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Top1. Introduction
With a rapidly ageing population and the debilitating effects of chronic diseases, our society is becoming increasingly conscious of the importance of maintaining personal health. The traditional hospital-centered health services are out-of-date and cannot meet people’s requirements for their daily health care (Tabish, Mnaouer, Touati, & Ghaleb, 2013). It is widely recognized that long-term in-home health care, including daily activity and home environment monitoring, are necessary for the elderly. In the future, smart chip embedded on pendant and bracelet would be small and light enough for people to wear. Thus, there is an urgent need to improve current healthcare systems to be smarter and ubiquitous.
Sensors integrated into the Wireless Sensor Network (WSN) and mobile devices are promising candidates to resolve current problems and are expected to play important roles in next-generation healthcare systems. Body sensors have strong capabilities to sense and collect physical data from multiple sources, such as motion, photoplethysmophgraphy (PPG), electrocardiography (ECG), and electromyography (EMG). Other types of sensors can automatically monitor the home environment by detecting light, humidity, temperature, and more. These sensors, together with a radio transceiver, form a WSN. Sensors in a WSN are spatially distributed, and their ranges are capable of covering the entire home area, in most cases. Using current IP technology, this wireless network can connect to the Internet, in which case it is broadly referred to as the Internet of Things (IoT).
The Web of Things (WoT), built on top of the IoT, is the architecture for the application layer of the IoT. With standard web protocols, sensors which suffer from limited computing resources can be interconnected. This opens up opportunities for faster and more accurate smart healthcare systems delivering better user experience.
Contiki, as an open source operating system (OS) for the IoT, supports fully standard IPv6 and IPv4, along with the recent low-power wireless standards (Oikonomou & Phillips, 2011), including “IPv6 over Low-Power Wireless Personal Area Networks” (6LoWPAN), “IPv6 Routing Protocol” (RPL), and “Constrained Application Protocol” (CoAP). 6LoWPAN extends the use of IP networking into low-power devices with limited processing capabilities so that these resource-constrained devices can participate in the IoT (Hui, Culler, & Chakrabarti, 2009). RPL can support traffic for Low-Power and Lossy Networks (LLNs), in which the routers and interconnections are constrained (Vasseur et al., 2011). CoAP is designed to be a simpler alternative to Hypertext Transfer Protocol (HTTP) in the application layer for efficient communication between heterogeneous wireless networks and the Internet (Levä, Mazhelis, & Suomi, 2014). However the competition between HTTP and CoAP has yet to be resolved for the dominant position in future WoT applications.
Our study focuses on a web-based motion detection system for health care, using Contiki and its low-power wireless standards. Our contributions include:
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Analyzed user requirements
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Improved the system architecture of motion-sensing based monitoring system for health care (Gao, Zhao, Qiu, Yu, & Chang, 2015)
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Improved motion detection algorithm (Gao, Zhao, Qiu, Yu, & Chang, 2015)
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Explored Contiki and its supported low-power wireless standards to address communication and security issues
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Provided a technical evaluation to show benefits of our system in terms of performance
In this paper, we first present our motivations and contributions. Section 2 references related work. Then from Section 3 to Section 5 we provide our approaches and results. Section 6 contains our conclusion. Acknowledgements are in Section 7.