AI at the disco: Low sample frequency human activity recognition for night club experiences
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.
|HAR, human activity recognition, deep learning, CNN, RF, low-frequency, sampling, power, battery, nightclub|
|Huawei, Amsterdam, The Netherlands , IBM Research, Dublin, Ireland|
|HuMA 2020 - Proceedings of the 1st International Workshop on Human-Centric Multimedia Analysis|
|Organisation||Centrum Wiskunde & Informatica, Amsterdam, The Netherlands|
Gill, A.S, Cabrero Barros, S, César Garcia, P.S, & Shamma, D.A. (2020). AI at the disco: Low sample frequency human activity recognition for night club experiences. In HuMA 2020 - Proceedings of the 1st International Workshop on Human-Centric Multimedia Analysis (pp. 21–29). doi:10.1145/3422852.3423485