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

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.

Artificial neural network can improve the accuracy of a markerless skeletal model in L5/S1 position estimation during symmetric lifting.

This study investigated whether using an artificial neural network (ANN) method for L5/S1 position estimation based on the Kinect markerless skeletal model can produce more accurate data than measurements using the original Kinect skeletal model during symmetric lifting tasks. Twenty participants performed three symmetric lifting tasks twice at three vertical lifting height paths. Their postural data were simultaneously collected by a Kinect and a reference motion tracking system (MTS). The Kinect-based data are used as the model inputs, while its outputs are based on MTS. Three-layer ANN models to predict the L5/S1 position over the entire lifting duration were trained by identifying the relationship between the seven inputs (the participant's height and weight and the Kinect-based trunk angle, left knee angle, and left hip joint coordinates on the X-axis, Y-axis, and Z-axis) and three outputs (the reference L5/S1 position on the X-axis, Y-axis, and Z-axis). As a measure of error, the distances between the reference anatomical L5/S1 position and the predicted positions (by the ANN-Kinect system and the Kinect system) were calculated and compared. The results showed that introducing the ANN method can significantly (p < 0.0001) reduce the L5/S1 position estimation error (5.12 ± 1.83 cm) in comparison with directly using the original data output from the skeletal model driven by Kinect data (20.54 ± 3.24 cm). This method provides an alternative for L5/S1 position estimation while retaining the advantages of using Kinect such as portability, easy of use, and being equipped with the function of automatic skeletal identification.

Simple benchmarking method for determining the accuracy of depth cameras in body landmark location estimation: Static upright posture as a measurement example.

To evaluate the postures in ergonomics applications, studies have proposed the use of low-cost, marker-less, and portable depth camera-based motion tracking systems (DCMTSs) as a potential alternative to conventional marker-based motion tracking systems (MMTSs). However, a simple but systematic method for examining the estimation errors of various DCMTSs is lacking. This paper proposes a benchmarking method for assessing the estimation accuracy of depth cameras for full-body landmark location estimation. A novel alignment board was fabricated to align the coordinate systems of the DCMTSs and MMTSs. The data from an MMTS were used as a reference to quantify the error of using a DCMTS to identify target locations in a 3-D space. To demonstrate the proposed method, the full-body landmark location tracking errors were evaluated for a static upright posture using two different DCMTSs. For each landmark, we compared each DCMTS (Kinect system and RealSense system) with an MMTS by calculating the Euclidean distances between symmetrical landmarks. The evaluation trials were performed twice. The agreement between the tracking errors of the two evaluation trials was assessed using intraclass correlation coefficient (ICC). The results indicate that the proposed method can effectively assess the tracking performance of DCMTSs. The average errors (standard deviation) for the Kinect system and RealSense system were 2.80 (1.03) cm and 5.14 (1.49) cm, respectively. The highest average error values were observed in the depth orientation for both DCMTSs. The proposed method achieved high reliability with ICCs of 0.97 and 0.92 for the Kinect system and RealSense system, respectively.