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

Chrisogonas O. Odhiambo; Sanjoy Saha; Corby K. Martin; Homayoun Valafar

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

Signal Processing 2022-06-16 v1 Human-Computer Interaction Machine Learning

Authors: Chrisogonas O. Odhiambo , Sanjoy Saha , Corby K. Martin , Homayoun Valafar

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.

Keywords

human activity recognition wearable physiological monitoring

Cite

@article{arxiv.2206.07654,
  title  = {Human Activity Recognition on Time Series Accelerometer Sensor Data using LSTM Recurrent Neural Networks},
  author = {Chrisogonas O. Odhiambo and Sanjoy Saha and Corby K. Martin and Homayoun Valafar},
  journal= {arXiv preprint arXiv:2206.07654},
  year   = {2022}
}

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8 pages, Accepted for publication at 2022 CSCE Conference (SPRINGER NATURE - Research Book Series)

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

Abstract

Keywords

Cite

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