Human Daily Activity Recognition for Healthcare Using Wearable and Visual Sensing Data

Wearable digital self-tracking technologies for monitoring individuals' health condition have become more accessible to the public in recent years with the development of connected portable devices, such as smart phones, smart watches, smart bands, and other personal biometric monitoring devices. Mining behavioural patterns from such wearable data along with other available sensory data, has the potential to offer an objective, insightful service in clinical professionals and healthcare. For example, accurate identification of human activities could help us provide a better patient recovery training guidance, or an early alarm of emergency that may happen to elder people, such as stroke, falls, etc. In this paper, we introduce an activity recognition system, which learns a nonlinear SVM algorithm to identify 20 different human activities from accelerometer and RGB-D camera data. Our early experimental results show that the proposed approach is promising and effective.