Smartphone-Based Human Sitting Behaviors Recognition Using Inertial Sensor

TytułSmartphone-Based Human Sitting Behaviors Recognition Using Inertial Sensor
Publication TypeJournal Article
Rok publikacji2021
AutorzySinha VKumar, Patro KKumar, Plawiak P, Prakash AJaya
JournalMDPI, Sensors
Start Page6652
Date Published10/2021
Słowa kluczoweaccelerometer, an inertial sensor, CFS, classifiers, gyroscope, human sitting behaviors, magnetometer, PSO, smartphone

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.


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Data aktualizacji: 14/11/2021 - 21:56; autor zmian: (