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Static body sway is a clinically relevant activity parameter, used to assess postural balance, across a wide spectrum of patient populations. We have examined static body sway using two different segmental total body center of mass (TBCM) estimation methods, the Generator of Body Data III (GEBOD) and Winter's method, using Microsoft Kinect skeletal data. Twenty subjects were recruited through an IRB study and asked to perform three trials of single leg stance with their eyes closed, with positioning based on the Balance Error Scoring System. A force plate system was used to estimate the ground truth data for comparison. Results show that both GEBOD and Winter's method performed similar in estimating anterior-posterior (AP) and medio-lateral (ML) body sway. The results also show highly correlated measurements by the two TBCM estimation methods when compared with the force plate system (mean RMSE value of 10.18 mm square in AP and 8.00 mm square in ML direction). Ordinary Least Square (OLS) linear regressions were performed to improve body sway results obtained from the two methods. Improved sway range values obtained from the simple regression method was able to reduce the estimation errors by 50% (~ 10 mm in both AP and ML body sway). The two static body sway estimation methods were found reliable for obtaining body sway. Thus, the inexpensive, portable Kinect V2.0 can be used for clinical measurements.

Objectives: Biomechanical factors such as dynamic knee valgus predispose young athletes to lower extremity injury including tears of the anterior cruciate ligament (ACL). Identifying these risk factors may allow for targeted injury prevention strategies. Our prior work has validated the Microsoft Kinect vs. Vicon to detect knee-ankle separation ratio (KASR) during the drop vertical jump test (DVJ). Our hypothesis is that screening with the Microsoft Kinect will be safe, efficient, and provide information to help detect injury risk in youth athletes. Methods: A total of 180 healthy high school athletes, ages 14-18 (80 males and 100 females, age of 16.9 ± 1.31 and BMI of 22.8 ± 3.7) participated in this study. Each subject performed three successful DVJ (Fig. 1). We used an inexpensive, portable motion sensor device to measure the KASR, which captures the ratio of the horizontal distance between knees to the horizontal distance between ankles. From previous studies, a KASR value Results: Using two motion sensor device stations, it took 1.5 minutes to screen and test each subject. There were no injuries that occurred during the screening process. Our results showed a statistically significant difference between male and female KASR for both initial contact (pConclusion: Our findings suggest that a portable and inexpensive motion analysis device can detect dynamic valgus during the DVJ in youth athletes. Large scale screening for dynamic valgus was safe and efficient. Known gender disparities between male and females for neuromuscular imbalances were identified. Potential use of this information for targeted injury prevention is appealing and requires further investigation.

We describe a feasibility study in which the Microsoft Kinect is used for a game-based exercise to strengthen posterior chain muscles which are often weak in those at high risk of anterior cruciate ligament (ACL) injury. In the game, subjects perform a single posterior chain strengthening exercise. The game uses a side-scrolling video display driven by a hip abduction exercise while a player lies down on the floor. Leg lifts beyond a predetermined angle trigger the jumping action of an animated tiger. We describe the scene and game control, which uses depth images from the Kinect. Although Kinect-based skeletal data are used for many games, the skeletal model does not yield good estimates for positions on the floor. Our proposed system uses multiple leg angle estimators for different angle regions to recognize the player lying down and capture the angle between two legs. We conducted an experiment that validates our system with marker-based Vicon ground truth data. We also present results of an end-to-end test using the game, showing feasibility.

Pianists who practice hours per day may have a risk for developing playing-related musculoskeletal injuries if they do not play with proper hand alignment. In order to detect the harmful, misaligned hand postures (such as wrist flexion and extension, knuckle collapse, and ulnar and radial deviation) and analyze the injury risk, a motion capture system was developed using the Microsoft Kinect depth camera. Data were captured on professional pianists and student pianists from the School of Music, University of Missouri. Features extracted from the 3D point clouds reconstructed from the Kinect depth images are used for hand posture evaluation. Results are included for different hand postures of participating pianists.