and fall detection algorithms in previous studies.

Ref./Year Fall Detection Algorithm Wireless Protocol/Technology Sensor Type Sensor Location Test Conditions Acc. (%)

[47]/2012 ANN BT, IEEE 802.15.4 Tri-axial accelerometer Waist Indoor, outdoor 81.4

[75]/2013 Fuzzy Logic 3D motion analysis Camera Shoulder, spine, hip N/A 98.6

[76]/2014 k-NN with weighted RSSI ZigBee Tri-axial accelerometer Waist Indoor 88.62

[77]/2014 Hierarchical rule-based WiFi Accelerometer, GPS, magnetic, orientation Waist Indoor 95

[38]/2015 Threshold-based WiFi (IEEE 802.11 b/g) Tri-axial accelerometer Tri-axial gyroscope Chest Indoor 90

[78]/2016 SVM HD camera Camera Head Indoor, outdoor 90.27

[79]/2016 SVM BT Tri-axial accelerometer Waist Indoor 94.58

[80]/2016 Fuzzy dynamic Kinect camera Accelerometer

Image Camera

Parallel to the floor Indoor 92.86

ANFIS and SVM 95.71

Fuzzy beacon and dynamic 97.14

[35]/2016 k-NN and sliding window BT Tri-axial accelerometer Tri-axial gyroscope Upper trunk Indoor 97.7

[81]/2016 ANN, RBF, k-NN BT Tri-axial accelerometer Chest Indoor 99

[82]/2017 SVM, Threshold-based WiFi Accelerometer

Gyroscope Waist, hand, chest, arm Indoor 79.13

[83]/2017 k-NN, NB, SVM, BN, DT, LR, MP Smartphone Tri-axial accelerometer N/A N/A 81.4 (k-NN)

[84]/2017 SVM, NNC, DT, DA WiFi Tri-axial accelerometer, Tri-axial gyroscope, proximity, compass Left, right, front-pockets Indoor 90

[85]/2017 Compressed sensing IEEE 802.15.4 Motion N/A Indoor 90.9

[86]/2017 Data-level fusion UWB radar system Doppler signatures L-shape geometry Indoor 91.63

Decision level fusion 79.63

Sensor selection 86.99

[87]/2017 SVM BT LE Smartwatch Wrist N/A 93.8

[88]/2017 SVM Microsoft kinect Microsoft kinect Skeletal joints Indoor 93.6

[89]/2017 Camera-based Microsoft lifeCam Camera Waist Indoor, outdoor 93.78

[90]/2017 Fall Risk Index (FRI) Bluetooth LE Accelerometer, Gyroscope, Pressure Insoles Indoor 93.9

[91]/2017 Depth image Microsoft kinect Infrared (IR) Skeletal joints Indoor 93.94

[92]/2017 SVM WiFi Without sensors (signal path) Beacon AP Indoor (LOS) 90

RF 95

[93]/2017 SVM Bluetooth Accelerometer Lumbar Indoor, outdoor 94.58

[94]/2017 k-NN Membership histogram descriptor Camera (video frames) Constant camera Indoor 94.73

[95]/2017 Vision-based Video samples Videos of different postures Constant camera Indoor 94.9

[96]/2017 Ameva Smartphone Electromyogram, accelerometer Hand, wrist Indoor, outdoor 95

[97]/2017 Threshold-based Arduino

UNO and Matlab Tri-axial accelerometer

Tri-axial gyroscope Wrist Indoor 95

[68]/2017 BN and SW BT class 2 Tri-axial accelerometer

Tri-axial gyroscope Vest Indoor, outdoor 95.67

[98]/2017 Dataset Data store in SD card Accelerometer and gyroscope Waist Indoor, outdoor 96

[99]/2017 SVM RFID RFID Tag Neck Indoor 96

[100]/2017 SVM, LR, k-NN, NB, DT N/A Tri-axial accelerometer Waist Indoor 96 (SVM)

[23]/2017 SVM, ANN Smartphone Tri-axial accelerometer, Tri-axial gyroscope Worn on the body Indoor 91.71 (SVM)

96.07 (ANN)

[101]/2017 Hidden Markova model WiFi (802.11 g), ZigBee 3D image sensors Ceiling-cameras Indoor 98

[102]/2017 VI Fusion-based, SVM Smartphone Tri-axial accelerometer, visual camera Users’ pocket N/A 98.9

[103]/2017 Threshold-based Knowledge-based N/A Tri-axial accelerometer Waist, knee, elbow, head Indoor 99.33

