Abstract
Multiple kinds of sensors in smart homes have been used successfully and widely on various pattern recognition tasks. In order to detect user’s activities of daily living (ADLs), an array of sensors have to be installed in many places in a smart home or armed upon a user’s body. Here, we present an approach for collecting and detecting activities data only via a smart phone, which largely reduces the cost of setup in a smart home and energy consumption. To the best of our knowledge, this study represents a pioneering work where a single-point smart phone is used to capture ADLs. The ADLs indoor are recognized by analyzing the data combination of sound, orientation, and Wi-Fi signals. This study engages real-life data collection, and the results from four test environments show that all of the ADL recognition rates are above 90 %.
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Acknowledgments
We would like to acknowledge the tremendous support provided by professor Muchun Su of National central university in order to conduct a full-scale experiment and collect a significant amount of data.
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Feng, Y., Chang, C.K., Chang, H. (2016). An ADL Recognition System on Smart Phone. In: Chang, C., Chiari, L., Cao, Y., Jin, H., Mokhtari, M., Aloulou, H. (eds) Inclusive Smart Cities and Digital Health. ICOST 2016. Lecture Notes in Computer Science(), vol 9677. Springer, Cham. https://doi.org/10.1007/978-3-319-39601-9_13
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DOI: https://doi.org/10.1007/978-3-319-39601-9_13
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