Human Behavior and Anomaly Detection using Machine Learning and Wearable Sensors

Abstract

This paper addresses the problem of detecting and analyzing human behavior using a set of non-privacy invasive wearable sensors aiming to identify potential anomalies. This may be an important tool for increasing the independence and delaying the institutionalization of older adults allowing them to live alone in their homes with little support from caregivers. We propose an experimental web-based distributed system that incorporates data from wearable sensors and machine learning-based algorithms for monitoring the person's behavior and detection of anomalies. Various configurations of feature selection techniques and features as well as manual labeling for supervised learning have been used. In case of anomalies detected in older adult behavior, the caregiver is notified. Finally, we illustrate the system implementation and functionality considering Fitbit smart band sensor and integration with Fitbit Cloud. The results obtained using a public activity dataset with different configurations of machine learning anomaly detection algorithms and features are promising, showing an accuracy of 87% and an F1-score of 0.9.