[73]/2017 SVM N/A Tri-axial accelerometer Tri-axial gyroscope Waist, sternum Indoor 99.99

[74]/2017 SVM WiFi Without sensors (signal path) Beacon AP Indoor 100

Proposed S-BFDA in LOS ANN ZigBee (XBee S2) Accelerometer, tilt heart rate Waist Indoor 100

Proposed S-BFDA in NLOS ANN ZigBee (XBee S2) Accelerometer, tilt heart rate Waist Indoor 92.5

Acc: Accuracy; AP: Access point; BT: Bluetooth; BN: Bayesian network; DA: Discriminant analysis; DT: Decision tree; k-NN: k-nearest neighbors; LE: Low-energy; LR: Logistic regression; MP: Multilayer perception; NB: naïve Bayes; NNC: Nearest neighbor classifiers; RBF: Radial basis function; RF: Random forest; RFID: Radio frequency identification; RSS: Received signal strength; SW: Sliding window; SVM: Support vector machine; Ultrawide band (UWB); VI: Visual and Inertial.

Author Contributions

Conceptualization, S.K.G., A.A.-N., and A.M.J.; Methodology, all authors; Software, A.A.-N. and M.J.A.-A.; Validation, S.K.G. and A.A.-N.; Formal Analysis, S.K.G., S.L.M., and M.J.A.-A.; Investigation, S.K.G. and H.M.J.; Resources, A.A.-N. and H.M.J.; Data curation, S.L.M. and A.M.J.; Writing-Original Draft Preparation, S.K.G. and A.M.J.; Writing-Review & Editing, A.A.-N., S.L.M., M.J.A.-A., and J.C.; Visualization, S.L.M. and H.M.J.; Supervision, S.K.G. and M.J.A.-A.

Funding

This research received no external funding.

Acknowledgments

The author would like to thank the staff of the Department of Medical Instrumentation Techniques Engineering, Electrical Engineering Technical College, Middle Technical University (MTU), for their support during this study. Authors would like to thanks Al-Rafidain University College for their generosity in financial assistance.

Conflicts of Interest

The authors declare no conflict of interest.

© 2018 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).

References

1. Amin, M.G.; Zhang, Y.D.; Ahmad, F.; Ho, K.D. Radar signal processing for elderly fall detection: The future for in-home monitoring. IEEE Signal Process. Mag. 2016, 33, 71-80.

2. Shinmoto Torres, R.L.; Visvanathan, R.; Hoskins, S.; van den Hengel, A.; Ranasinghe, D.C. Effectiveness of a batteryless and wireless wearable sensor system for identifying bed and chair exits in healthy older people. Sensors 2016, 16, 546.

3. Chen, M.-C.; Chiu, Y.-W.; Chen, C.-H.; Chen, E.-J. Implementation of fall detection and localized caring system. Math. Probl. Eng. 2013, 2013, 217286.

4. Naranjo, P.G.V.; Shojafar, M.; Mostafaei, H.; Pooranian, Z.; Baccarelli, E. P-sep: A prolong stable election routing algorithm for energy-limited heterogeneous fog-supported wireless sensor networks. J. Supercomput. 2017, 73, 733-755.

5. Saleh, N.; Kassem, A.; Haidar, A.M. Energy-efficient architecture for wireless sensor networks in healthcare applications. IEEE Access 2018, 6, 6478-6486.

6. Naranjo, P.G.V.; Pooranian, Z.; Shojafar, M.; Conti, M.; Buyya, R. Focan: A fog-supported smart city network architecture for management of applications in the internet of everything environments. J. Parallel Distrib. Comput. 2018.

7. Shojafar, M.; Pooranian, Z.; Naranjo, P.G.V.; Baccarelli, E. Flaps: Bandwidth and delay-efficient distributed data searching in fog-supported p2p content delivery networks. J. Supercomput. 2017, 73, 5239-5260.

8. Yang, L.; Ren, Y.; Zhang, W. 3D depth image analysis for indoor fall detection of elderly people. Digit. Commun. Netw. 2016, 2, 24-34.

9. Yang, L.; Ren, Y.; Hu, H.; Tian, B. New fast fall detection method based on spatio-temporal context tracking of head by using depth images. Sensors 2015, 15, 23004.

10. Diraco, G.; Leone, A.; Siciliano, P. An active vision system for fall detection and posture recognition in elderly healthcare. In Proceedings of the Conference on Design, Automation and Test in Europe, Dresden, Germany, 8-12 March 2010; European Design and Automation Association: Leuven, Belgium, 2010; pp. 1536-1541.

