A novel approach for human activity recognition using object interactions and machine learning

Recognising human activity can be advantageous in a number of different scenarios including elder care, healthcare or for training purposes. It can be of direct use to support humans in doing different activities, but is still a challenge for systems to correctly classify the activity in a way that is valuable for the user, as they often times lack the robustness or simplicity for day-to-day use. In this paper an approach for human activity recognition based on object interactions is presented. The proposed system consists of a wireless sensor network, with each sensor node measuring the received signal strength indication (RSSI) to its neighbouring nodes. The accumulated RSSI data is then analyzed by a machine learning algorithm which tries to infer one of several cooked dishes from that data. Experimental studies demonstrate promising results and therefore potential for this technology for recognising human activity in the form of cooking, but its generalised approach makes it suitable for other environments, too.