Human motion activity recognition and pattern analysis using compressed deep neural networks-Reference-Cited by-同舟云学术

Human motion activity recognition and pattern analysis using compressed deep neural networks

Published:2024-04-04 Issue:1 Volume:12 Page:
ISSN:2168-1163
Container-title:Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization
language:en
Short-container-title:Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization

Author:

Kumari Navita¹,Yadagani Amulya²,Behera Basudeba¹,Semwal Vijay Bhaskar²,Mohanty Somya³

Affiliation:

1. Department of Electronics and Communication Engineering, National Institute of Technology, Jamshedpur, India

2. Department of Computer Science Engineering, University of North Carolina at Greensboro, Greensboro, NC, USA

3. Department of Computer Science Engineering, Maulana Azad National Institute of Technology, Bhopal, M.P, India

Funder

DST, Govt. of India, and Research under the SERB [Empowerment and Equity Opportunities for Excellence in Science]

Publisher

Informa UK Limited

Link

https://www.tandfonline.com/doi/pdf/10.1080/21681163.2024.2331052

Reference41 articles.

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2. American Psychological Association. 2010. Publication manual of the American psychological association. 6th ed. Washington DC: Canadian Psychological Association; p. 272.

3. Banbury CR et al. Benchmarking TinyML systems: challenges and direction. arXiv preprint arXiv:2003.04821 2020.

4. Sensorized Assessment of Dynamic Locomotor Imagery in People with Stroke and Healthy Subjects

5. Internet of Things and Its Applications

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