1. Introduction
According to the World Health Organization (WHO) [1], about 50 million people around the world have Alzheimer’s disease or another type of dementia. As reported by the Pan American Health Organization (OPAS) [2], Alzheimer’s is one of the top ten death-causing diseases in the world. Alzheimer’s is one of the most prevalent diseases of dementia, which is characterized as a syndrome—usually of a chronic or progressive nature—for which there is no cure or treatment, with deterioration of cognitive function (ability to process thoughts), which can be expected from normal aging. The impairment of cognitive function is usually followed and sometimes preceded by deterioration of emotional control, social behavior, or motivation. Although dementia mainly affects elderly people, the demonstration of symptoms can begin to occur even before the aging stage, which influences the fact that there are almost 10 million new cases each year around the world [3]. The total number of people with dementia is expected to reach 82 million in 2030 and 152 million in 2050, and great part of this increase is attributable to the growing number of people with dementia living in low and middle income countries [4].
Dementia has physical, psychological, social, and economic impacts, not only on people with the disease, but also on their caregivers, families, and society in general, as it is one of the main causes of disability and dependency among the elderly worldwide [1]. Caregivers who provide assistance to individuals with dementia often feel stressed, frustrated with the amount of time required to them, and emotionally challenged. In addition, cognitive function may progressively decrease over time in patients, in variations that fluctuate throughout the day or over the long term, as the (neurological) system is used [5]. These behaviors are difficult for caregivers to manage and are positively correlated with the caregiver’s suffering [6]. They also contribute to increasing the cost of care for patients and are the main reason for institutionalization [7,8]. In 2015, society’s total cost for dementia care was estimated at $818 billion, equivalent to 1.1% of the global gross domestic product (GDP). The total cost as a proportion of GDP with a variation of 0.2% in low and middle income countries to 1.4% in high income countries [1].
Alzheimer’s disease can only be detected currently after symptoms start to appear, but monitoring solutions are emerging with the use of electronic devices to care for these patients in the most diverse stages of the disease; therefore, technology presents possibilities for palliative care support for patients. According to Burleson et al. [5], recent research work on information and communication technologies (
The majority of caregivers who use technology (as help support) obtained benefits for their patients from at least one activity of daily living (ADL) in the past year (86%). On average, they (caregivers) provide assistance in three of the six activities listed. The ADLs that caregivers usually help with are: getting up and lying/sitting on beds and chairs (73%), dressing (61%), and eating (52%). About one in three (caregivers) helps in the bath (37%) or in the bathroom (34%), and 26% help to deal with incontinence [9]. The WHO has developed a global action plan in which it is proposed that by 2025, the global production of research on dementia should double the number of scientific publications, 50% of countries should routinely collect data on the main indicators of dementia, and 75% of countries should have support for dementia care providers [10]. The alarming prevalence of Alzheimer’s disease and the absence of any effective treatment made this disease an important issue; it has been highlighted as a priority by the nations of the G8 [11].
This work aims to propose a model for monitoring Alzheimer’s patients, called DCARE, which means dementia care, seeking to synthesize the needs and characteristics that make up a better approach for its validation. The main scientific contribution of this work is the specification of a computational model to be the first to use the analysis on context histories [12,13,14] and context predictions [15,16] focusing on Alzheimer’s disease. The prototype was developed from the model created. Subsequently, the tests were performed based on the data sets generated by the DCARE Dataset Simulator Tool, which was also developed in this work.
This article is organized into seven sections. In Section 2, we present the description of key themes for understanding the proposed model. In Section 3, we discuss related works. In Section 4, the DCARE model is explained in detail. In Section 5, we discuss the implementation aspects. In Section 6, we describe the evaluation and results. Section 7 encompasses the conclusions and proposals for future work.
2. Background
This section presents two key themes for understanding the proposed model. In Section 2.1, we describe the ambient intelligence (AmI) paradigm and its importance for the field of the study of this work. In Section 2.2, we introduce concepts of Alzheimer’s disease (AD).
2.1. Ambient Intelligence and IoT
The exponential growth of the development of technologies such as mobile devices, wearables, and IoT devices enables the expansion of services to assist users in their daily lives [17]. In addition to the quantity, there was an increase in intelligence and integration among these computational elements [18,19].
The IoT’s smartness has been possible on account of the massive adoption of artificial intelligence, mainly machine learning (ML), natural language processing (NLP), and computational vision (CV) techniques. This kind of environment allows a more natural and spontaneous human–computer interaction [20].
In terms of communication and integration of these complex and heterogeneous computational artifacts, cloud computing’s approach has also been evolving. For instance, edge and fog computing contribute as architectural alternatives approach to make the ambient IoT vision feasible [21].
IoT enables the integration of objects from the physical environment into the virtual. It also promotes machine-to-machine communication over the Internet, which makes the sharing of data and information to achieve specific goals possible. This is achieved by integrating devices, sensors, and systems [22], generating specialized intelligent services.
Smart environments [20] aim to seamlessly add computational power to the ambient to broaden and support day-to-day activities (ADLs). Ambient intelligence (AmI) is another term to name a space that is designed to understand and adapt to the people’s presence, preferences, and needs to free them from the manual and explicit control of surroundings [23].
