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• W3093417991 abstract "Human activity recognition (HAR) has grown in popularity as sensors have become more ubiquitous. Beyond standard health applications, there exists a need for embedded low cost, low power, accurate activity sensing for entertainment experiences. We present a system and method of using a deep neural net for HAR using low-cost accelerometer-only sensor running at 0.8Hz to preserve battery power. Despite these limitations, we demonstrate an accuracy at 94.79% over 6 activity classes with an order of magnitude less data. This sensing system conserves power further by using a connectionless reading---embedding accelerometer data in the Bluetooth Low Energy broadcast packet---which can deliver over a year of human-activity recognition data on a single coin cell battery. Finally, we discuss the integration of our HAR system in a smart-fashion wearable for a live two night deployment in an instrumented night club." @default.
• W3093417991 created "2020-10-22" @default.
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• W3093417991 date "2020-10-12" @default.
• W3093417991 modified "2023-09-27" @default.
• W3093417991 title "AI at the Disco" @default.
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• W3093417991 doi "https://doi.org/10.1145/3422852.3423485" @default.
• W3093417991 hasPublicationYear "2020" @default.
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