2018 | OriginalPaper | Buchkapitel
Feet Fidgeting Detection Based on Accelerometers Using Decision Tree Learning and Gradient Boosting
Autoren: Julien Esseiva, Maurizio Caon, Elena Mugellini, Omar Abou Khaled, Kamiar Aminian
Erschienen in:
Bioinformatics and Biomedical Engineering
Abstract
Detection of fidgeting activities is a field which has not been much explored as of now. Studies have shown that fidgeting has a beneficial impact on people’s healthiness as it burns a significant amount of energy. Being able to detect when someone is fidgeting would allow to study more closely the health impact of fidgeting. The purpose of this work is to propose an algorithm being able to detect feet fidgeting period of subjects while sitting using 3-D accelerometers on both shoes. Initial results on data from 5 subjects collected during this work shows an accuracy of 95% for a classification between sitting with fidgeting and sitting without fidgeting.
