Publication: Deep Learning Approach for Complex Activity Recognition using Heterogeneous Sensors from Wearable Device
Issued Date
2021-09-01
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2-s2.0-85118385641
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Mahidol University
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SCOPUS
Bibliographic Citation
Proceedings - 2021 Research, Invention, and Innovation Congress: Innovation Electricals and Electronics, RI2C 2021. (2021), 60-65
Suggested Citation
Narit Hnoohom, Anuchit Jitpattanakul, Ilsun You, Sakorn Mekruksavanich Deep Learning Approach for Complex Activity Recognition using Heterogeneous Sensors from Wearable Device. Proceedings - 2021 Research, Invention, and Innovation Congress: Innovation Electricals and Electronics, RI2C 2021. (2021), 60-65. doi:10.1109/RI2C51727.2021.9559773 Retrieved from: https://repository.li.mahidol.ac.th/handle/20.500.14594/76636
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Title
Deep Learning Approach for Complex Activity Recognition using Heterogeneous Sensors from Wearable Device
Abstract
The classification of simple and complex sequences of operations is made easier according to the use of heterogeneous sensors from a wearable device. Sensor-based human activity recognition (HAR) is being used in smartphone platforms for elderly healthcare monitoring, fall detection, and inappropriate behavior prevention, such as smoking habit, unhealthy eating, and lack of exercise. Common machine learning and deep learning techniques have recently been presented to tackle the HAR issue, with a focus on everyday activities, particularly general human activities including moving, sitting, and standing. However, there is an intriguing and challenging HAR research subjects involving more complicated psychological activities in various environments, including smoking, eating, and drinking. The use of heterogeneous sensor data to enhance recognition performance over sensor-based deep learning networks is considered in this work. We demonstrate that using a combination of two inertial measurement units outperforms employing either an accelerometer or a gyroscope by utilizing four deep learning classifiers to recognize complex human activity (CHA). Furthermore, we describe the impact of five window sizes (5s - 40s) on a publicly accessible benchmark dataset and how increasing window size effects to the classification performance of CHA deep learning networks.