Intelligence services can be created based on AmI methods. In this sense, the environment is capable to manage different data sources dynamically. The data fusion can consider systems, IoT objects, people, and other environmental entities to be processed and derive the proper information to meet the user’s needs [20].
The AmI model promotes smart services, which provide characteristics such as context awareness, adaptation, proactivity, and other features envisioned by Mark Weiser [24]. According to his vision about ubiquitous computing, technology becomes indistinguishable from everyday life by being present all the time and in various formats. It aims to make the human–ambient interaction invisible or most natural for people.
The evolution of ubiquitous computing provided applications in areas including health [25,26,27,28,29,30], agriculture [14,31], commerce [32], and education [33,34,35,36,37], among others. The application of ubiquitous computing [38] has led to the emergence of research areas such as u-learning [39], u-commerce [40], and u-health [41,42,43].
Although scientific advances have already been made toward u-health [44], a more specific model for ubiquitous healthcare in an ambient intelligent space for people with Alzheimer’s disease in their home environment, according to the situation awareness perspective [45,46] of their ADLs, is still unknown.
2.2. Alzheimer’s Disease
Alzheimer’s is one of the most prevalent diseases of dementia; it stands out as a syndrome—usually of a chronic or progressive nature—for which there is no cure or treatment, and with which there is a deterioration of cognitive function (that is, the ability to process thought) beyond what can be expected from normal aging. It affects memory, thinking, guidance, understanding, calculation, learning ability, language, and judgment, but consciousness is not affected. The impairment of cognitive function is usually accompanied and sometimes preceded by deterioration in emotional control, social behavior or motivation. Alzheimer’s disease is the most common form of dementia and can contribute to 60–70% of the total cases [47].
Individuals with Alzheimer’s and other forms of dementia usually experience a period of significant behavioral and psychological symptoms of dementia (BPSD) [48]. According to Cohen-Mansfield (2008), BPSDs are generally divided into several categories, physical and nonphysical agitation or aggression and verbal agitation. Nonphysical behaviors include undressing, expressing, hiding things, and leaving search behavior. Physical aggressive behaviors include biting, hitting, kicking, pushing, scratching, and unwanted sexual advances. There may also be verbal agitation, such as cursing, shouting, and repeated actions of attention.
BPSD behaviors are difficult to be managed by caregivers and are positively correlated with the caregiver’s suffering [49]. They also contribute to the increased cost of care for people with dementia and are the main reason for institutionalization [8,11,48]. Behavioral problems are a safety concern for family members and professional caregivers, as well as for other elderly people living in community settings. There are well-validated nonpharmacological methods used to manage BPSD. These methods include redirection, music therapy, individual socialization, art therapy, and animal-assisted therapy [50].
A crucial issue about BPSD is the caregiver’s recognition of triggers or events that generally precede unwanted behavior [51,52]. Even if the triggers cannot be identified, the simple recognition that a patient is agitated can be beneficial, as a caregiver can be called in as needed. It should be noted that caregivers are generally not present at the beginning of these triggers. Because Alzheimer’s disease can only be detected currently after symptoms start to appear, follow-up solutions appear with the use of devices to care for these patients in the most diverse stages of the disease; therefore, technology presents possibilities for palliative care support. According to Burleson et al. [5], recent research work on information and communication technologies (ICTs) for the treatment of dementia demonstrated how the successful incorporation of technology in everyday practices implies a set of judgments and attitudes of value about the best way to care and make decisions for someone else. Over the years, the use of smart devices and wearable sensors has increased in the health field. These sensors are used for a variety of applications, from security to monitoring health measures, such as quality and quantity of sleep [53]. The majority of caregivers who use technology (as support help) obtained benefits for their patients from at least one activity of daily living (ADL) in the past year (86%).
On average, the caregivers provide assistance in three of the six activities listed. The ADLs that caregivers usually help with are: getting up and lying/sitting on beds and chairs (73%), dressing (61%), and eating (52%). About one in three (caregivers) helps in the bath (37%) or in the bathroom (34%), and 26% help to deal with incontinence [9]. The WHO has developed a global action plan in which it is proposed that by 2025, the global production of research on dementia should double the number of scientific publications, 50% of countries should routinely collect data on the main indicators of dementia, and 75% of countries should have support for dementia care providers [10]. The alarming prevalence of Alzheimer’s disease and the absence of any effective treatment made this disease an relevant issue, highlighted as a priority by the G8 nations [11]. It can be noticed, only in Brazil, that there are more than 30 million people over the age of 60, according to the data from the Brazilian Institute of Geography and Statistics (IBGE in portuguese). In 2013, almost 2 million people have dementia; around 40% to 60% of them have Alzheimer’s disease [54].
Although dementia mainly affects elderly people, the demonstration of symptoms can starts even before the aging stage, which influences the fact that there are almost 10 million new cases each year around the world [3]. There is often a lack of awareness and understanding of dementia, resulting in stigmatization and barriers to diagnostics and care. The impact of dementia on caregivers, the family and society in general can be physical, psychological, social, and economic [1]. It also is one of the main causes of disability and dependency among the elderly worldwide.
Caregivers who provide assistance to individuals with dementia often feel stressed, frustrated with the amount of time needed and emotionally challenged. In addition, cognitive function may progressively decrease over time in patients, in variations that fluctuate throughout the day or over the long term, as the system is used (for example, weeks to months or years) [5]. These behaviors are difficult to be managed by caregivers and are positively correlated with the caregiver’s suffering [6]. They also contribute to the increased cost of care for people with dementia and are the main reason for institutionalization [7,8].
