The aim of the study was to perform a preliminary validation of a low cost markerless motion capture system (CAPTURE) against an industry gold standard (Vicon). Measurements of knee valgus and flexion during the performance of a countermovement jump (CMJ) between CAPTURE and Vicon were compared. After correction algorithms were applied to the raw CAPTURE data acceptable levels of accuracy and precision were achieved. The knee flexion angle measured for three trials using Capture deviated by ?3.8° ± 3° (left) and 1.7° ± 2.8° (right) compared to Vicon. The findings suggest that low-cost markerless motion capture has potential to provide an objective method for assessing lower limb jump and landing mechanics in an applied sports setting. Furthermore, the outcome of the study warrants the need for future research to examine more fully the potential implications of the use of low-cost markerless motion capture in the evaluation of dynamic movement for injury prevention.
Understanding the developmental levels of fundamental movement skills has a critical role in the improvement of motor competence in childhood. In this respect, the use of Microsoft Kinect to assess vertical jumping skill and to predict developmental levels in 9- to 12-yr.-old children was evaluated. 41 boys and girls repeated the countermovement jump test three times.
Vertical jumping skill levels were categorized using observational records, while kinematic and temporal parameters were estimated using a biomechanical model based on data acquired by the Kinect. Multivariate analysis of variance (MANOVA) and discriminant analysis verified that the height of the jump and the flight height predict the primary differences in jumping skill developmental levels, and the Kinect-based assessment discriminates these levels.
Markerless motion capture may have the potential to make motion capture technology widely clinically practical. However, the ability of a single markerless camera system to quantify clinically relevant, lower extremity joint angles has not been studied in vivo.
Therefore, the goal of this study was to compare in vivo joint angles calculated using a marker-based motion capture system and a Microsoft Kinect during a squat. Fifteen individuals participated in the study: 8 male, 7 female, height 1.702±0.089m, mass 67.9±10.4kg, age 24±4 years, BMI 23.4±2.2kg/m(2). Marker trajectories and Kinect depth map data of the leg were collected while each subject performed a slow squat motion. Custom code was used to export virtual marker trajectories for the Kinect data.
Each set of marker trajectories was utilized to calculate Cardan knee and hip angles. The patterns of motion were similar between systems with average absolute differences of <5 deg. Peak joint angles showed high between-trial reliability with ICC>0.9 for both systems.
The peak angles calculated by the marker-based and Kinect systems were largely correlated (r>0.55). These results suggest the data from the Kinect can be post processed in way that it may be a feasible markerless motion capture system that can be used in the clinic.
Microsoft's Kinect for Xbox 360 virtual reality (VR) video games are promising rehabilitation options because they involve motivating, full-body movement practice. However, these games were designed for recreational use, which creates challenges for clinical implementation.
Busy clinicians require decision-making support to inform game selection and implementation that address individual therapeutic goals. This article describes the development and preliminary evaluation of a knowledge translation (KT) resource to support clinical decision making about selection and use of Kinect games in physical therapy. The knowledge-to-action framework guided the development of the Kinecting With Clinicians (KWiC) resource. Five physical therapists with VR and video game expertise analyzed the Kinect Adventure games. A consensus-building method was used to arrive at categories to organize clinically relevant attributes guiding game selection and game play.
The process and results of an exploratory usability evaluation of the KWiC resource by clinicians through interviews and focus groups at 4 clinical sites is described. Subsequent steps in the evaluation and KT process are proposed, including making the KWiC resource Web-based and evaluating the utility of the online resource in clinical practice.
The Microsoft Kinect V2 for Windows, also known as the Xbox One Kinect, includes new and potentially far improved depth and image sensors which may increase its accuracy for assessing postural control and balance. The aim of this study was to assess the concurrent validity and reliability of kinematic data recorded using a marker-based three dimensional motion analysis (3DMA) system and the Kinect V2 during a variety of static and dynamic balance assessments.
Thirty healthy adults performed two sessions, separated by one week, consisting of static standing balance tests under different visual (eyes open vs. closed) and supportive (single limb vs. double limb) conditions, and dynamic balance tests consisting of forward and lateral reach and an assessment of limits of stability. Marker coordinate and joint angle data were concurrently recorded using the Kinect V2 skeletal tracking algorithm and the 3DMA system. Task-specific outcome measures from each system on Day 1 and 2 were compared. Concurrent validity of trunk angle data during the dynamic tasks and anterior-posterior range and path length in the static balance tasks was excellent (Pearson's r>0.75). In contrast, concurrent validity for medial-lateral range and path length was poor to modest for all trials except single leg eyes closed balance.
Within device test-retest reliability was variable; however, the results were generally comparable between devices. In conclusion, the Kinect V2 has the potential to be used as a reliable and valid tool for the assessment of some aspects of balance performance.
Treadmill walking is commonly used to analyze several gait cycles in a limited space. Depth cameras, such as the low-cost and easy-to-use Kinect sensor, look promising for gait analysis on a treadmill for routine outpatient clinics.
However, gait analysis is based on accurately detecting gait events (such as heel-strike) by tracking the feet which may be incorrectly recognized with Kinect. Indeed depth images could lead to confusion between the ground and the feet around the contact phase. To tackle this problem we assume that ...
Normalisation of gait data is routine practice to reduce inter-subject variability. Although non-dimensional (N-D) normalisation of data is thought as the most appropriate technique to normalise gait data there have been very few attempts to test this assumption.
This paper describes an investigation that has been completed to assess the effectiveness of N-D normalisation technique.
Researchers have identified high exposure to game conditions, low back dysfunction, and poor endurance of the core musculature as strong predictors for the occurrence of sprains and strains among collegiate football players.
To refine a previously developed injury-prediction model through analysis of 3 consecutive seasons of data.
National Collegiate Athletic Association Division I Football Championship Subdivision football program.
PATIENTS OR OTHER PARTICIPANTS:
For 3 consecutive years, all 152 team members (age = 19.7 ± 1.5 years, height = 1.84 ± 0.08 m, mass = 101.08 ± 19.28 kg) presented for a mandatory physical examination on the day before initiation of preseason practice sessions.
MAIN OUTCOME MEASURE(S):
Associations between preseason measurements and the subsequent occurrence of a core or lower extremity sprain or strain were established for 256 player-seasons of data. We used receiver operating characteristic analysis to identify optimal cut points for dichotomous categorizations of cases as high risk or low risk. Both logistic regression and Cox regression analyses were used to identify a multivariable injury-prediction model with optimal discriminatory power.
Exceptionally good discrimination between injured and uninjured cases was found for a 3-factor prediction model that included equal to or greater than 1 game as a starter, Oswestry Disability Index score equal to or greater than 4, and poor wall-sit-hold performance. The existence of at least 2 of the 3 risk factors demonstrated 56% sensitivity, 80% specificity, an odds ratio of 5.28 (90% confidence interval = 3.31, 8.44), and a hazard ratio of 2.97 (90% confidence interval = 2.14, 4.12).
High exposure to game conditions was the dominant injury risk factor for collegiate football players, but a surprisingly mild degree of low back dysfunction and poor core-muscle endurance appeared to be important modifiable risk factors that should be identified and addressed before participation.