11. Delahoz, Y.; Labrador, M. Survey on fall detection and fall prevention using wearable and external sensors. Sensors 2014, 14, 19806-19842.

12. Mubashir, M.; Shao, L.; Seed, L. A survey on fall detection: Principles and approaches. Neurocomputing 2013, 100, 144-152.

13. Sorvala, A.; Alasaarela, E.; Sorvoja, H.; Myllylä, R. A two-threshold fall detection algorithm for reducing false alarms. In Proceedings of the IEEE 6th International Symposium on Medical Information and Communication Technology (ISMICT), La Jolla, CA, USA, 25-29 March 2012; pp. 1-4.

14. Xu, Y.; Zhou, J.; Zhang, P. Rss-based source localization when path-loss model parameters are unknown. IEEE Commun. Lett. 2014, 18, 1055-1058.

15. Halder, S.J.; Giri, P.; Kim, W. Advanced smoothing approach of RSSI and LQI for indoor localization system. Int. J. Distrib. Sens. Netw. 2015, 11.

16. Mi, Q.; Stankovic, J.A.; Stoleru, R. Practical and secure localization and key distribution for wireless sensor networks. Ad Hoc Netw. 2012, 10, 946-961.

17. Liu, Y.; Hu, Y.H.; Pan, Q. Distributed, robust acoustic source localization in a wireless sensor network. IEEE Trans. Signal Process. 2012, 60, 4350-4359.

18. López, J.D.; Ocampo, C.; Sucerquia, A.; Vargas-Bonilla, J.F. Analyzing multiple accelerometer configurations to detect falls and motion. In Proceedings of the VII Latin American Congress on Biomedical Engineering CLAIB 2016, Bucaramanga, Santander, Colombia, 26-28 October 2016; Torres, I., Bustamante, J., Sierra, D.A., Eds.; Springer: Singapore, 2017; pp. 169-172.

19. Pal, A. Localization algorithms in wireless sensor networks: Current approaches and future challenges. Netw. Protoc. Algorithms 2010, 2, 45-73.

20. El Assaf, A.; Zaidi, S.; Affes, S.; Kandil, N. Robust anns-based wsn localization in the presence of anisotropic signal attenuation. IEEE Wirel. Commun. Lett. 2016, 5, 504-507.

21. Gharghan, S.K.; Nordin, R.; Ismail, M. Energy efficiency of ultra-low-power bicycle wireless sensor networks based on a combination of power reduction techniques. J. Sens. 2016, 2016, 7314207.

22. Wang, C.; Lu, W.; Narayanan, M.R.; Redmond, S.J.; Lovell, N.H. Low-power technologies for wearable telecare and telehealth systems: A review. Biomed. Eng. Lett. 2015, 5, 1-9.

23. Panhwar, M.; Shah, S.M.S.; Shah, S.M.Z.S.; Shah, S.M.A.; Chowdhry, B.S. Smart phone based fall detection using auto regression modeling in a non-restrictive setting. Indian J. Sci. Technol. 2017, 10.

24. Benocci, M.; Tacconi, C.; Farella, E.; Benini, L.; Chiari, L.; Vanzago, L. Accelerometer-based fall detection using optimized zigbee data streaming. Microelectron. J. 2010, 41, 703-710.

25. Wang, L.-H.; Hsiao, Y.-M.; Xie, X.-Q.; Lee, S.-Y. An outdoor intelligent healthcare monitoring device for the elderly. IEEE Trans. Consum. Electron. 2016, 62, 128-135.

26. Gharghan, S.K.; Nordin, R.; Ismail, M. A wireless sensor network with soft computing localization techniques for track cycling applications. Sensors 2016, 16, 1043.

27. Cheng, J.; Chen, X.; Shen, M. A framework for daily activity monitoring and fall detection based on surface electromyography and accelerometer signals. IEEE J. Biomed. Health Inform. 2013, 17, 38-45.

28. Chen, S.-K.; Kao, T.; Chan, C.-T.; Huang, C.-N.; Chiang, C.-Y.; Lai, C.-Y.; Tung, T.-H.; Wang, P.-C. A reliable transmission protocol for zigbee-based wireless patient monitoring. IEEE Trans. Inf. Technol. Biomed. 2012, 16, 6-16.

29. Özdemir, A.T.; Barshan, B. Detecting falls with wearable sensors using machine learning techniques. Sensors 2014, 14, 10691-10708. [Green Version]

30. Felisberto, F.; Laza, R.; Fdez-Riverola, F.; Pereira, A. A distributed multiagent system architecture for body area networks applied to healthcare monitoring. BioMed Res. Int. 2015, 2015, 192454.