In 2015, the society’s total cost for dementia care was estimated at 818 billion dolars, equivalent to 1.1% of the global gross domestic product (GDP). The total cost as a proportion of GDP with a variation of 0.2% in low-and-middle-income countries to 1.4% in high-income countries [1]. According to WHO [1], about 50 million people around the world are carriers of Alzheimer’s disease or other dementia. According to PAHO [2], Alzheimer’s is on the list among one of the top ten death-causing diseases in the world. The total number of people with dementia is expected to reach 82 million in 2030 and 152 million in 2050, and much of this increase is attributable to the growing number of people with dementia living in low-and-middle-income countries [4].
3. Related Works
The research to identify the state of the art in the theme follows the criteria described in the systematic review developed in the article by Machado e Barbosa [55], and it was conducted by listing three main perspectives: (1) monitoring of vital signs; (2) location monitoring; and (3) analysis of the data collected in the monitoring.
The works were selected through searches in the following databases: ACM Digital Library, IEEE Xplore Digital Library, Journal of Medical Internet Research (JMIR), PubMed Central, Science Direct, and Springer Library. Among them, PubMed Central and JMIR stand out as literary bases in the area of health and natural sciences, while the other databases are a reference in computing.
The searches were based on terms synonymous with the subject of the work and its derivations, defined as (“Alzheimer” OR “Alzheimer’s Disease” OR “Alzheimer Patients” OR “Alzheimer’s Care”) AND (“care” OR “detect” OR “track” OR “monitoring” OR “Assistive Technology” OR “Patient Monitoring” OR “device” OR “smartphone” OR “smartphones” OR “mobile application” OR “mobile” OR “mHealth” OR “app”). These groups of terms were interleaved with each other to increase the reach of the search. The choice of works was selected considering the following elements: Alzheimer’s disease; vital signs monitoring features; and data analysis, resulting in better patient care.
The examination of the articles resulted in five papers that were selected as the basis for the research, which were analyzed as follows.
Thorpe et al. [56] presented a computational model that uses smartphones and smartwatches to calculate a set of metrics for spatial, temporal, and step-based mobility. This work has as its main objective the measurement of the mobility of people who have dementia. The work presented by Wan et al. [57] consists of a monitoring system in the dementia treatment process to support caregivers in meeting the specific needs of each person with the symptoms of cognitive loss and memory loss. Nesbitt et al. [48] approaches the monitoring of vital patient information via sensors, and the objective is to monitor the location of individuals and measure physical and physiological changes, such as limb movements, vocalizations, and heart rate that occur during the state of agitation. In order to explore an application of remote monitoring technologies, the work developed by Amato et al. [58] can detect the onset of crises in patients affected by Alzheimer’s disease and aims to reduce the psychological burden suffered by caregivers. Lai Kwan et al. [59] presented in 2019 a work with the objective of detecting significant moments of people with dementia, making visible what is most significant to them and maintaining a sense of interpersonal connection between their caregivers and family members.
The differential of the proposed work in comparison with the related works is listed according the criteria below:
Vital signs monitoring: It performs vital signs data collection through a wearable, which is used daily by the patient.
Monitoring by geolocation: It performs collection of geolocation data, which is used daily by the patient.
Data analysis: It performs analysis on the collected data.
Result of the momentary data analysis: It performs the analysis of the collected data instantly, with no further analysis.
Danger alert: It performs real-time alert triggering if the patient is committed to any danger to his health or safety.
Action predictions: Behavior predictions based on historical data.
All works presented include the data analysis criterion. The solutions presented in the works of Thorpe et al. [56] and Wan et al. [57] aim to make specific reference to monitoring the patient on his geolocation, identifying his position to control his caregivers, not covering the rest of the criteria specified in the comparison with DCARE. Another point to consider is that only the solution of the work of Thorpe et al. [56] presents an approach to validate the vital aspects in order to legitimize the necessary actions to guarantee the safety and health of the patient (danger alert), also not covering the rest of the criteria specified in the comparison with the DCARE.
With regard to the work carried out by Nesbitt et al. [48] and Amato et al. [58], the definitions demonstrate that although a set of sensors is also used, data analysis and later presentation of results are performed, covering some of the criteria. The computational models proposed in these works do not present in their architecture an approach to process the various mental disorders and behaviors that a patient with Alzheimer’s disease can trigger, not covering one of the main criteria proposed in the DCARE model, which is to ensure the information already processed is displayed immediately in real time to the caregiver, and an alert is sent in case of danger.
In the computational model developed by Lai Kwan et al. [59], context sensitivity is used to correlate the user’s vital signs with the analysis of mental health in order to identify in which situations the user may have reached reactions of affective commitment with their caregivers or family members. In this case, no type of evaluation is sent to the caregiver; therefore, it is a model in which the information is analyzed later and only used for the purpose of identifying peaks in mental health. Therefore, the monitoring of vital data is not carried out, and the sending of information is not performed immediately to the caregiver in case of danger to the patient’s health, and does not cover the requirements indicated in this sense.
