Abstract:
currently, with the growth of smart sensing technologies in ubiquitous computing.
Human activity recognition (HAR) is becoming a fundamental research problem.
HAR aims to recognize the person’s body position, motion, and function with
camera and sensors-based systems. Even though camera based HAR gain much
progress but due to certain privacy concerns researchers focus with cost-effective
sensor-based miniatures for HAR. Because it can play a vital role in aging care,
smart homes, and daily life assistant Apps. As the human activities bring a lot of
information about context that can help models to accomplish context-awareness.
The precise acknowledgement of inthewild human activities and the contexts
related with these activities remains an open research challenge that needs to be
addressed. In this work, the aim is to present a context aware human activity
recognition (CAHAR) scheme to learn the variability of human behavior context in
the wild with physical activity recognition. Deep neural networks and Machine
Learning (ML) algorithms opted to get behavioral context of a person in the designed
scheme of CAHAR and use different machine learning classifier for comparison
with the presented scheme.
