TY - GEN
T1 - IoT System Based on Deep Learning for the Identification and Feedback of Work Postures When Using a Computer
AU - Caballero-Lara, Eduardo
AU - Camargo-Ramirez, Enzo
AU - Ugarte, Willy
N1 - Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2026.
PY - 2026
Y1 - 2026
N2 - It is common for office workers, mostly dedicated to IT, to present musculoskeletal pain in the back, neck and shoulders due to poor posture practices they adopt while doing their work in front of the computer for long periods, this is known as forced postures. Our main work seeks to implement an IoT system with force sensors, model RP-S40-ST, based on the use of classification algorithms and deep learning techniques for the identification and correction of postures through feedback. Ten classification algorithms were used for training and validation of the model, with the Logistic Regression algorithm achieving the highest accuracy rate being .8794 and .9052 respectively.
AB - It is common for office workers, mostly dedicated to IT, to present musculoskeletal pain in the back, neck and shoulders due to poor posture practices they adopt while doing their work in front of the computer for long periods, this is known as forced postures. Our main work seeks to implement an IoT system with force sensors, model RP-S40-ST, based on the use of classification algorithms and deep learning techniques for the identification and correction of postures through feedback. Ten classification algorithms were used for training and validation of the model, with the Logistic Regression algorithm achieving the highest accuracy rate being .8794 and .9052 respectively.
KW - ergonomics
KW - feedback
KW - lumbar
KW - musculoskeletal
KW - postures
UR - https://www.scopus.com/pages/publications/105011355780
U2 - 10.1007/978-981-96-8892-0_20
DO - 10.1007/978-981-96-8892-0_20
M3 - Contribución a la conferencia
AN - SCOPUS:105011355780
SN - 9789819688913
T3 - Lecture Notes in Computer Science
SP - 231
EP - 243
BT - Advances and Trends in Artificial Intelligence. Theory and Applications - 38th International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2025, Proceedings
A2 - Fujita, Hamido
A2 - Watanobe, Yutaka
A2 - Ali, Moonis
A2 - Wang, Yinglin
PB - Springer Science and Business Media Deutschland GmbH
T2 - 38th International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE2025
Y2 - 1 July 2025 through 4 July 2025
ER -