Figure 1 shows the comparison between the related works analyzed and the proposed work. The figure allows to observe that the models presented do not reach the proposed objectives to complete the general aspects of monitoring vital data, GPS location monitoring, analysis and results of momentary data, presentation of data to the caregiver, and warning of danger on the health of the immediate patient or in the model of prediction to the caregiver. In addition, it is important to note that making predictions about behaviors is not considered in any of the works studied.
Therefore, in Section 4, we present DCARE, which means Dementia Care, a computational model that covers the proposed objectives for general monitoring, data processing, and sending alerts to the caregiver in case of danger to the patient. The main benefit in using the DCARE model, concerning the existing five approaches, is that the model developed helps patients be more independent and gives the caregivers a tool for helping them, monitoring the patients in their daily activities, and ensuring their safety. DCARE uses a context histories framework, which collects context information from the environment. Subsequently, this information is processed, generating user context predictions, allowing to automatically detect future actions, allowing the caregiver to have greater control and care for the patient. The main scientific contribution of this work is the specification of a model for monitoring people with Alzheimer’s disease during activities of daily living (ADLs), with the promotion of access for a tool to health care and patient safety, in addition to contributing to the development of a data set simulator with ADL scenarios for patients with Alzheimer’s disease.
4. The DCARE model
This section describes the proposed computational model for monitoring and caring for patients with Alzheimer’s disease. The section is divided into three subsections. In Section 4.1, we detail the architecture of the model and its components. In Section 4.2, we present the context entities that make up the model, and finally, in Section 4.3, we discuss the details of context predictions.
4.1. Architecture
The DCARE architecture was designed based on the technical modeling TAM (Standard for Technical Architecture Modeling), created by the company SAP [60]. Figure 2 presents architecture of the DCARE model composed of actors (
The architecture on the DCARE application server has five components and a database. First, the architecture presents the communication between the model and the data availability application, which is an external design application that provides the GPS location information and the patient’s heart rate signals collected from a wearable. DCARE makes requests to the application of data availability through an API; this communication is carried out through the
After receiving the data from
The
The
At the end of the data processing, DCARE sends the results of context prediction to the gateway
4.2. Context Entities
Context-sensitive architectures that use not only present contexts, but also contexts about the past, need to store observed contexts for later use [13]. These works generally have a well-defined domain representation through an ontology. Ontology represents knowledge about low and high levels of contexts, presenting the entities involved, as well as their relationship in a semantic way [61].
The
Figure 3 shows the context data modeling used by DCARE, both in data acquisition and processing. The DCARE ontology is used to obtain and classify the context information of the project. The entity
4.3. Context Prediction
DCARE provides the monitoring of people with Alzheimer’s disease during their daily lives, monitoring the user and collecting daily information about GPS location and vital signs of the patient, which can help in more appropriate and personalized care for users, providing greater support for caregivers in the tasks of preserving the health and safety of the patient.
The DCARE is based on a tool for analysis and processing of contexts using the concepts of Context Histories and Context Prediction, allowing to record data according to the selection of relevant contextual information.
The operation of DCARE is managed by two stages, each of which performs a specific set of tasks for the functioning of the modules. The stages are divided into: (1) Data acquisition, processing/identification of contexts, and generation of Context Histories; and (2) Identification of behavior, context predictions and alert generation. The execution starts as soon as the data is made available by the external application that collects the data. The first stage performs the collection of patient data and associates it with the model’s ontology, then stores the data in the database, that is, resulting in the generation of Context Histories. The second stage, in the first step, performed in the
5. Implementation and Interviews
The implementation of a prototype following the specifications of the model was carried out using the programming language Python, in order to allow its evaluation. Thus, to develop the architecture of the project, an SQL database was used to implement the
The interview technique was utilized to collect the information that was used in the development of the model’s scenarios to identify the different contexts that make up the daily lives of Alzheimer’s patients and their caregivers. This technique was chosen taking into consideration the wide range of possible situations in which the patient may find himself and seeking to enrich the work with scenarios that are not usually highlighted in the academic literature. According to Ribeiro [69], the interview is used whenever there is a need to obtain data that cannot be found in records and documentary sources, which may be provided by certain people. Also according to Ribeiro [69], the interview is the most pertinent technique when the researcher wants to obtain information about his object, which allows to know about attitudes, feelings and values underlying the behavior, which means that one can go beyond the descriptions of the actions, incorporating new sources for the interpretation of the results by the interviewers themselves. In order to detect the view of caregivers and health professionals about their daily routine in different environments, some relevant scenarios were developed based on the academic literature, where the interviewees were invited to comment and contribute with information about the scenario in addition to proposing new scenarios based on the objective of the work. The scenarios developed based on the literature that make up the research items of the interviews are described in Table 1.
Table 2 shows the interviews that were carried out with the volunteers who fit the caregiver positions, being a family member or health professional. The interviews were conducted via video call by an application for smartphone, and were recorded and saved for later analysis, according to the consent of the interviewees. The profile of the interviewees is diverse, and is composed of caregivers who have experience between 1 and 28 years of age working in the care of people with Alzheimer’s disease. With regard to occupation, 60% of the interviewees have a profession in the health area, caregiver of the elderly and/or caregiver of patients with Alzheimer’s disease, with the other 40% of the interviewees being family members of the patients.
