Shalini Mukhopadhyay
Arpan Pal
ABSTRACT
In this work, we present a lightweight attention network to perform Time Series Classification on Edge devices. We evaluate the merit of our system on a Human Activity Recognition dataset and show the demonstration with the help of a Wearable device (Smartwatch) with IMU sensors.
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- Iveta Dirgová Luptáková, Martin Kubovčík, and Jiří Pospíchal. 2022. Wearable sensor-based human activity recognition with transformer model. Sensors 22, 5 (2022), 1911.Google ScholarCross Ref
- Niloy Sikder, Md Atiqur Rahman Ahad, and Abdullah-Al Nahid. 2021. Human Action Recognition Based on a Sequential Deep Learning Model. In 2021 Joint 10th International Conference on Informatics, Electronics Vision (ICIEV) and 2021 5th International Conference on Imaging, Vision Pattern Recognition (icIVPR). 1--7. Google ScholarCross Ref
- Niloy Sikder and Abdullah-Al Nahid. 2021. KU-HAR: An open dataset for heterogeneous human activity recognition. Pattern Recognition Letters 146 (2021), 46--54. Google ScholarCross Ref
- Alexander Wong, Mahmoud Famouri, Maya Pavlova, and Siddharth Surana. 2020. Tinyspeech: Attention condensers for deep speech recognition neural networks on edge devices. arXiv preprint arXiv:2008.04245 (2020).Google Scholar
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