A SWOT Analysis of Portable and Low-Cost Markerless Motion Capture Systems to Assess Lower-Limb Musculoskeletal Kinematics in Sport

Wearable Technology in Piano Training: Improving Posture and Motion Precision with Biofeedback Devices Like Upright Go

This study examines the impact of wearable technologies on posture and movement accuracy in pianists during the learning process. The primary objective was to determine how the use of the Upright Go biofeedback device influences physical parameters and the effectiveness of exercise performance. To achieve this goal, a comparative study was conducted, in which participants were divided into two groups. Participants in Group A utilized the Upright Go device for posture monitoring and correction, whereas Group B followed a traditional training methodology. The study involved students from the Shenyang Conservatory of music, with 30 participants in each group. Prior to using the device, an individual posture calibration session was conducted for Group A. Over a 4-week period, both groups practiced for 1 h daily, performing identical musical exercises. At the end of the course, participants completed a test assignment designed to assess changes in posture and movement accuracy. To analyze the results, quantitative methods, including Student's t-test and ANCOVA, were employed, alongside qualitative methods such as semi-structured interviews and thematic analysis. The findings indicated that participants in Group A demonstrated a significant improvement in spinal deviation angle (an average of 6.4° compared to 13.7° in Group B), as well as greater accuracy and fluidity of movements. Additionally, participants using the device reported a reduction in physical tension and discomfort during practice sessions. Statistical analysis confirmed the significant influence of wearable technologies on key aspects of piano learning. This study highlights the potential for integrating wearable devices into educational processes aimed at enhancing students' awareness of proper posture and developing precise movement coordination skills. The findings may contribute to the development of innovative methodologies in music education and the improvement of musicians' physical training.

Influence of the Camera Viewing Angle on OpenPose Validity in Motion Analysis

(1) Background: With human pose estimation on the rise in the field of biomechanics, the need for scientific investigation of those algorithms is becoming evident. The validity of several of those algorithms has been presented in the literature. However, there is only limited research investigating the applicability of human pose estimation outside the lab. The aim of this research was to quantify the effect of deviating from the standard camera setup used in biomechanics research. (2) Methods: Video data from four camera viewing angles were recorded and keypoints estimated using OpenPose. Kinematic data were compared against a gold-standard marker-based motion capture system to quantify the effect of the camera viewing angle on the validity of joint angle estimation of the knee, hip, elbow and shoulder joints. (3) Results: The results of this study showed reasonable correlations between the joint angles of OpenPose and the gold standard, except for the shoulder. However, the analysis also revealed significant biases when comparing the joint angles inferred from the different viewing angles. In general, back-viewing cameras performed best and resulted in the lowest percental deviations. (4) Conclusions: The findings of this study underscore the importance of conducting a detailed examination of individual movements before proposing specific camera angles for users in diverse settings.

Early Recognition and Intervention in SIBlingS at High Risk for Neurodevelopment Disorders (ERI-SIBS): a controlled trial of an innovative and ecological intervention for siblings of children with autism spectrum disorder

Background

It has been widely demonstrated that siblings of children with autism spectrum disorder (ASD) have an increased risk of abnormal developmental trajectories. In response to this, early recognition protocols have been developed worldwide, aiming to promote early interventions that can positively impact the neurodevelopment of this population. This paper presents the protocol of a controlled trial: ERI-SIBS (Early Recognition and Intervention in SIBlingS at High Risk for Neurodevelopment Disorders) is an innovative and ecological early recognition and intervention program designed specifically for siblings of children with ASD.

Methods

We aim to recruit siblings at low risk and high risk of neurodevelopmental disorders. Based on clinical evaluation at T0, we will allocate the infants into three groups: Group 1, infants at low risk without any signs of neurodevelopmental disorders; Group 2, infants at high risk without any signs of neurodevelopmental disorders; Group 3: infants at low or high risk with signs suggestive of neurodevelopmental disorders. Children of Group 2 will undergo Active Monitoring (one 90 min session once a month for 6 months), while children of Group 3 will undergo Early Intervention (one 90 min session once a week for 6 months). In both cases, the ERI-SIBS contents are based on a multidimensional and naturalistic approach and always involve caregivers. All recruited children will be evaluated at three different time points (T0 within the 8 months of life of the child, T1 after 6 months and T2 after 12 months) using behavioural, technological, and biological techniques to assess infants' neurodevelopmental functions, parent-infant interaction, and early ASD markers.