The interviews allowed to construct a synthesis of the data and identify common behaviors performed by patients with Alzheimer’s disease that can be identified as dangerous to the patient’s health and safety. As a result of the interviews, the scenarios previously identified based on the literature were approved, and four more scenarios were added for validation according to Table 3. Resulting in ten scenarios to be used by the model.
6. Evaluation and Results
This section describes the evaluation and results. The section is divided into two subsections. Section 6.1 details the evaluation aspects. Section 6.2 describes how model was evaluated, using the DCARE Dataset Simulator Tool, and finally, Section 6.3 presents the lessons learned and limitations of this work.
6.1. Evaluation Aspects
The tests were performed with a data set with 1026 samples generated by DCARE Dataset Simulator. The observations were generated randomly, taking into account the impartiality of the simulator in the choice. The sample was segmented in 80% for training the model and 20% for the test to evaluate the ML model, following the pattern commonly used in the machine learning projects.
The main regression machine learning algorithms were researched to enable the prediction of the risk of BPSD of patients with AD to preventively notify caregivers and familiars. The linear Regression (LR), k-Nearest Neighbor (kNN) and PLSRegression were evaluated. The strong correlation between the State Heart Rate and Caregiver Alert Required was found. After the data analysis and experimentation, the PLSRegression resulted in the best performance metrics among the ML algorithms tested.
The tests were carried out using scikit-learn library, approaching specifically the module
Table 4 shows the average accuracy of the model in relation to context predictions according to different sample quantities. The smallest amount of samples used (100) allowed to identify the lowest average accuracy with 97.3%, and with the largest amount of samples (1026) it is possible to identify that the average accuracy is high, reaching 97.5%.
From the different tests performed, changing the amount of samples used, it was possible to identify that the general accuracy of the model is 97.44%. Based on the simulations carried out on the data, it can be concluded that the model was effective, in order to predict scenarios dangerous to the health or safety of the patient in a predictive way.
The analysis of the model metrics’ performance indicates that the strong correlation between the features State Heart Rate and the Caregiver Alert Required exists. It is constant and is present in the different number of samples tested. Given the characteristics of BPSD events, the PLSRegression model fits the data in order to predict with high accuracy the events.
A confusion matrix (Figure 4) allowed to further detail the results. In this way, it is possible to identify results related to false positives (15 cases), false negatives (11 cases), true positives (22 cases), and true negatives, in which 978 cases were found.
The additional performance metrics indicate the mean precision of 59.45%, recall of 66.66%, and general f1-score of 62.85 %. The number of false positives (11 cases) is considered elevated concerning the true positives. It reflects in a medium model precision and represents that a considerable portion of BPSD would not be reported. On the other hand, 15 cases were erroneously registered as positive. In this case, despite false notifications could be launched, the proportion about the real negative cases is lower.
6.2. DCARE Dataset Simulator Tool
The evaluation was carried out using the acceptability test based on the data collected through the interviews described in Section 5. The records that represent instances of the scenarios used in the tests were generated with the DCARE Dataset Simulator Tool.
The application was built using the Python programming language as a base. The simulator takes into account each of the items specified in Section 4.2. Each simulation is generated according to the random choice of items in the structure. During the simulations, all items are replaced by a dictionary model, where each element receives an enumerator as an identifier.
The construction of the simulator was carried out according to the enumerated data identified with a dictionary model, presented in Table 5. For the generation of the data set, the scenarios identified in Section 5, were built with the structure:
(1). id_cenario: enumerated identification of the scenario;
(2). heart_rate_state: state of the heart rate for the duration specified in the context;
(3). activity: behavior of the patient specified in the context duration;
(4). semantic_location: significant patient’s location according to the daily living space;
(5). duration im minutes: duration of the context (in minutes);
(6). timestamp: start and end date of the context;
(7). necessary_alert_caregiver: if there is a need to send a danger alert to the caregiver.
The scenarios used in the construction of the data set were built according to the Table 6, and generated in the file format csv.
6.3. Lessons Learned and Limitations
This research focused on a solution that approaches smart environments [44] oriented to the healthcare of people with Alzheimer’s disease. During this study, many lessons were learned as a consequence of this applied project. Alongside the main activities, some insights emerged resulting from technical experience of design, modeling, and development of software artifacts, besides the implementation of DCARE Simulator. Moreover, other ideas were documented reporting medical and social aspects learned while the relationship with patient’s familiars and healthcare professionals happened.
Therefore, twenty learned lessons were organized into three groups, that is, technological, methodological and medical. Table 7 summarizes these items, mapping these observations of this work.
In addition, this study is an initial validation of the viability of the clinical use of the DCARE model. Therefore, some limitations were identified during its execution. The first limitation was the reduced number of pairs of caregivers and patients who used the DCARE, and none of the patients were final users.
The second limitation was that the time using the prototype was not sufficient to collect enough insights to analyze both the engagement and effectiveness in DCARE usage in contrast to the traditional approach.
To overcome these limitations the new steps of this project should encompass the recruitment of real AD volunteers to evaluate the proposal as a whole during long-term monitoring for some months.
Another limitation of this study is related to the use of synthetic data instead of real sensor data to represent the vital signs, movements and sounds. To cope with this limitation, several IoT and wearables have been tested to be integrated into future assessments in the clinical and domestic setup. Challenges regarding the software integration, device battery duration, unobtrusive and ubiquitous use as well as safety issues should be faced to achieve these goals.
