Fall Detection and Monitoring using Machine Learning: A Comparative Study

Abstract

The detection of falls has emerged as an important topic for the public to discuss because of the prevalence and severity of unintentional falls, particularly among the elderly. A Fall Detection System, known as an FDS, is a system that gathers data from wearable Internet-of-Things (IoT) device and classifies the outcomes to distinguish falls from other activities and call for prompt medical aid in the event of a fall. In this paper, we determine either fall or not fall using machine learning prior to our collected fall dataset from accelerometer sensor. From the acceleration data, the input features are extracted and deployed to supervised machine learning (ML) algorithms namely, Support Vector Machine (SVM), Decision Tree, and Naive Bayes. The results show that the accuracy of fall detection reaches 95%, 97 % and 91% without any false alarms for the SVM, Decision Tree, and Naïve Bayes, respectively.