Discussion

The ERI-SIBS study will expand knowledge regarding the impact of early intervention on families of infants at risk of neurodevelopmental disorders for the presence of a child with a diagnosis of ASD. The study will have the potential to significantly contribute to future research and the scientific and clinical debate on the best way to implement early intervention in at-risk populations.

Clinical Trial Registration

Clinicaltrials.gov identifier (NCT06512649).

Open Source, Open Science: Development of OpenLESS as the Automated Landing Error Scoring System

Context

The Open Landing Error Scoring System (OpenLESS) is a novel development aimed at automating the LESS for assessment of lower extremity movement quality during a jump-landing task. With increasing utilization of clinical measures to monitor outcomes and limited time during clinical visits for a lengthy analysis of functional movement, there is a pressing need to extend automation efforts. Addressing these issues, OpenLESS is an open-source tool that utilizes a freely available markerless motion capture system to automate the LESS using three-dimensional kinematics.

Objective

To describe the development of OpenLESS, examine the validity against expert rater LESS scores in healthy and clinically relevant cohorts, and assess the intersession reliability collected across four time points in an athlete cohort.

Design

Observational.

Participants

92 participants (72 females and 20 males, mean age 23.3 years) from healthy, post-anterior cruciate ligament reconstruction (ACLR; median 33 months since surgery), and amateur athlete cohorts.

Main Outcome Measures

A software package, "OpenLESS," was developed to interpret movement quality (LESS score) from kinematics captured from markerless motion capture. Validity and reliability were assessed with intraclass correlation coefficients (ICC), standard error of measure (SEM), and minimal detectable change (MDC).

Results

OpenLESS agreed well with expert rater LESS scores for healthy (ICC 2, k =0.79) and clinically relevant, post-ACLR cohorts (ICC 2, k =0.88). The automated OpenLESS system reduced scoring time, processing all 159 trials in under 15 minutes compared to the 18.5 hours (7 minutes per trial) required for manual expert rater scoring. When tested outside laboratory conditions, OpenLESS showed excellent reliability across repeated sessions (ICC 2, k >0.89), with a SEM of 0.98 errors and MDC of 2.72 errors.

Conclusion

OpenLESS shows promise as an efficient, automated tool for clinically assessing jump-landing quality, with good validity versus experts in healthy and post-ACLR populations, and excellent field reliability, addressing the need for objective movement analysis.

KEY POINTS

OpenLESS accurately detected jump-landing events (ICC>0.99) using markerless motion capture, validating its use as an alternative to laboratory-based force plate measurements.The automated scoring system showed good agreement with expert raters in healthy (ICC=0.79) and post-ACLR (ICC=0.88) populations.OpenLESS demonstrated good to excellent test-retest reliability (ICC=0.89) across multiple testing sessions, with minimal score variation, supporting its utility for longitudinal movement assessment.

AI-smartphone markerless motion capturing of hip, knee, and ankle joint kinematics during countermovement jumps

Recently, AI-driven skeleton reconstruction tools that use multistage computer vision pipelines were designed to estimate 3D kinematics from 2D video sequences. In the present study, we validated a novel markerless, smartphone video-based artificial intelligence (AI) motion capture system for hip, knee, and ankle angles during countermovement jumps (CMJs). Eleven participants performed six CMJs. We used 2D videos created by a smartphone (Apple iPhone X, 4K, 60 fps) to create 24 different keypoints, which together built a full skeleton including joints and their connections. Body parts and skeletal keypoints were localized by calculating confidence maps using a multilevel convolutional neural network that integrated both spatial and temporal features. We calculated hip, knee, and ankle angles in the sagittal plane and compared it with the angles measured by a VICON system. We calculated the correlation between both method's angular progressions, mean squared error (MSE), mean average error (MAE), and the maximum and minimum angular error and run statistical parametric mapping (SPM) analysis. Pearson correlation coefficients (r) for hip, knee, and ankle angular progressions in the sagittal plane during the entire movement were 0.96, 0.99, and 0.87, respectively. SPM group-analysis revealed some significant differences only for ankle angular progression. MSE was below 5.7°, MAE was below 4.5°, and error for maximum amplitudes was below 3.2°. The smartphone AI motion capture system with the trained multistage computer vision pipeline was able to detect, especially hip and knee angles in the sagittal plane during CMJs with high precision from a frontal view only.