7. Conclusions and Future Works
This work presented in detail the model DCARE for monitoring people with Alzheimer’s disease, in a scenario of day-to-day care by their caregivers. Based on studies of the works related to the theme, the opportunity to develop a model to offer monitoring of patients with Alzheimer’s disease was noted, enabling individual care for each user, in which the analyzes performed result in the prediction and/or alert possible dangers to the health and safety of the patient. The data collected based on the Context Histories concept are analyzed and processed, resulting in the understanding of behaviors or prediction of future behaviors of the patient, generating an alert to the caregiver if it demonstrates any type of danger to the health or safety of the user, these being the great differentials in relation to the current state of the art.
The construction of the scenarios that were used in the development of the model, was carried out in the interviews with five volunteers, with profiles of health professionals and family members. The mass of data for carrying out the tests was generated by DCARE Dataset Simulator. The results of the tests based on the predictions of contexts showed that the developed model met the objective of the project, reaching 97.44% of general rate of accuracy of prediction of scenarios. Based on the simulations performed on the data, it can be concluded that the model was effective, in order to predictively predict scenarios of danger to the health or safety of the patient, in a predictive way.
The main scientific contribution of this work is the specification of a model for monitoring people with Alzheimer’s disease during their daily lives, resulting in helping their caregivers, interacting with technological resources, promoting access to a tool for health care and patient safety, in addition to contributing to the development of a data sets simulator with scenarios of daily activities of patients with Alzheimer’s disease.
As a social contribution, we can highlight the development of a new resource for use within the area of mental health care, collaborating with the development of an application that can be widely used by caregivers.
Based on the studies of this work, new possibilities arise for the continuation of future works. The use of the model for people with other types of progressive neurodegenerative diseases is promising, especially due to the daily monitoring feature, generating important information for caregivers about the individual health of each user. Furthermore, the use of real data obtained from specialized IoT devices and wearables could enrich the results and approximate the solution of the end-user.
Author Contributions
Conceptualization, S.D.M., J.E.d.R.T., M.G.M., and J.L.V.B.; investigation, S.D.M. and J.L.V.B.; methodology, S.D.M., J.E.d.R.T., and J.L.V.B.; software, S.D.M.; project administration, J.L.V.B. and J.E.d.R.T.; supervision, J.L.V.B.; validation, S.D.M., J.E.d.R.T., M.G.M., and J.L.V.B.; writing—original draft, S.D.M., J.E.d.R.T., M.G.M., and J.L.V.B.; writing—review and editing, J.L.V.B., V.R.Q.L., and G.V.G.; financial, V.R.Q.L. and G.V.G. All authors have read and agreed to the published version of the manuscript.
Funding
Project managed by Fundación General de laUniversidad de Salamanca and co-financed with Junta de Castilla y León and FEDER funds, in part by the postdoctoral fellowship fromthe University of Salamanca and Banco Santander, and in part by national funds through the Fundação para a Ciência e a Tecnologia, I.P. (Portuguese Foundation for Science and Technology) by the project UIDB/05064/2020 (VALORIZA—Research Centre for Endogenous Resource Valorization).
Institutional Review Board Statement
Not applicable.
Informed Consent Statement
This research did not require ethical approval in accordance with the regulations of the University of Vale do Rio dos Sinos (UNISINOS). The subjects who participated in the evaluation were not patients in treatment for Alzheimer’s disease, but voluntary caregivers. They assessed the usability aspects of DCARE and not the effectiveness of its application in the treatment. In addition, the participants agreed to participate in the evaluation of DCARE.
Data Availability Statement
Not applicable.
Acknowledgments
The authors would like to thank the University of Vale do Rio dos Sinos (Unisinos), the Applied Computing Graduate Program (PPGCA), the Mobile Computing Laboratory (Mobilab), the Research Support Foundation of the State of Rio Grande do Sul (FAPERGS), the National Development Council Scientific and Technological (CNPq), the Coordination for the Improvement of Higher Education Personnel - Brazil (CAPES) - Code Funding 001, the Seed Funding ILIND–Instituto Lusófono de Investigação e Desenvolvimento, COPELABS [COFAC/ILIND/COPELABS 2020]. Proyeto Uso de algoritmos y protocolos de comunicación en dispositivos con énfasis en la privacidad de los datos.
Conflicts of Interest
The authors declare no conflict of interest.
Abbreviations
The following abbreviations are used in this manuscript:
| AD | Alzheimer’s Disease |
| ADL | Activity of Daily Living |
| AmI | Ambient Intelligence |
| BPSD | Behavioral and Psychological Symptoms of Dementia |
| GDP | Gross Domestic Product |
| HRV | Heart Rate Variability |
| ICT | Information and Communication Technologies |
| IoT | Internet of Things |
| WHO | World Health Organization |
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Figures and Tables
Table 1
Scenarios developed based on the literature.