31. Rescio, G.; Leone, A.; Siciliano, P. Supervised expert system for wearable mems accelerometer-based fall detector. J. Sens. 2013, 2013, 254629.

32. Lim, D.; Park, C.; Kim, N.H.; Kim, S.-H.; Yu, Y.S. Fall-detection algorithm using 3-axis acceleration: Combination with simple threshold and hidden markov model. J. Appl. Math. 2014, 2014, 896030.

33. Özdemir, A.T. An analysis on sensor locations of the human body for wearable fall detection devices: Principles and practice. Sensors 2016, 16, 1161.

34. Charlon, Y.; Fourty, N.; Bourennane, W.; Campo, E. Design and evaluation of a device worn for fall detection and localization: Application for the continuous monitoring of risks incurred by dependents in an alzheimer's care unit. Expert Syst. Appl. 2013, 40, 7316-7330.

35. He, J.; Hu, C.; Wang, X. A smart device enabled system for autonomous fall detection and alert. Int. J. Distrib. Sens. Netw. 2016, 2016.

36. Panicker, N.V.; Kumar, A.S. Tablet pc enabled body sensor system for rural telehealth applications. Int. J. Telemed. Appl. 2016, 2016.

37. Oporto, E.; Vilcahuaman, L. Fall detection system for elderly by MEMS accelerometer and SMS alert. In Proceedings of the VII Latin American Congress on Biomedical Engineering CLAIB 2016, Bucaramanga, Santander, Colombia, 26-28 October 2016; Springer: Singapore, 2017; pp. 114-117.

38. Huynh, Q.T.; Nguyen, U.D.; Irazabal, L.B.; Ghassemian, N.; Tran, B.Q. Optimization of an accelerometer and gyroscope-based fall detection algorithm. J. Sens. 2015, 2015, 452078.

39. Majumder, A. Development of a Wireless Mobile Computing Platform for Fall Risk Prediction. Ph.D. Thesis, Marquette University, Milwaukee, WI, USA, 2016.

40. Luque, R.; Casilari, E.; Morón, M.-J.; Redondo, G. Comparison and characterization of android-based fall detection systems. Sensors 2014, 14, 18543-18574.

41. Zhao, Z.; Chen, Y.; Liu, J. Fall detecting and alarming based on mobile phone. In Proceedings of the 7th International Conference on Ubiquitous Intelligence & Computing and 7th International Conference on Autonomic & Trusted Computing (UIC/ATC), Xi'an, China, 26-29 October 2010; pp. 494-497.

42. Zhao, Z.; Chen, Y.; Wang, S.; Chen, Z. Fallalarm: Smart phone based fall detecting and positioning system. Procedia Comput. Sci. 2012, 10, 617-624.

43. Majumder, A.J.A.; Zerin, I.; Ahamed, S.I.; Smith, R.O. A multi-sensor approach for fall risk prediction and prevention in elderly. ACM SIGAPP Appl. Comput. Rev. 2014, 14, 41-52.

44. Dai, J.; Bai, X.; Yang, Z.; Shen, Z.; Xuan, D. Mobile phone-based pervasive fall detection. Pers. Ubiquitous Comput. 2010, 14, 633-643.

45. Habib, M.A.; Mohktar, M.S.; Kamaruzzaman, S.B.; Lim, K.S.; Pin, T.M.; Ibrahim, F. Smartphone-based solutions for fall detection and prevention: Challenges and open issues. Sensors 2014, 14, 7181-7208.

46. Mellone, S.; Tacconi, C.; Schwickert, L.; Klenk, J.; Becker, C.; Chiari, L. Smartphone-based solutions for fall detection and prevention: The farseeing approach. Z. Gerontol. Geriatr. 2012, 45, 722-727.

47. Abbate, S.; Avvenuti, M.; Bonatesta, F.; Cola, G.; Corsini, P.; Vecchio, A. A smartphone-based fall detection system. Pervasive Mob. Comput. 2012, 8, 883-899.

48. Medrano, C.; Plaza, I.; Igual, R.; Sánchez, Á.; Castro, M. The effect of personalization on smartphone-based fall detectors. Sensors 2016, 16, 117.

49. Huang, C.-N.; Chan, C.-T. Zigbee-based indoor location system by k-nearest neighbor algorithm with weighted rssi. Procedia Comput. Sci. 2011, 5, 58-65.

