Research Article
Posture Prediction for Healthy Sitting Using a Smart Chair
@INPROCEEDINGS{10.1007/978-3-030-93709-6_26, author={Tariku Adane Gelaw and Misgina Tsighe Hagos}, title={Posture Prediction for Healthy Sitting Using a Smart Chair}, proceedings={Advances of Science and Technology. 9th EAI International Conference, ICAST 2021, Hybrid Event, Bahir Dar, Ethiopia, August 27--29, 2021, Proceedings, Part I}, proceedings_a={ICAST}, year={2022}, month={1}, keywords={Sitting posture Smart chair Pressure sensor Deep neural networks Prediction}, doi={10.1007/978-3-030-93709-6_26} }- Tariku Adane Gelaw
Misgina Tsighe Hagos
Year: 2022
Posture Prediction for Healthy Sitting Using a Smart Chair
ICAST
Springer
DOI: 10.1007/978-3-030-93709-6_26
Abstract
Poor sitting habits have been identified as a risk factor to musculoskeletal disorders and lower back pain especially on the elderly, disabled people, and office workers. In the current computerized world, even while involved in leisure or work activity, people tend to spend most of their days sitting at computer desks. This can result in spinal pain and related problems. Therefore, a means to remind people about their sitting habits and provide recommendations to counterbalance, such as physical exercise, is important. Posture recognition for seated postures have not received enough attention as most works focus on standing postures. Wearable sensors, pressure or force sensors, videos and images were used for posture recognition in the literature. The aim of this study is to build Machine Learning models for classifying sitting posture of a person by analyzing data collected from a chair platted with two 32 by 32 pressure sensors at its seat and backrest. Models were built using five algorithms: Random Forest (RF), Gaussian Naïve Bayes, Logistic Regression, Support Vector Machine and Deep Neural Network (DNN). All the models are evaluated using KFold cross validation technique. This paper presents experiments conducted using the two separate datasets, controlled and realistic, and discusses results achieved at classifying six sitting postures. Average classification accuracies of 98% and 97% were achieved on the controlled and realistic datasets, respectively.
