TY - GEN
T1 - A Noninvasive Smart Chair System for Monitoring Postures in Sedentary Workers
AU - Sigcha, Luis
AU - Pereira, Eduarda
AU - Lima, Ana
AU - Antunes, Joao Tiago
AU - Carvalhais, Diana
AU - Sousa, Diogo
AU - Abreu, Abdulay
AU - Costa, Nelson
AU - Cardoso, Paulo
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - Nowadays, service-providing employment accounts for about 80% of jobs [1], most of which require low physical activity. This sedentary behavior is carried out by workers who are mainly seated for long periods and usually also includes postural behavior that can cause pain and chronic injuries. To increase worker well-being, in addition to encouraging and promoting physical activities, posture monitoring can be helpful in identifying wrong behaviors, leading to less postural stress and musculoskeletal injuries. This paper presents a non-intrusive posture monitoring system, based on a matrix of pressure sensors inserted on a chair cushion, where the worker is seated. The gathered data is then subject to a machine learning algorithm that can infer favorable/unfavorable postures. The results lead to more than 80% accuracy, which means that this setup can be helpful in informing users of their postures that lead to their correction, thus helping to mitigate pain and injuries.
AB - Nowadays, service-providing employment accounts for about 80% of jobs [1], most of which require low physical activity. This sedentary behavior is carried out by workers who are mainly seated for long periods and usually also includes postural behavior that can cause pain and chronic injuries. To increase worker well-being, in addition to encouraging and promoting physical activities, posture monitoring can be helpful in identifying wrong behaviors, leading to less postural stress and musculoskeletal injuries. This paper presents a non-intrusive posture monitoring system, based on a matrix of pressure sensors inserted on a chair cushion, where the worker is seated. The gathered data is then subject to a machine learning algorithm that can infer favorable/unfavorable postures. The results lead to more than 80% accuracy, which means that this setup can be helpful in informing users of their postures that lead to their correction, thus helping to mitigate pain and injuries.
KW - machine learning
KW - musculoskeletal disorders
KW - posture recognition
UR - http://www.scopus.com/inward/record.url?scp=85172092963&partnerID=8YFLogxK
U2 - 10.1109/ISIE51358.2023.10228048
DO - 10.1109/ISIE51358.2023.10228048
M3 - Conference contribution
AN - SCOPUS:85172092963
T3 - IEEE International Symposium on Industrial Electronics
BT - 2023 IEEE 32nd International Symposium on Industrial Electronics, ISIE 2023 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 32nd IEEE International Symposium on Industrial Electronics, ISIE 2023
Y2 - 19 June 2023 through 21 June 2023
ER -