50. Wu, F.; Zhao, H.; Zhao, Y.; Zhong, H. Development of a wearable-sensor-based fall detection system. Int. J. Telemed. Appl. 2015, 2015.

51. Bian, Z.-P.; Hou, J.; Chau, L.-P.; Magnenat-Thalmann, N. Fall detection based on body part tracking using a depth camera. IEEE J. Biomed. Health Inform. 2015, 19, 430-439.

52. Yu, M.; Yu, Y.; Rhuma, A.; Naqvi, S.M.R.; Wang, L.; Chambers, J.A. An online one class support vector machine-based person-specific fall detection system for monitoring an elderly individual in a room environment. IEEE J. Biomed. Health Inform. 2013, 17, 1002-1014.

53. Ozcan, K.; Mahabalagiri, A.K.; Casares, M.; Velipasalar, S. Automatic fall detection and activity classification by a wearable embedded smart camera. IEEE J. Emerg. Sel. Top. Circuits Syst. 2013, 3, 125-136.

54. Su, B.Y.; Ho, K.; Rantz, M.J.; Skubic, M. Doppler radar fall activity detection using the wavelet transform. IEEE Trans. Biomed. Eng. 2015, 62, 865-875.

55. Sachs, J.; Herrmann, R. M-sequence-based ultra-wideband sensor network for vitality monitoring of elders at home. IET Radar Sonar Navig. 2015, 9, 125-137. [Green Version]

56. Garripoli, C.; Mercuri, M.; Karsmakers, P.; Soh, P.J.; Crupi, G.; Vandenbosch, G.A.; Pace, C.; Leroux, P.; Schreurs, D. Embedded dsp-based telehealth radar system for remote in-door fall detection. IEEE J. Biomed. Health Inform. 2015, 19, 92-101.

57. Fernández-Caballero, A.; Sokolova, M.V.; Serrano-Cuerda, J. Lateral inhibition in accumulative computation and fuzzy sets for human fall pattern recognition in colour and infrared imagery. Sci. World J. 2013, 2013, 935026.

58. Ndzi, D.L.; Harun, A.; Ramli, F.M.; Kamarudin, M.L.; Zakaria, A.; Shakaff, A.Y.M.; Jaafar, M.N.; Zhou, S.; Farook, R.S. Wireless sensor network coverage measurement and planning in mixed crop farming. Comput. Electron. Agric. 2014, 105, 83-94. [Green Version]

59. Jawad, H.M.; Nordin, R.; Gharghan, S.K.; Jawad, A.M.; Ismail, M. Energy-efficient wireless sensor networks for precision agriculture: A review. Sensors 2017, 17, 1781.

60. Xu, J.; Liu, W.; Lang, F.; Zhang, Y.; Wang, C. Distance measurement model based on RSSI in WSN. Wirel. Sens. Netw. 2010, 2, 606.

61. Sahu, P.K.; Wu, E.H.-K.; Sahoo, J. Durt: Dual RSSI trend based localization for wireless sensor networks. IEEE Sens. J. 2013, 13, 3115-3123.

62. Cotuk, H.; Bicakci, K.; Tavli, B.; Uzun, E. The impact of transmission power control strategies on lifetime of wireless sensor networks. IEEE Trans. Comput. 2014, 63, 2866-2879.

63. Gharghan, S.K.; Nordin, R.; Ismail, M.; Ali, J.A. Accurate wireless sensor localization technique based on hybrid PSO-ANN algorithm for indoor and outdoor track cycling. IEEE Sens. J. 2016, 16, 529-541.

64. Payal, A.; Rai, C.S.; Reddy, B.R. Analysis of some feedforward artificial neural network training algorithms for developing localization framework in wireless sensor networks. Wirel. Pers. Commun. 2015, 82, 2519-2536.

65. Kukolj, D.; Levi, E. Identification of complex systems based on neural and takagi-sugeno fuzzy model. IEEE Trans. Syst. Man Cybern. Part B Cybern. 2004, 34, 272-282.

66. Kumar, S.; Jeon, S.M.; Lee, S.R. Localization estimation using artificial intelligence technique in wireless sensor networks. J. Korea Inf. Commun. Soc. 2014, 39, 820-827.

67. Aboelela, E.H.; Khan, A.H. Wireless sensors and neural networks for intruders detection and classification. In Proceedings of the International Conference on Information Networking (ICOIN), Bali, Indonesia, 1-3 February 2012; pp. 138-143.

68. He, J.; Bai, S.; Wang, X. An unobtrusive fall detection and alerting system based on kalman filter and bayes network classifier. Sensors 2017, 17, 1393.