On-water Rowing Biomechanical Assessment: A Systematic Scoping Review

BACKGROUND

Biomechanical parameters can distinguish a skilled rower from a less skilled rower and can provide coaches with meaningful feedback and objective evidence to inform coaching practices on rowing technique. Therefore, it is critical to understand which technical characteristics can be related to the fundamental rowing performance indicators. The aim of this systematic scoping review was to describe the current focus and density of rowing biomechanics research specific to on-water rowing and provide a guide for practitioners and researchers on future directions for on-water rowing biomechanics research.

METHODS

All peer-reviewed publications involving the on-water assessment of rowing biomechanics were reviewed from four databases (SPORTDiscus, PubMed, Sage online journals, and Web of Science). Search results returned 1659 records, of which 27 studies met the inclusion criteria for the review.

RESULTS

All reported variables were collated and summarised according to the three main measurements of basic mechanics: time, space and force. Study characteristics were collated to provide a descriptive overview of the literature. The main categorical variables included time, distance, velocity, acceleration, force, power and crew synchrony.

CONCLUSION

Data extraction revealed gate force, horizontal oar angle and boat velocity as the most reported variables with numerous subcategories of metrics within each measure. A framework to help guide and standardise on-water rowing biomechanical assessment and the establishment of standards for environmental data collection could help guide practitioners and researchers in the on-water rowing environment. This scoping review was registered on the Open Science Framework ( https://osf.io/8q5vw/ ).

Biological reliability of a movement analysis assessment using a markerless motion capture system

Introduction

Advances in motion capture technology include markerless systems to facilitate valid data collection. Recently, the technological reliability of this technology has been reported for human movement assessments. To further understand sources of potential error, biological reliability must also be determined. The aim of this study was to determine the day-to-day reliability for a three-dimensional markerless motion capture (MMC) system to quantify 4 movement analysis composite scores, and 81 kinematic variables.

Methods

Twenty-two healthy men (n = 11;X ¯ ± SD ; age = 23.0 ± 2.6 years, height = 180.4.8 cm, weight = 80.4 ± 7.3 kg) and women (n = 11; age = 20.8 ± 1.1 years, height = 172.2 ± 7.4 cm, weight = 68.0 ± 7.3 kg) participated in this study. All subjects performed 4 standardized test batteries consisting of 14 different movements on four separate days. A three-dimensional MMC system (DARI Motion, Lenexa, KS) using 8 cameras surrounding the testing area was used to quantify movement characteristics. 1 × 4 RMANOVAs were used to determine significant differences across days for the composite movement analysis scores, and RM-MANOVAs were used to determine test day differences for the kinematic data (p < 0.05). Intraclass correlation coefficients (ICCs) were reported for all variables to determine test reliability. To determine biological variability, mean absolute differences from previously reported technological variability data were subtracted from the total variability data from the present study.

Results

No differences were observed for any composite score (i.e., athleticism, explosiveness, quality, readiness; or any of the 81 kinematic variables. Furthermore, 84 of 85 measured variables exhibited good to excellent ICCs (0.61-0.99). When compared to previously reported technological variability data, 62.3% of item variability was due to biological variability, with 66 of 85 variables exhibiting biological variability as the primary source of error (i.e., >50% total variability).

Discussion

Combined, these findings effectively add to the body of literature suggesting sufficient reliability for MMC solutions in capturing kinematic features of human movement.

Validation of OpenCap: A low-cost markerless motion capture system for lower-extremity kinematics during return-to-sport tasks

Low-cost markerless motion capture systems offer the potential for 3D measurement of joint angles during human movement. This study aimed to validate a smartphone-based markerless motion capture system's (OpenCap) derived lower extremity kinematics during common return-to-sport tasks, comparing it to an established optoelectronic motion capture system. Athletes with prior anterior cruciate ligament reconstruction (12-18 months post-surgery) performed three movements: a jump-landing-rebound, single-leg hop, and lateral-vertical hop. Kinematics were recorded concurrently with two smartphones running OpenCap's software and with a 10-camera, marker-based motion capture system. Validity of lower extremity joint kinematics was assessed across 437 recorded trials using measures of agreement (coefficient of multiple correlation: CMC) and error (mean absolute error: MAE, root mean squared error: RMSE) across the time series of movement. Agreement was best in the sagittal plane for the knee and hip in all movements (CMC > 0.94), followed by the ankle (CMC = 0.84-0.93). Lower agreement was observed for frontal (CMC = 0.47-0.78) and transverse (CMC = 0.51-0.6) plane motion. OpenCap presented a grand mean error of 3.85° (MAE) and 4.34° (RMSE) across all joint angles and movements. These results were comparable to other available markerless systems. Most notably, OpenCap's user-friendly interface, free software, and small physical footprint have the potential to extend motion analysis applications beyond conventional biomechanics labs, thus enhancing the accessibility for a diverse range of users.

