A Simple Method to Optimally Select Upper-Limb Joint Angle Trajectories from Two Kinect Sensors during the Twisting Task for Posture Analysis

Abstract

A trunk-twisting posture is strongly associated with physical discomfort. Measurement of joint kinematics to assess physical exposure to injuries is important. However, using a single Kinect sensor to track the upper-limb joint angle trajectories during twisting tasks in the workplace is challenging due to sensor view occlusions. This study provides and validates a simple method to optimally select the upper-limb joint angle data from two Kinect sensors at different viewing angles during the twisting task, so the errors of trajectory estimation can be improved. Twelve healthy participants performed a rightward twisting task. The tracking errors of the upper-limb joint angle trajectories of two Kinect sensors during the twisting task were estimated based on concurrent data collected using a conventional motion tracking system. The error values were applied to generate the error trendlines of two Kinect sensors using third-order polynomial regressions. The intersections between two error trendlines were used to define the optimal data selection points for data integration. The finding indicates that integrating the outputs from two Kinect sensor datasets using the proposed method can be more robust than using a single sensor for upper-limb joint angle trajectory estimations during the twisting task.

A study on the Real-Time Biomechanical Analysis of Lumbar Burden Utilizing Stereoscope Cameras

“…Therefore, many researchers are trying to monitor and improve body movements with various approaches. Among these methods, the approach that is often to be seen is using smartphones and Kinect sensors [4], [5], [6], [7], [8]. The approaches have already taken their respective advantages.…”

“…This change in body inclination will then be used to estimate the lumbar burden that occurs on the body and classify the movements the body makes [5]. On the other hand, the Kinect sensor [6][7][8] uses a camera to capture human images and a depth sensor to measure the depth of key-points captured by the camera. This data will then be used to obtain 3-dimensional body key points.…”