69. Wang, C.; Lu, W.; Narayanan, M.R.; Chang, D.C.W.; Lord, S.R.; Redmond, S.J.; Lovell, N.H. Low-power fall detector using triaxial accelerometry and barometric pressure sensing. IEEE Trans. Ind. Inform. 2016, 12, 2302-2311.

70. Gharghan, S.K.; Nordin, R.; Ismail, M. An ultra-low power wireless sensor network for bicycle torque performance measurements. Sensors 2015, 15, 11741.

71. Gharghan, S.K. Energy-efficient remote temperature monitoring system for patients based on GSM modem and microcontroller. J. Commun. 2017, 12, 433-442.

72. Data Acquisition and Graphical Plotting Software for Microcontrolles (Makerplot). Available online: http://www.makerplot.com (accessed on 3 March 2014).

73. Aziz, O.; Klenk, J.; Schwickert, L.; Chiari, L.; Becker, C.; Park, E.J.; Mori, G.; Robinovitch, S.N. Validation of accuracy of SVM-based fall detection system using real-world fall and non-fall datasets. PLoS ONE 2017, 12, e0180318.

74. Wang, H.; Zhang, D.; Wang, Y.; Ma, J.; Wang, Y.; Li, S. Rt-fall: A real-time and contactless fall detection system with commodity wifi devices. IEEE Trans. Mob. Comput. 2017, 16, 511-526.

75. Planinc, R.; Kampel, M. Robust fall detection by combining 3D data and fuzzy logic. In Computer Vision-ACCV 2012 Workshops: ACCV 2012 International Workshops, Daejeon, Korea, 5-6 November 2012, Revised Selected Papers, Part II; Park, J.-I., Kim, J., Eds.; Springer: Berlin/Heidelberg, Germany, 2013; pp. 121-132.

76. Huang, C.-N.; Chan, C.-T. A zigbee-based location-aware fall detection system for improving elderly telecare. Int. J. Environ. Res. Public Health 2014, 11, 4233-4248.

77. Zhang, S.; McCullagh, P.; Zhang, J.; Yu, T. A smartphone based real-time daily activity monitoring system. Clust. Comput. 2014, 17, 711-721.

78. Zhao, K.; Jia, K.; Liu, P. Fall detection algorithm based on human posture recognition. In Advances in Intelligent Information Hiding and Multimedia Signal Processing: Proceeding of the Twelfth International Conference on Intelligent Information Hiding and Multimedia Signal Processing, Nov., 21-23, 2016, Kaohsiung, Taiwan, Volume 2; Pan, J.-S., Tsai, P.-W., Huang, H.-C., Eds.; Springer International Publishing: Cham, Switzerland, 2017; pp. 119-126.

79. Chen, K.-H.; Yang, J.-J.; Jaw, F.-S. Accelerometer-based fall detection using feature extraction and support vector machine algorithms. Instrum. Sci. Technol. 2016, 44, 333-342.

80. Kwolek, B.; Kepski, M. Fuzzy inference-based fall detection using kinect and body-worn accelerometer. Appl. Soft Comput. 2016, 40, 305-318.

81. Gibson, R.M.; Amira, A.; Ramzan, N.; Casaseca-de-la-Higuera, P.; Pervez, Z. Multiple comparator classifier framework for accelerometer-based fall detection and diagnostic. Appl. Soft Comput. 2016, 39, 94-103.

82. Fang, Y.-C.; Dzeng, R.-J. Accelerometer-based fall-portent detection algorithm for construction tiling operation. Autom. Constr. 2017, 84, 214-230.

83. Wang, C.; Lee, S.; Ho, J.; Na, Y.; Min, S.D. Detection of optimal activity recognition algorithm for elderly using smartphone. In Advances in Computer Science and Ubiquitous Computing: CSA-CUTE2016; Park, J.J., Pan, Y., Yi, G., Loia, V., Eds.; Springer: Singapore, 2017; pp. 1013-1018.

84. Hakim, A.; Huq, M.S.; Shanta, S.; Ibrahim, B.S.K.K. Smartphone based data mining for fall detection: Analysis and design. Procedia Comput. Sci. 2017, 105, 46-51.

85. Veeraputhiran, A.; Sankararajan, R. Feature based fall detection system for elders using compressed sensing in WVSN. Wirel. Netw. 2017.

86. Erol, B.; Amin, M.G.; Boashash, B. Range-doppler radar sensor fusion for fall detection. In Proceedings of the IEEE Radar Conference (RadarConf), Seattle, WA, USA, 8-12 May 2017; pp. 819-824.

