Abstract
Automatic activity detection is important for remote monitoring of elderly people or patients, for context-aware applications, or simply to measure one’s activity level. Recent studies have started to use accelerometers of smart phones. Such systems require users to carry smart phones with them which limit the practical usability of these systems as people place their phones in various locations depending on situation, activity, location, culture and gender. We developed a prototype for shoe based activity detection system that uses pressure data of shoe and showed how this can be used for remote monitoring. We also developed a multimodal system where we used pressure sensor data from shoes along with accelerometers and gyroscope data from smart phones to make a robust system. We present the details of our novel activity detection system, its architecture, algorithm and evaluation.
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This work was partially supported by grant from IBCRF.
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Kawsar, F., Ahamed, S., Love, R. (2015). Remote Monitoring Using Smartphone Based Plantar Pressure Sensors: Unimodal and Multimodal Activity Detection. In: Bodine, C., Helal, S., Gu, T., Mokhtari, M. (eds) Smart Homes and Health Telematics. ICOST 2014. Lecture Notes in Computer Science(), vol 8456. Springer, Cham. https://doi.org/10.1007/978-3-319-14424-5_15
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DOI: https://doi.org/10.1007/978-3-319-14424-5_15
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