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Received Jun 13, 2017; Revised Aug 31, 2017; Accepted Sep 20, 2017
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1. Introduction
The rapid growth of Internet of Things (IoT) has made it possible to sense many types of information from the real world. In the research area of human activity recognition, IoT devices equipped with various sensors have been widely used as a tool for sensing the activities. There are three approaches of activity recognition studies in terms of sensor deployment.
The first approach deploys sensors into the environment like a smart home. As typical sensors, camera [1], sound [2], and power consumption [3] have been used for monitoring dweller’s activities.
In the second approach, sensors are attached to the human. The smartphone has been widely used [4, 5] as a sensing tool in this approach because it has many sensors (an acceleration sensor, gyro sensor, magnetic sensor, etc.) as well as a processor, a large data storage, and wireless network. Nowadays, instead of the smartphone, wearable devices such as a smartwatch are used [6]. JINS MEME [7] is a smart eyewear having the electrooculography (EOG) electrodes in the bridge of the glasses and the nose pads. It tries to measure the movement of eyes for recognizing the internal contexts such as drowsiness and tiredness. Also, we have proposed Waiston Belt [8] that can measure the abdominal circumference and posture.
The third approach is a new paradigm where sensors are embedded in the various things surrounding us. Kurahashi et al. [9] have installed an acceleration sensor into a toilet paper holder for recognizing the person. HAPIfork [10] has embedded an acceleration sensor into a fork for monitoring eating activities. We have also added an acceleration sensor on a faucet of water or a remote control for sensing various activities of the dweller. We think that the spread of IoT makes...