NOTICE OF 2nd PhD SEMINAR PRESENTATION
CANDIDATE: Victor Eghuvjobo
DEGREE SOUGHT: PhD
DATE: 3/26/2025
TIME: 3:00pm
LOCATION: Room 2101 CEI
TITLE: Posture Recognition, Classification and Adjustment of an office worker based on pressure sensors: A comprehensive review
Abstract
Incorrect sitting posture, characterized by an uneven positioning of the body, often leads to imbalance in the spin. However prolonged sedentary behaviors can adversely lead to the development of spinal deformities and musculoskeletal disorders. Smart chairs equipped with cutting-edge sensor technologies have been introduced as a viable solution for the real-time detection, classification, and monitoring of sitting postures of office workers, aiming to mitigate the risk of musculoskeletal disorders and promote overall good health. This comprehensive literature review evaluates the current body of research on smart chairs, with a specific focus on the strategies used for posture detection and classification and feedback monitoring with the effectiveness of different pressure sensors. Searches in MDPI, IEEE, Google Scholar, Scopus, and PubMed databases yielded several studies that utilized non-invasive methods for posture monitoring. The analysis revealed that Force Sensing Resistors (FSRs) are the predominant sensors utilized for posture detection, whereas Convolutional Neural Networks (CNNs) and Artificial Neural Networks (ANNs) 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 the 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.
All Graduate Students are invited to attend