Three-dimensional motion capture analysis is considered the gold standard for any movement research. Motion capture data were recorded for 7 healthy female participants with no prior throwing experience to investigate the learning process for overarm throwing during a selected period. Participants were monitored 3 times a week for 5 weeks. Each session consisted of 15 dominant and 15 nondominant hand side overarm throws. A total of 3,150 trials were recorded and preprocessed (labeling reflective markers) for further analysis. The presented dataset can provide valuable information about upper extremity kinematics of the learning process of overarm throwing without any kind of feedback. Furthermore, this dataset may be used for more advanced analysis techniques, which could lead to more insightful information.

In this study, an inertial measurement unit (IMU) sensor module and software algorithm were developed to identify anomalous kicks that should not be given scores in Taekwondo competitions. The IMU sensor module was manufactured with dimensions of 3 cm × 3 cm × 1.5 cm and consists of a high-g sensor for high acceleration measurement, a 9-DOF sensor, and a Wi-Fi module for wireless communication. In the experiment, anomalous kicks and normal kicks were collected by the IMU sensor module, and an AI model was trained. The anomalous kick determination accuracy of the trained AI model was found to be 97.5%. In addition, in order to check whether the strength of a blow can be distinguished using the IMU sensor module, an impact test was performed with a pendulum under the same test conditions as the impact sensor installed in the impact test setup, and the correlation coefficient was 0.99. This study is expected to contribute to improving scoring reliability by suggesting the possibility of discriminating anomalous kicks, which were difficult to judge in Taekwondo competitions, through the analysis of Taekwondo kicks using inertial data and impulses.

In this paper, we propose a three-dimensional design and evaluation framework and process based on a probabilistic-based motion synthesis algorithm and biomechanical analysis system for the design of the Smith machine and squat training programs. Moreover, we implemented a prototype system to validate the proposed framework. The framework consists of an integrated human–machine–environment model as well as a squat motion synthesis system and biomechanical analysis system. In the design and evaluation process, we created an integrated model in which interactions between a human body and machine or the ground are modeled as joints with constraints at contact points. Next, we generated Smith squat motion using the motion synthesis program based on a Gaussian process regression algorithm with a set of given values for independent variables. Then, using the biomechanical analysis system, we simulated joint moments and muscle activities from the input of the integrated model and squat motion. We validated the model and algorithm through physical experiments measuring the electromyography (EMG) signals, ground forces, and squat motions as well as through a biomechanical simulation of muscle forces. The proposed approach enables the incorporation of biomechanics in the design process and reduces the need for physical experiments and prototypes in the development of training programs and new Smith machines.

Background and Objectives: This study aimed to verify the validity of markerless gait analysis using both single-camera markerless system (S-ML) and multi-camera markerless system (M-ML) approaches by comparing them with a gold-standard marker-based system (MB). Materials and Methods: Sixteen healthy adults walked at their gait speed, and their gaits were simultaneously analyzed using three systems: S-ML, M-ML, and MB. Intraclass correlation coefficients were used to assess the reliability of the spatiotemporal parameters, and the root mean squared error (RMSE) was calculated to quantify the kinematic differences relative to the MB systems. Results: Both S-ML and M-ML demonstrated good-to-excellent reliability in spatiotemporal parameters, including step length, stance time, and gait speed, whereas stride length and swing time measured by S-ML revealed moderate reliability. In terms of joint kinematics, S-ML demonstrated a performance comparable to that of M-ML, particularly at the hip and knee in the sagittal plane. For certain parameters, such as knee abduction/adduction in the frontal plane, the S-ML demonstrated lower RMSE values than M-ML. In contrast, the ankle joint angles estimated using S-ML exhibited reduced agreement. Conclusions: In conclusion, markerless gait analysis can serve as an alternative to conventional gait analysis. However, certain parameters need to be improved.

Mechanical testers have commonly been used to measure the frictional properties of socks. However, the friction values may be susceptible to the level of stretchiness of tested fabrics or human variability. Thus, the aim of this study was to propose a novel method that enables friction measurement of socks in a sock-wearing condition with less human variability effects. Five socks with different frictional properties were chosen. Three experimental ramp tests were performed with an artificial structure shaped like the foot-ankle complex (last) and a ramp tester to quantify the static coefficient of friction (COF) at the foot against sock, at the sock against an insole, and the foot wearing socks against the insole, respectively. The angle where the last slipped while the ramp surface was gradually inclined was used to compute the static COF values for each sock. The reliability was 0.99, and COF values ranged from 0.271 to 0.861 at the foot-sock interface, 0.342 to 0.639 at the sock-insole interface, and 0.310 to 0.614 in the third test. Socks with different frictional properties were successfully distinguished each other. Thus, the suggested protocol could be a reliable option for measuring the static COF values in the tension similar with it found in a sock-waring condition with reduced effects of human variability.