Abstract
At present, people spend most of their time in passive rather than active mode. Sitting with computers for a long time may lead to unhealthy conditions like shoulder pain, numbness, headache, etc. To overcome this problem, human posture should be changed for particular intervals of time. This paper deals with using an inertial sensor built in the smartphone and can be used to overcome the unhealthy human sitting behaviors (HSBs) of the office worker. To monitor, six volunteers are considered within the age band of 26 ± 3 years, out of which four were male and two were female. Here, the inertial sensor is attached to the rear upper trunk of the body, and a dataset is generated for five different activities performed by the subjects while sitting in the chair in the office. Correlation-based feature selection (CFS) technique and particle swarm optimization (PSO) methods are jointly used to select feature vectors. The optimized features are fed to machine learning supervised classifiers such as naive Bayes, SVM, and KNN for recognition. Finally, the SVM classifier achieved 99.90% overall accuracy for different human sitting behaviors using an accelerometer, gyroscope, and magnetometer sensors.
Highlights
Accepted: 28 September 2021People spend their lives in three modes from childhood to old age: active, sedentary, and non-active
Correlation-based feature selection (CFS) and particle swarm optimization (PSO) are jointly utilized for feature selection among various extracted features to provide the highest performance of the applied classifiers
Five general sitting behaviors such as left movement, right movement, front movement, back movement, and straight movement of office workers were recognized by using the inertial sensor inbuilt in the smartphone with the help of machine learning classification techniques
Summary
People spend their lives in three modes from childhood to old age: active, sedentary, and non-active. In [4,5,6,7,8], a smart chair-based approach is applied to improve the sitting postures. The cushion-based smart chair combines pressure sensors and IMU to monitor the sitting behavior at the workplace, in the car, and wheelchair. In [6], a smart cushion system was implemented to monitor the sitting activities containing calibrated e-Textile sensors and developed a dynamic time warping-based algorithm to recognize the human sitting behaviors. The IMU sensor attached to the rearof trunk to track exact movement of the spine, which will is help recognize theupper postures the body [28].the. Sitting movement of the spine, which will help recognize the postures of the body [28].
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