| ID | Description | Heart Rate | Activity | Semantic Location | Duration in Minutes | Start Time/End Time | It Is Necessary |
|---|---|---|---|---|---|---|---|
| CE1 | The patient is experiencing a crisis of psychological dysfunction (BPSD symptoms—behavioral and psychological symptoms of dementia) | regularStirring/ |
stopped | houseBathroom/ |
1 min | Between 00:00 AM and 11:59 PM | yes |
| CE2 | The patient is on the run without monitoring (symptoms BPSD—behavioral and psychological symptoms of dementia) | regular/ |
walk/run | houseBalcony/houseCourtyard/ |
1 min | between 00:00 AM and 11:59 PM | yes |
| CE3 | The patient is experiencing a crisis of psychological dysfunction related to unknown environments (symptoms BPSD—behavioral and psychological symptoms of dementia) | regularStirring/ |
stopped/ |
houseCourtyard/ |
1 min | between 00:00 AM and 11:59 PM | yes |
| CE4 | The patient is experiencing a respiratory/cardiac health problem | irrregularStirring | stopped | houseBathroom/ |
1 min | between 00:00 AM and 11:59 PM | yes |
| CE5 | The patient is sleeping | atRest | stopped | houseRoom/ |
1 min | between 00:00 AM and 11:59 PM | no |
| CE6 | The patient is in a regular scenario | regular | stopped/ |
houseBathroom/ |
1 min | between 08:01 AM and 11:59 PM | no |
Interviewee profiles.
| Scenario ID | Professional and Academic Profile | Years of Experience |
|---|---|---|
| CE1 | Geriatric Caregiver; |
17 |
| CE1 | Nursing technique; |
1 |
| CE2 and CE4 | Caregiver of Patients with Alzheimer’s Disease; |
28 |
| CE3 | Family person | 2 |
| CE3 | Family person | 12 |
Scenarios developed based on the interviews.
| ID | Description | Heart Rate | Ativity | Semantic Location | Duration in Minutes | Start Time/End Time | It Is Necessary |
|---|---|---|---|---|---|---|---|
| CE1 | The patient is experiencing a seizure (symptoms BPSD - behavioral and psychological symptoms of dementia) | irrregularStirring | stopped/ |
houseBathroom/houseRoom/ |
1 min and 30 s | between 00:00 AM and 11:59 PM | yes |
| CE2 | The patient is in a fall scenario when trying to perform bathing or hygiene activities alone (symptoms BPSD—behavioral and psychological symptoms of dementia) | regularStirring/ |
stopped | houseBathroom | 1 min | between 00:00 AM and 23:59 PM | yes |
| CE3 | The patient is in a scenario where he wakes up during the night and is without monitoring, with no sense of time and hours (symptoms BPSD—behavioral and psychological symptoms of dementia) | Normal/ |
stopped/ |
houseBathroom/houseRoom/ |
1 min | night rest time, between 11:00 PM and 8:00 AM | yes |
| CE4 | The patient is in a scenario where he has some type of infection in the body but does not show fever (symptoms BPSD—behavioral and psychological symptoms of dementia) | regularStirrin/ |
stopped/ |
houseBathroom/houseRoom/ |
2 h | between 00:00 AM and 11:59 PM | yes |
Accuracy tests of the DCARE model.
| Number of Samples | Average Accuracy |
|---|---|
| 100 | 97.3% |
| 200 | 97.4% |
| 400 | 97.5% |
| 600 | 97.5% |
| 1026 | 97.5% |
DCARE Dataset Simulator dictionary structure data.
| Item Description | States of Each Item |
|---|---|
| State Heart Rate | atRest (1), regular (2), regularStirring (3), irregularStirring (4) |
| Activity | stopped (1), walk (2), run (3), inVehicle (4) |
| Semantic Location | houseBathroom (1), houseRoom (2), houseLivingRoom (3), houseKitchen (4), houseBalcony (5), houseCourtyard (6), houseOutsideCourtyard (7) |
| Caregiver Alert Required | false (0), true (1) |
Scenarios used to generate DCARE Dataset Simulator datasets.
| ID | Description of the Scenario | (1) | (2) | (3) | (4) | (5) | (6) | (7) |
|---|---|---|---|---|---|---|---|---|
| CN1 | Psychological dysfunction crisis | 1 | 3, 4 | 1 | 1, 2, 3, 4, 5, 6, 7 | 1 | 1 | |
| CN2 | Escape without Monitoring | 2 | 2, 3 | 2, 3 | 5, 6, 7 | 1 | 1 | |
| CN3 | Psychological Dysfunction in an unknown environment | 3 | 3, 4 | 1, 2, 4 | 7, 8 | 1 | 1 | |
| CN4 | Respiratory Health Problem | 4 | 4 | 1 | 1, 2, 3, 4, 5, 6, 7 | 1 | 1 | |
| CN5 | Sleeping | 5 | 1 | 1 | 2, 3 | 1 | 0 | |
| CN6 | Normal Scenario | 6 | 2 | 1, 2, 4 | 1, 2, 3, 4, 5, 6, 7 | 1 | “08:01:00”, “22:59:59” | 0 |
| CN7 | Health crisis, seizure | 7 | 4 | 1, 2, 3, 4 | 1, 2, 3, 4, 5, 6, 7 | 2 | 1 | |
| CN8 | Fall in bathing/hygiene activities | 8 | 3, 4 | 1 | 1 | 1 | 1 | |
| CN9 | Awake during unaccompanied dawn | 9 | 2, 3, 4 | 1, 2, 3 | 1, 2, 3, 4, 5, 6 | 1 | “23:00:00”, “08:00:59” | 1 |
| CN10 | Infection, but without fever | 10 | 3, 4 | 1, 2, 3, 4 | 1, 2, 3, 4, 5, 6, 7 | 120 | 1 |
Lessons learned.
