Human activity recognition based on smart chair

Abstract

We present a smart chair that can detect and classify some common daily activities of elderly people. The chair has the potential to be a huge source of information on the behaviors of people since most indoor activities are performed in sedentary positions. The proposed smart chair comprises six pressure sensors mounted in a chair, together with a Raspberry Pi to collect raw data. The mounted pressure sensors collect signals and transmit them to a server for processing and analysis while the user sits in the chair. Five different activities are detected and classified by these sensors: working at the desk, eating, napping, coughing, and watching TV. In an effort to achieve the best classification of these activities, three different machine learning algorithms are employed and their accuracy scores were compared. These algorithms are the random forest (RF), extremely randomized trees (ERTs), and support vector machine (SVM). The experimental results have proven the ERT to be the best classifier in this survey, since it yielded a classification accuracy above 98% over the testing data.