State-of-the-art survey on activity recognition and classification using smartphones and wearable sensors

Activity Recognition and Classification (ARC) have enabled growth of many automated applications like recommender services, old age assistance, health monitoring, security and surveillance etc. This becomes possible due to advancement of technology in wearable devices and smartphones. The small size, easy availability of various relevant sensors, ever decreasing cost, ability to monitor continuously and handy to use features have made them prominent devices to use in ARC. In this work, we provide a comprehensive survey on ARC using smartphones and wearable sensors. The work begins with the understanding of the ARC process followed by description of inertial sensors present in the smartphones and wearables. It covers the various feature extraction methods and the models used in traditional methods and the trending deep learning based methods. It is observed that, performance of any ARC method largely depends on number of sensors, classification technique, kind of device and placement and orientation of the device among many other parameters considered in the work. In our study, we present a detailed comparison of work done in this area considering ten such important parameters, which, to the best of our knowledge, is the first of its kind of surveys. Finally, we present ten challenges in this area and provide prospective dimensions that can be explored in future research.