Human Activity Recognition on Time Series Accelerometer Sensor Data using LSTM Recurrent Neural Networks

2022/06/03 by Chrisogonas O. Odhiambo, Odhiambo, Chrisogonas O., Sanjoy Kumar Saha +5
Computer Science · #Context-Aware Activity Recognition Systems #FOS: Computer and information sciences #FOS: Electrical engineering #Human-Computer Interaction (cs.HC) #Machine Learning (cs.LG) #Signal Processing (eess.SP) #electronic engineering #information engineering

paper · pdf · doi:10.48550/arxiv.2206.07654

openalex publication_date 2022/06/03 · openalex created_date 2022/06/18 · openalex updated_date 2026/07/28

Abstract

The use of sensors available through smart devices has pervaded everyday life in several applications including human activity monitoring, healthcare, and social networks. In this study, we focus on the use of smartwatch accelerometer sensors to recognize eating activity. More specifically, we collected sensor data from 10 participants while consuming pizza. Using this information, and other comparable data available for similar events such as smoking and medication-taking, and dissimilar activities of jogging, we developed a LSTM-ANN architecture that has demonstrated 90% success in identifying individual bites compared to a puff, medication-taking or jogging activities.

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