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

• URL: http://arxiv.org/abs/2206.07654v1
• Date: Fri, 3 Jun 2022 19:24:20 GMT
• Title: Human Activity Recognition on Time Series Accelerometer Sensor Data using LSTM Recurrent Neural Networks
• Authors: Chrisogonas O. Odhiambo, Sanjoy Saha, Corby K. Martin, Homayoun Valafar
• Abstract summary: In this study, we focus on the use of smartwatch accelerometer sensors to recognize eating activity. We collected sensor data from 10 participants while consuming pizza. We developed a LSTM-ANN architecture that has demonstrated 90% success in identifying individual bites compared to a puff, medication-taking or jogging activities.
• Score: 0.2294014185517203
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• 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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