01675nas a2200181   4500000000100000000000100001008004100002260001200043100001600055700001600071700001800087700001800105245007900123856004600202490000700248520122400255022001401479       2021                            d  c10/20211 aVikas Sinha1 aKiran Patro1 aPawel Plawiak1 aAllam Prakash00aSmartphone-Based Human Sitting Behaviors Recognition Using Inertial Sensor  uhttps://www.mdpi.com/1424-8220/21/19/66520 v213 a<p>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.</p>
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