Human Activity Monitoring With Wearable Sensors and Hybrid Classifiers
Activity monitoring plays a crucial role in ambient living environments for assessing changes in the normal behavioral pattern of elderly people. In this paper, the authors present an action description and detection mechanism for real-time activity monitoring using wearable sensors and hybrid classifiers. First a Single Sensor Single Classifier model is presented (SSSC) for the detection of simple and composite actions. Then the model is enhanced with multiple sensors and classifiers for the purpose of real-time monitoring. The enhanced Multi-Sensor Multi Classifier (MSMC) model uses two wearable TI Chronos watches with a built-in tri-axial accelerometer for data acquisition and a composition of naive Bayes, Susan Corner Detector (SCD) and Hidden Markov (HMM) classifiers for the detection of transitions between defined actions in real-time.