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.