Probabilistic identification of sit-to-stand and stand-to-sit with a wearable sensor

Abstract

• High-level probabilistic identification of sit-to-stand. • High-level probabilistic tracking of the human body during the transition state. • Adaptable method for identification of sit-to-stand using one wearable sensor. • Autonomous decisions and actions for control of assistive robots. • Interaction of high- and low-level methods for control of assistive robots. Identification of human movements is crucial for the design of intelligent devices capable to provide assistance. In this work, a Bayesian formulation, together with a sequential analysis method, is presented for identification of sit-to-stand (SiSt) and stand-to-sit (StSi) activities. This method performs autonomous iterative accumulation of sensor measurements and decision-making processes, while dealing with noise and uncertainty present in sensors. First, the Bayesian formulation is able to identify sit, transition and stand activity states. Second, the transition state, divided into transition phases, is used to identify the state of the human body during SiSt and StSi. These processes employ acceleration signals from an inertial measurement unit attached to the thigh of participants. Validation of our method with experiments in offline, real-time and a simulated environment, shows its capability to identify the human body during SiSt and StSi with an accuracy of 100% and mean response time of 50 ms (5 sensor measurements). In the simulated environment, our approach shows its potential to interact with low-level methods required for robot control . Overall, this work offers a robust framework for intelligent and autonomous systems , capable to recognise the human intent to rise from and sit on a chair, which is essential to provide accurate and fast assistance.