Thanks to the advancement of computer vision technology and knowledge, the accuracy of human pose estimation has improved to the level that can be used for motion capture. Especially, human pose estimation has been gaining attention in research due to its efficiency and accuracy. The traditional motion capture system is not accessible to everyone. Human pose estimation could be a solution to replace the traditional system. However, the validity of human pose estimation has not been investigated enough yet in athletic and sports contexts. For this reason, this research aims to validate the kinematic measurements of human pose estimation by comparing them against the measurement of marker-based motion capture system. Five participants were recruited and asked to perform eight athletic and nine sports movements, respectively while being captured by normal and infrared cameras. Human pose estimation was run on frames from the RGB cameras to estimate human landmarks. From estimated landmarks in human pose estimation and marker-based motion capture system, elbow, shoulder, hip, and knee joint angles on the left and right sides were calculated and compared. Mean absolute error was used to evaluate the accuracy of human pose estimation measurements. The mean errors for athletic and sports movements were 9.7 ± 4.7 degrees and 9.0 ± 3.3 degrees, respectively. Errors were generally largest for elbow joint angles. The errors might be due to occlusion and systematic differences between human pose estimation and marker-based motion capture system. In conclusion, human pose estimation contains room for improvement, but has the potential to be used in some applications in which strictly precise measurements are not required.

The increasing diversity in motion capture technologies necessitates a structured approach to review and compare different systems. This paper presents a conceptual framework based on a review of existing motion capture methodologies, ranging from single-camera configurations to multi-camera systems enhanced with depth sensing and computer vision technology. The framework encompasses three distinct approaches: 1) single-camera with depth estimation, 2) single-camera with depth sensors, and 3) multiple cameras. Each method is detailed in terms of setup procedures, calibration techniques, advantages and disadvantages, as well as data processing workflows. The paper provides a framework and guide that can be adapted to different research and application contexts for sports and exercise, ensuring accurate and reliable 3D markerless motion capture. This framework aims to assist researchers, analysts and scientists in choosing the most suitable configuration based on their sport, specific requirements and/or constraints. By outlining the processes and considerations for each setup, this paper serves as a methodological guide, facilitating broader adoption and standardisation of advanced 3D motion capture technologies for sports and exercise. Although empirical data is not included in this paper, the focus on procedural guidelines demonstrates methodological rigour and practical implementation for 3D markerless motion capture research in sports and exercise.

Computer-vision-based frameworks enable markerless human motion capture on consumer-grade devices in real-time. They open up new possibilities for application, such as in the health and medical sector. So far, research on mobile solutions has been focused on 2-dimensional motion capture frameworks. 2D motion analysis is limited by the viewing angle of the positioned camera. New frameworks enable 3-dimensional human motion capture and can be supported through additional smartphone sensors such as LiDAR. 3D motion capture promises to overcome the limitations of 2D frameworks by considering all three movement planes independent of the camera angle. In this study, we performed a laboratory experiment with ten subjects, comparing the joint angles in eight different body-weight exercises tracked by Apple ARKit, a mobile 3D motion capture framework, against a gold-standard system for motion capture: the Vicon system. The 3D motion capture framework exposed a weighted Mean Absolute Error of 18.80° ± 12.12° (ranging from 3.75° ± 0.99° to 47.06° ± 5.11° per tracked joint angle and exercise) and a Mean Spearman Rank Correlation Coefficient of 0.76 for the whole data set. The data set shows a high variance of those two metrics between the observed angles and performed exercises. The observed accuracy is influenced by the visibility of the joints and the observed motion. While the 3D motion capture framework is a promising technology that could enable several use cases in the entertainment, health, and medical area, its limitations should be considered for each potential application area.

As pose estimation has garnered considerable attention for kinematic analysis, numerous pose estimation models have been developed in recent times. A pose estimation model is a trained neural network that predicts human body landmarks from an image. Each model contains different strong and weak points, which make it difficult for users to decide which model to use for kinematic analysis. The accuracy of the model can be one big factor for model selection, but there are not many studies investigating this critical point. Therefore, this study aims to investigate the accuracy of different models and variants by comparing the measurements from the models and variants against reference measurements. Five male participants were invited to this study. Each participant was asked to perform five exercises: squat, squat jump, counter movement jump, walk, and jog while being recorded by twelve normal RGB cameras (Contemplas) and ten marker-based tracking cameras (VICON). The video recordings from the Contemplas were processed by six different pose estimation models and variants: Mediapipe, MeTRAbs Small, MeTRAbs X Large, YOLO, MoveNet Lightning, and MoveNet Thunder to detect joint positions. From the detected joint positions, four joint angles, left hip, right hip, left knee, and right knee, were calculated. Three-way repeated measures ANOVA and Tukey HSD post-hoc analysis were applied to compare the pose estimation models with VICON measurements. The ANOVA result showed that exercise and model factors had a significant impact on the measurement errors although angle factor did not. In the post-hoc analysis, knee joint angle errors from YOLO, MoveNet Lightning, and MoveNet Thunder in jog and walk were significantly higher than those from Mediapipe, MeTRAbs Small, and MeTRAbs X Large. In conclusion, differentiated recommendations can be given for optimum model and variant choice in different conditions in kinematic analyses.

Introduction: Tooth extraction is one of the clinical internship requirements in Japan. Human posture during tooth extraction is important since poor posture can cause failure of the safe operation and musculoskeletal injuries. Only a few studies aimed to evaluate the posture, but most of them used manual or complex measurement methods which can lead to some inconvenient problems such as subjective biases, quantitativeness, and device availability. Thanks to the recent advancement of computer vision and technology, pose estimation has been widely used for kinematic analysis. However, none of the research has been used for posture analysis during tooth extraction. Aim: Therefore, this research aims to analyze posture kinematics during tooth extraction using pose estimation and find key kinematic variables for tooth extraction. Method: All participants were grouped into three; dental students, young dentists, and experienced dentists. They were asked to perform tooth extraction on a tooth extraction simulator while being video recorded. Pose estimation was used to extract joint locations on recorded videos. Joint angles of interest were calculated based on the extracted joint locations. Results: Right shoulder angles were significantly lower in the experienced dentists’ group than in other groups, Discussion: Which has been pointed out by other research as a crucial point. Conclusion: Although sample size is a main concern in this study, the result shows that pose estimation can be useful in posture analysis during tooth extraction.