Human Activity Recognition on Time Series Accelerometer Sensor Data using LSTM Recurrent Neural Networks
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Abstract (arXiv)
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.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 7 of its 40 references that are in the map. First posted on arXiv 3 Jun 2022.
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7 of 40 references.
References · 7
- 2017Deep Residual Bidir-LSTM for Human Activity Recognition Using Wearable Sensorscited by 24
- 2019Automated Analysis of Femoral Artery Calcification Using Machine Learning Techniquescited by 4
- 2019Modelling of Sickle Cell Anemia Patients Response to Hydroxyurea using Artificial Neural Networkscited by 2
- 2020State Transition Modeling of the Smoking Behavior using LSTM Recurrent Neural Networkscited by 2
- 2020Recognition of Smoking Gesture Using Smart Watch Technologycited by 2
- 2021MedSensor: Medication Adherence Monitoring Using Neural Networks on Smartwatch Accelerometer Sensor Datacited by 1
- 2021Application of Machine Learning in Early Recommendation of Cardiac Resynchronization Therapycited by 1
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