Hand Gestures Identification for Fine-Grained Human Activity Recognition in Smart Homes

Abstract

Most developed countries are facing important demographic issues related to ageing populations. Maintaining elders at home while ensuring their safety and well-being often constitutes the main goal of these countries. An interesting solution to this challenge is to develop a smart home, able to monitor the routines of the resident, to recognize the on-going activities, and to provide support when required. In the literature, most works focus on monitoring high-level behaviors such as eating, sleeping, etc. However, to provide live guidance, the system needs to have a far more detailed recognition process able to identify the specific steps of the on-going task and the erroneous executions. In this paper, we propose an algorithmic approach for hand gesture recognition designed to be used as the core component of a fine-grained activity recognition model. It is based on the processing of inertial data collected from a wristband equipped with triaxial accelerometer and gyroscope, and machine learning techniques. A simple set of gestures for cooking activities as been defined, enabling characterizing high-level cooking tasks. To do that, we constructed a labelled dataset of atomic gestures performed by participants that we made available to the scientific community. We obtained an average accuracy of 0.83 in recognizing the gestures with the leave-one-subject-out strategy.