Vision-Based Posture Detection for Rehabilitation Program

Sudhir Gaikwad; Shripad Bhatlawande; Atharva Dusane; Dyuti Bobby; Krushna Durole; Swati Shilaskar

doi:10.2991/978-94-6463-252-1_50

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Vision-Based Posture Detection for Rehabilitation Program

Authors

Sudhir Gaikwad¹^{, 1}^{, *}, Shripad Bhatlawande¹, Atharva Dusane¹, Dyuti Bobby¹, Krushna Durole¹, Swati Shilaskar¹

¹Department of Electronics & Telecommunication, Vishwakarma Institute of Technology, Pune, India

^*Corresponding author. Email: sg22jn@gmail.com

Corresponding Author

Sudhir Gaikwad

Available Online 9 November 2023.

DOI: 10.2991/978-94-6463-252-1_50 How to use a DOI?
Keywords: Posture Detection; LSTM; Machine Learning; Media Pipe; OpenCV; Rehabilitation; Random Forest
Abstract: Individuals with disabilities frequently struggle to do simple tasks. Recurrent workouts have been demonstrated to aid affected patients in rehabilitation. Physical rehabilitation therapy that can be self-managed provides a convenient solution for people with motor disabilities who may find it challenging to attend regular in-person therapy sessions. Analyzing body postures is instrumental in assisted living and health monitoring at home. Tracking body postures is a profound issue in computer vision. Monitoring the upper-limb posture of the body is the primary goal, while considering the complication of human pose, despite having no publicly available dataset. In this text, a self-data procurement system followed by real-time body posture recognition is implemented using LSTM. The posture classification accuracy is 93.75 percent. If current frames are incorrect, immediate results will be displayed. As a result, the user can instantly improve their posture if they complete their exercises inaccurately, by viewing the correctness of their performance in real-time.
Copyright: © 2023 The Author(s)
Open Access: Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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Volume Title: Proceedings of the Second International Conference on Emerging Trends in Engineering (ICETE 2023)
Series: Advances in Engineering Research
Publication Date: 9 November 2023
ISBN: 10.2991/978-94-6463-252-1_50
ISSN: 2352-5401
DOI: 10.2991/978-94-6463-252-1_50 How to use a DOI?
Open Access: Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Cite this article

ris enw bib

TY  - CONF
AU  - Sudhir Gaikwad
AU  - Shripad Bhatlawande
AU  - Atharva Dusane
AU  - Dyuti Bobby
AU  - Krushna Durole
AU  - Swati Shilaskar
PY  - 2023
DA  - 2023/11/09
TI  - Vision-Based Posture Detection for Rehabilitation Program
BT  - Proceedings of the Second International Conference on Emerging Trends in Engineering (ICETE 2023)
PB  - Atlantis Press
SP  - 473
EP  - 483
SN  - 2352-5401
UR  - https://doi.org/10.2991/978-94-6463-252-1_50
DO  - 10.2991/978-94-6463-252-1_50
ID  - Gaikwad2023
ER  -

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