Version 1
: Received: 27 March 2024 / Approved: 27 March 2024 / Online: 28 March 2024 (08:29:10 CET)
How to cite:
Odesola, D.; Kulon, J.; Verghese, S.; Partlow, A.; Gibson, C. Smart-Sensing Chairs for Sitting Posture Detection, Classification and Monitoring: A Comprehensive Review. Preprints2024, 2024031695. https://doi.org/10.20944/preprints202403.1695.v1
Odesola, D.; Kulon, J.; Verghese, S.; Partlow, A.; Gibson, C. Smart-Sensing Chairs for Sitting Posture Detection, Classification and Monitoring: A Comprehensive Review. Preprints 2024, 2024031695. https://doi.org/10.20944/preprints202403.1695.v1
Odesola, D.; Kulon, J.; Verghese, S.; Partlow, A.; Gibson, C. Smart-Sensing Chairs for Sitting Posture Detection, Classification and Monitoring: A Comprehensive Review. Preprints2024, 2024031695. https://doi.org/10.20944/preprints202403.1695.v1
APA Style
Odesola, D., Kulon, J., Verghese, S., Partlow, A., & Gibson, C. (2024). Smart-Sensing Chairs for Sitting Posture Detection, Classification and Monitoring: A Comprehensive Review. Preprints. https://doi.org/10.20944/preprints202403.1695.v1
Chicago/Turabian Style
Odesola, D., Adam Partlow and Colin Gibson. 2024 "Smart-Sensing Chairs for Sitting Posture Detection, Classification and Monitoring: A Comprehensive Review" Preprints. https://doi.org/10.20944/preprints202403.1695.v1
Abstract
Incorrect sitting posture, characterized by asymmetrical or uneven positioning of the body, often leads to spinal misalignment and muscle tone imbalance. Prolonged maintenance of such postures can adversely impact well-being and contribute to the development of spinal deformities and musculoskeletal disorders. In response, smart-sensing chairs equipped with cutting-edge sensor technologies have been introduced as a viable solution for real-time detection, classification, and monitoring of sitting postures, aiming to mitigate the risk of musculoskeletal disorders and promote overall health. This comprehensive literature review evaluates the current body of research on smart-sensing chairs, with a specific focus on the strategies used for posture detection and classification, as well as the effectiveness of different sensor technologies. A meticulous search across MDPI, IEEE, and Google Scholar databases yielded 34 pertinent studies that utilize non-invasive methods for posture monitoring. The analysis reveals that Force Sensing Resistors (FSR) are the predominant sensors utilized for posture detection, whereas Convolutional Neural Networks (CNN) and Artificial Neural Networks (ANN) are the leading machine learning models for posture classification. However, it was observed that CNNs and ANNs do not outperform traditional statistical models in terms of classification accuracy, due to constrained size and lack of diversity within training datasets. These datasets often fail to comprehensively represent the array of human body shapes and musculoskeletal configurations. Moreover, this review identifies a significant gap in the evaluation of user feedback mechanisms, essential for alerting users to their sitting posture and facilitating corrective adjustments.
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
