Posture Prediction for Healthy Sitting using a Smart Chair. (arXiv:2201.02615v1 [cs.LG])

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 Na”ive 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.



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