Accessibility of Motion Capture as a Tool for Sports Performance Enhancement for Beginner and Intermediate Cricket Players

Motion Capture (MoCap) has become an integral tool in fields such as sports, medicine, and the entertainment industry. The cost of deploying high-end equipment and the lack of expertise and knowledge limit the usage of MoCap from its full potential, especially at beginner and intermediate levels of sports coaching. The challenges faced while developing affordable MoCap systems for such levels have been discussed in order to initiate an easily accessible system with minimal resources.

A Step Forward Understanding Directional Limitations in Markerless Smartphone-Based Gait Analysis: A Pilot Study

The progress in markerless technologies is providing clinicians with tools to shorten the time of assessment rapidly, but raises questions about the potential trade-off in accuracy compared to traditional marker-based systems. This study evaluated the OpenCap system against a traditional marker-based system-Vicon. Our focus was on its performance in capturing walking both toward and away from two iPhone cameras in the same setting, which allowed capturing the Timed Up and Go (TUG) test. The performance of the OpenCap system was compared to that of a standard marker-based system by comparing spatial-temporal and kinematic parameters in 10 participants. The study focused on identifying potential discrepancies in accuracy and comparing results using correlation analysis. Case examples further explored our results. The OpenCap system demonstrated good accuracy in spatial-temporal parameters but faced challenges in accurately capturing kinematic parameters, especially in the walking direction facing away from the cameras. Notably, the two walking directions observed significant differences in pelvic obliquity, hip abduction, and ankle flexion. Our findings suggest areas for improvement in markerless technologies, highlighting their potential in clinical settings.

Agreement between a markerless and a marker-based motion capture systems for balance related quantities

Balance studies usually focus on quantities describing the global body motion. Assessing such quantities using classical marker-based approach can be tedious and modify the participant's behaviour. The recent development of markerless motion capture methods could bypass the issues related to the use of markers. This work compared dynamic balance related quantities obtained with markers and videos. Sixteen young healthy participants performed four different motor tasks: walking at self-selected speed, balance loss, walking on a narrow beam and countermovement jumps. Their movements were recorded simultaneously by marker-based and markerless motion capture systems. Videos were processed using a commercial markerless pose estimation software, Theia3D. The centre of mass position (CoM) was computed, and the associated extrapolated centre of mass position (XCoM) and whole-body angular momentum (WBAM) were derived. Bland-Altman analysis was performed and root mean square difference (RMSD) and coefficient of correlation were computed to compare the results obtained with marker-based and markerless methods. Bias remained of the magnitude of a few mm for CoM and XCoM positions, and RMSD of CoM and XCoM was around 1 cm. RMSD of the WBAM was less than 10 % of the total amplitude in any direction, and bias was less than 1 %. Results suggest that outcomes of balance studies will be similar whether marker-based or markerless motion capture system are used. Nevertheless, one should be careful when assessing dynamic movements such as jumping, as they displayed the biggest differences (both bias and RMSD), although it is unclear whether these differences are due to errors in markerless or marker-based motion capture system.

Using a Markerless Motion Capture System to Identify Preinjury Differences in Functional Assessments

Functional assessments identify biomechanical issues which may indicate risk for injury and can be used to monitor functional recovery after an injury or surgery. Although the gold standard to assess functional movements is marker-based motion capture systems, these are cost prohibitive and have high participant burden. As such, this study was conducted to determine if a markerless motion capture system could detect preinjury differences in functional movements between those who did and did not experience a noncontact lower extremity injury (NCLEI). A three-dimensional markerless motion capture system comprised an area of 3 m × 5 m × 2.75 m was used. Participants were Division I collegiate athletes wearing plain black long-sleeve shirts, pants, and running shoes of their choice. Functional assessments were the bilateral squat, right and left squat, double leg drop vertical jump, static vertical jump, right and left vertical jump, and right and left 5 hop. Measures were recorded once and the first NCLEI was recorded during the first year after measurement. Two-factor analysis of variance models were used for each measure with factors sex and injury status. Preinjury functional measures averaged 8.4 ± 3.4 minutes capture time. Out of the 333 participants recruited, 209 were male and 124 were female. Of those, 127 males (61%) and 92 females (74%) experienced later NCLEI. The most common initial NCLEI was nonanterior cruciate ligament knee injury in 38 females (41.3%) and 80 males (62.0%). Females had decreased flexion and lower valgus/varus displacement during the bilateral squat (p < 0.006). In addition, knee loading flexion for those who were not injured were more than that seen in the injured group, and was more pronounced for injured females (p < 0.03). The markerless motion capture system can efficiently provide data that can identify preinjury functional differences for lower extremity noncontact injuries. This method holds promise for effectively screening patients or other populations at risk of injury, as well as for monitoring pre-/postsurgery function, without the large costs or participant burden.