87. Ngu, A.; Wu, Y.; Zare, H.; Polican, A.; Yarbrough, B.; Yao, L. Fall detection using smartwatch sensor data with accessor architecture. In Smart Health: International Conference, ICSH 2017, Hong Kong, China, June 26-27, 2017, Proceedings; Chen, H., Zeng, D.D., Karahanna, E., Bardhan, I., Eds.; Springer International Publishing: Cham, Switzerland, 2017; pp. 81-93.

88. Alazrai, R.; Momani, M.; Daoud, M. Fall detection for elderly from partially observed depth-map video sequences based on view-invariant human activity representation. Appl. Sci. 2017, 7, 316.

89. Ozcan, K.; Velipasalar, S.; Varshney, P.K. Autonomous fall detection with wearable cameras by using relative entropy distance measure. IEEE Trans. Hum.-Mach. Syst. 2017, 47, 31-39.

90. Di Rosa, M.; Hausdorff, J.M.; Stara, V.; Rossi, L.; Glynn, L.; Casey, M.; Burkard, S.; Cherubini, A. Concurrent validation of an index to estimate fall risk in community dwelling seniors through a wireless sensor insole system: A pilot study. Gait Posture 2017, 55, 6-11.

91. Nizam, Y.; Mohd, M.N.H.; Jamil, M.M.A. Human fall detection from depth images using position and velocity of subject. Procedia Comput. Sci. 2017, 105, 131-137.

92. Wang, Y.; Wu, K.; Ni, L.M. Wifall: Device-free fall detection by wireless networks. IEEE Trans. Mob. Comput. 2017, 16, 581-594.

93. Chen, K.-H.; Hsu, Y.-W.; Yang, J.-J.; Jaw, F.-S. Enhanced characterization of an accelerometer-based fall detection algorithm using a repository. Instrum. Sci. Technol. 2017, 45, 382-391.

94. Ismail, M.M.B.; Bchir, O. Automatic fall detection using membership based histogram descriptors. J. Comput. Sci. Technol. 2017, 32, 356-367.

95. Liu, H.; Guo, Y. A vision-based fall detection algorithm of human in indoor environment. In Proceedings of the Second International Conference on Photonics and Optical Engineering, Xi'an, China, 14-17 October 2016; SPIE: Bellingham, WA, USA, 2017; p. 6.

96. Álvarez de la Concepción, M.Á.; Soria Morillo, L.M.; Álvarez García, J.A.; González-Abril, L. Mobile activity recognition and fall detection system for elderly people using ameva algorithm. Pervasive Mob. Comput. 2017, 34, 3-13.

97. de Quadros, T.; Lazzaretti, A.E.; Schneider, F.K. Development and evaluation of a method for fall detection based on a wrist-located device. In Proceedings of the VII Latin American Congress on Biomedical Engineering CLAIB 2016, Bucaramanga, Santander, Colombia, 26-28 October 2016; Torres, I., Bustamante, J., Sierra, D.A., Eds.; Springer: Singapore, 2017; pp. 256-259.

98. Sucerquia, A.; López, J.; Vargas-Bonilla, J. Sisfall: A fall and movement dataset. Sensors 2017, 17, 198.

99. Zhu, L.; Wang, R.; Wang, Z.; Yang, H. Tagcare: Using rfids to monitor the status of the elderly living alone. IEEE Access 2017, 5, 11364-11373.

100. Aziz, O.; Musngi, M.; Park, E.J.; Mori, G.; Robinovitch, S.N. A comparison of accuracy of fall detection algorithms (threshold-based vs. Machine learning) using waist-mounted tri-axial accelerometer signals from a comprehensive set of falls and non-fall trials. Med. Biol. Eng. Comput. 2017, 55, 45-55.

101. Kianoush, S.; Savazzi, S.; Vicentini, F.; Rampa, V.; Giussani, M. Device-free rf human body fall detection and localization in industrial workplaces. IEEE Internet Things J. 2017, 4, 351-362.

102. Li, Y.; Zhai, Q.; Ding, S.; Yang, F.; Li, G.; Zheng, Y.F. Efficient health-related abnormal behavior detection with visual and inertial sensor integration. Pattern Anal. Appl. 2017.

103. Hsieh, C.-Y.; Liu, K.-C.; Huang, C.-N.; Chu, W.-C.; Chan, C.-T. Novel hierarchical fall detection algorithm using a multiphase fall model. Sensors 2017, 17, 307.