| Item | Category | Description |
|---|---|---|
| 1 | Medical | People with Alzheimer’s disease have peculiar characteristics that differ from other types of Dementia and Neurodegenerative diseases that need to be observed. Especially the BPSD events. |
| 2 | Medical | The architecture of the solution must address the various mental disorders and behaviors that a patient with Alzheimer’s disease can manifest. It may be physical, mental, or emotional, including loss of cognition and memory. |
| 3 | Medical | Vital signal analysis, mainly the heart rate variation and sweating, besides movement and location data are highly valued by experts. |
| 4 | Medical | The monitoring, follow-up and diagnostic are important. However, the predictive approach, in order to support caregivers and family members, is even more strategic as it can assist in the treatment and prevention of damage from the disorders manifested by the person with AD. |
| 5 | Medical | The holistic view of the context’s history, encompassing not only isolated events, is of great value to the caregiver, doctors, and family members, as it helps in monitoring and serves as a support for decision-making in terms of treatments, medications and other clinical interventions. Allowing even more detailed monitoring of disease progression. |
| 6 | Medical | The use of technologies for the monitoring and follow-up of patients with neurodegenerative diseases in a nonintrusive way in their ADLs is a more natural approach. It tends to generate more qualified results because the conditioning of tests in offices or clinics may not accurately represent the reality of the patient or there may be losses due to time lapses between the assessment and the BPSD events that are sporadic and random. Therefore, the detection or even more positively the prediction of BPSD events can contribute significantly to the increase in the quality of life of people with AD. The storage and management of context’s history can add new insights for caregivers and family members involved in daily patient’s healthcare routines. |
| 7 | Technological | The alert notification proved to be a important resource and valued by the evaluators, as it expands the capacity for monitoring and assisting the patient, even remotely. It was praised both by family members and by specialists participating in this study. This feature is a promising resource that can positively assist the healthcare of peolple with Alzheimer’s disease. |
| 8 | Technological | The development of an app with an accessible interface is helpful for people with Alzheimer’s disease. There are few solutions for monitoring and predicting adverse events for these people. |
| 9 | Technological | The use of data related to vital signs, especially HR, proved to be fundamental for the analysis of BPSD events. |
| 10 | Technological | The use of unobtrusive wearables is more natural and accepted by patients. This approach enables health monitoring without overexposing privacy like some approaches that use video monitoring. |
| 11 | Technological | Ontology was an essential software artifact for modeling the simulated scenarios and standardizing attributes. It enables easy communication between those involved in the project and aligning the technical aspects of the application. |
| 12 | Technological | The application of machine learning algorithms using the Python language and the scikit learn library proved to be assertive and effective for the modeling and assessment of the experiments. |
| 13 | Technological | Smart environments that employ diverse sensors and wearables are a promising approach to aid in the monitoring and treatment of AD People. |
| 14 | Technological | The absence of standardized data sets was observed. DCARE Simulator allows the sharing of the generated data set. It also could generate new customized data sets. |
| 15 | Technological | The generation of simulated scenarios contributes to experimentation, especially because the people are patients with special needs. Nowadays, it is a period of restriction due to the pandemic. Therefore, the DCARE simulator can help to overcome the limitations of assessments. |
| 16 | Technological | Simulator could have considered profile customization and profile balancing to avoid possible bias in the generated data set. |
| 17 | Technological | People’s location is a key aspect in supporting the healthcare of AD people, mainly indoor. However, it appears that the precision in an indoor location still lacks greater accuracy and accessibility as occurs outdoors. |
| 18 | Methodological | The mixed approach of obtaining scenarios via literary review combined with the description of scenarios via interviews with specialists and family members of AD people was an assertive choice. It was essential to make the evaluation process more assertive and to qualify the generation of the scenarios generated by the simulator. This approach makes possible a great approximation of the reality of AD people, without exposing them to risks, nor to any resistance or inconvenience of eventual long term and continuous monitoring. |
| 19 | Methodological | The use of the Technical Architecture Modeling (TAM) modeling methodology, created by the SAP company, proved to be favorable in the standardization of DCARE architecture. |
| 20 | Methodological | The evaluation of usability, usefulness, and technology acceptance by users would add even more value to the solution. It could shorten the distance between the application interface features to the real needs and preferences of AD people. |
© 2021 by the authors.
Details
; João Elison da Rosa Tavares 1
; Márcio Garcia Martins 1
; Jorge Luis Victória Barbosa 1
; Gabriel Villarrubia González 2
; Valderi Reis Quietinho Leithardt 3
1 Applied Computing Graduate Program, University of Vale do Rio dos Sinos, Av. Unisinos 950, Bairro Cristo Rei, São Leopoldo RS 93022-750, Brazil;
2 Expert Systems and Applications Laboratory, Faculty of Science, University of Salamanca, Plaza de los Caídos s/n, 37008 Salamanca, Spain;
3 COPELABS, Universidade Lusófona de Humanidades e Tecnologias, 1749-024 Lisboa, Portugal;