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

Abstract

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.

Learning from sensory predictions for autonomous and adaptive exploration of object shape with a tactile robot

“…Gaussian Processes (GP), which are a probabilistic formulation, have been used for autonomous active exploration and recognition of objects using biomimetic tactile sensors, force sensors and geometrical information [22,32]. In general, probabilistic approaches have demonstrated to be suitable for autonomous robotics, providing flexibility and robustness to deal with sensor limitations, noise and uncertainty observed in the changing environment [7,33,34].…”

Assessment of Sit-to-Stand Transfers during Daily Life Using an Accelerometer on the Lower Back

“…However, the poor wearability and high patient burden associated with the use of these multi-sensor systems make them ill-suited for long-term monitoring in free-living conditions and limits their application in clinical research. To address these limitations, several attempts have been made to use a single IMU located somewhere on the torso (typical locations: chest, waist, lower back) [ 19 , 20 , 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 ] or thigh/upper leg [ 28 , 29 ] for the detection and assessment of STS transfers. While the thigh-based approaches can be relatively robust because STS transfers can be detected by simply tracking orientation using an accelerometer, this device location is not ideal for measuring other aspects of mobility such as gait and balance.…”

Automatic and Real-Time Computation of the 30-Seconds Chair-Stand Test without Professional Supervision for Community-Dwelling Older Adults

“…Measurements of the thigh angle from a single device have already been used in previous literature to study SiSt and StSi transitions, mostly to identify different postures and activities (sitting, standing, walking, ramp or stair ascending, etc.) while performing activities of daily living (either in controlled lab settings or in free-living conditions) [ 34 , 35 , 36 , 37 ]. However, we have not found any descriptions of an instrumented version of the 30-s CST based on this approach.…”