104. Kulkarni, R.V.; Venayagamoorthy, G.K. Bio-inspired algorithms for autonomous deployment and localization of sensor nodes. IEEE Trans. Syst. Man Cybern. Part C (Appl. Rev.) 2010, 40, 663-675.

105. Li, S.; Qin, F. A dynamic neural network approach for solving nonlinear inequalities defined on a graph and its application to distributed, routing-free, range-free localization of wsns. Neurocomputing 2013, 117, 72-80.

106. Zhao, L.-Z.; Wen, X.-B.; Li, D. Amorphous localization algorithm based on BP artificial neural network. Int. J. Distrib. Sens. Netw. 2015, 11, 9.

107. Kumar, S.; Sharma, R.; Vans, E. Localization for wireless sensor networks: A neural network approach. Int. J. Comput. Networks Commun. 2016, 8, 61-71.

108. Payal, A.; Rai, C.; Reddy, B. Comparative analysis of bayesian regularization and levenberg-marquardt training algorithm for localization in wireless sensor network. In Proceedings of the 15th International Conference on Advanced Communication Technology (ICACT), PyeongChang, Korea, 27-30 January 2013; pp. 191-194.

109. Payal, A.; Rai, C.; Reddy, B. Artificial neural networks for developing localization framework in wireless sensor networks. In Proceedings of the International Conference on Data Mining and Intelligent Computing (ICDMIC), Delhi, India, 5-6 September 2014; pp. 1-6.

110. Jun, W.; Fu, Z.; Tiansi, R.; Xun, C.; Gang, L. A novel hybrid localization method for wireless sensor network. Int. J. Smart Sens. Intell. Syst. 2016, 9, 1323-1340.

111. Wu, X.; Deng, F.; Chen, Z. Rfid 3D-landmarc localization algorithm based on quantum particle swarm optimization. Electronics 2018, 7, 19.

112. Nekooei, S.M.; Manzuri-Shalmani, M. Location finding in wireless sensor network based on soft computing methods. In Proceedings of the International Conference on Control, Automation and Systems Engineering (CASE), Singapore, 30-31 July 2011; pp. 1-5.

113. So-In, C.; Permpol, S.; Rujirakul, K. Soft computing-based localizations in wireless sensor networks. Pervasive Mob. Comput. 2016, 29, 17-37.

114. Wang, Z.; Yang, Z.; Dong, T. A review of wearable technologies for elderly care that can accurately track indoor position, recognize physical activities and monitor vital signs in real time. Sensors 2017, 17, 341.

115. Liang, Y.; Zhou, X.; Yu, Z.; Guo, B. Energy-efficient motion related activity recognition on mobile devices for pervasive healthcare. Mob. Netw. Appl. 2014, 19, 303-317.

116. Miramontes, R.; Aquino, R.; Flores, A.; Rodríguez, G.; Anguiano, R.; Ríos, A.; Edwards, A. Plaimos: A remote mobile healthcare platform to monitor cardiovascular and respiratory variables. Sensors 2017, 17, 176.

117. Kantoch, E.; Grochala, D.; Kajor, M. Bio-inspired topology of wearable sensor fusion for telemedical application. In Artificial Intelligence and Soft Computing: 16th International Conference, ICAISC 2017, Zakopane, Poland, June 11-15, 2017, Proceedings, Part I; Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M., Eds.; Springer International Publishing: Cham, Switzerland, 2017; pp. 658-667.

118. Nguyen Gia, T.; Sarker, V.K.; Tcarenko, I.; Rahmani, A.M.; Westerlund, T.; Liljeberg, P.; Tenhunen, H. Energy efficient wearable sensor node for IoT-based fall detection systems. Microprocess. Microsyst. 2018, 56, 34-46.

119. Wang, Y.; Krishnamachari, B.; Annavaram, M. Semi-markov state estimation and policy optimization for energy efficient mobile sensing. In Proceedings of the Annual IEEE Communications Society Conference on Sensor, Mesh and Ad Hoc Communications and Networks (SECON), Seoul, Korea, 18-21 June 2012; pp. 533-541.

120. Ren, L.; Zhang, Q.; Shi, W. Low-power fall detection in home-based environments. In Proceedings of the 2nd ACM international workshop on Pervasive Wireless Healthcare, Hilton Head, SC, USA, 11 June 2012; pp. 39-44.

121. Pinto, S.; Cabral, J.; Gomes, T. We-care: An iot-based health care system for elderly people. In Proceedings of the IEEE International Conference on Industrial Technology (ICIT), Toronto, ON, Canada, 22-25 March 2017; pp. 1378-1383.