Measurement of Shoulder Abduction Angle with Posture Estimation Artificial Intelligence Model

Substantial advancements in markerless motion capture accuracy exist, but discrepancies persist when measuring joint angles compared to those taken with a goniometer. This study integrates machine learning techniques with markerless motion capture, with an aim to enhance this accuracy. Two artificial intelligence-based libraries-MediaPipe and LightGBM-were employed in executing markerless motion capture and shoulder abduction angle estimation. The motion of ten healthy volunteers was captured using smartphone cameras with right shoulder abduction angles ranging from 10° to 160°. The cameras were set diagonally at 45°, 30°, 15°, 0°, -15°, or -30° relative to the participant situated at a distance of 3 m. To estimate the abduction angle, machine learning models were developed considering the angle data from the goniometer as the ground truth. The model performance was evaluated using the coefficient of determination R2 and mean absolute percentage error, which were 0.988 and 1.539%, respectively, for the trained model. This approach could estimate the shoulder abduction angle, even if the camera was positioned diagonally with respect to the object. Thus, the proposed models can be utilized for the real-time estimation of shoulder motion during rehabilitation or sports motion.

Level of Agreement between the MotionMetrix System and an Optoelectronic Motion Capture System for Walking and Running Gait Measurements

Markerless motion capture systems (MCS) have been developed as an alternative solution to overcome the limitations of 3D MCS as they provide a more practical and efficient setup process given, among other factors, the lack of sensors attached to the body. However, this might affect the accuracy of the measures recorded. Thus, this study is aimed at evaluating the level of agreement between a markerless MSC (i.e., MotionMetrix) and an optoelectronic MCS (i.e., Qualisys). For such purpose, 24 healthy young adults were assessed for walking (at 5 km/h) and running (at 10 and 15 km/h) in a single session. The parameters obtained from MotionMetrix and Qualisys were tested in terms of level of agreement. When walking at 5 km/h, the MotionMetrix system significantly underestimated the stance and swing phases, as well as the load and pre-swing phases (p < 0.05) reporting also relatively low systematic bias (i.e., ≤ -0.03 s) and standard error of the estimate (SEE) (i.e., ≤0.02 s). The level of agreement between measurements was perfect (r > 0.9) for step length left and cadence and very large (r > 0.7) for step time left, gait cycle, and stride length. Regarding running at 10 km/h, bias and SEE analysis revealed significant differences for most of the variables except for stride time, rate and length, swing knee flexion for both legs, and thigh flexion left. The level of agreement between measurements was very large (r > 0.7) for stride time and rate, stride length, and vertical displacement. At 15 km/h, bias and SEE revealed significant differences for vertical displacement, landing knee flexion for both legs, stance knee flexion left, thigh flexion, and extension for both legs. The level of agreement between measurements in running at 15 km/h was almost perfect (r > 0.9) when comparing Qualisys and MotionMetrix parameters for stride time and rate, and stride length. The agreement between the two motion capture systems varied for different variables and speeds of locomotion, with some variables demonstrating high agreement while others showed poor agreement. Nonetheless, the findings presented here suggest that the MotionMetrix system is a promising option for sports practitioners and clinicians interested in measuring gait variables, particularly in the contexts examined in the study.

Accuracy of a markerless motion capture system in estimating upper extremity kinematics during boxing

Kinematic analysis of the upper extremity can be useful to assess the performance and skill levels of athletes during combat sports such as boxing. Although marker-based approach is widely used to obtain kinematic data, it is not suitable for “in the field” activities, i.e., when performed outside the laboratory environment. Markerless video-based systems along with deep learning-based pose estimation algorithms show great potential for estimating skeletal kinematics. However, applicability of these systems in assessing upper-limb kinematics remains unexplored in highly dynamic activities. This study aimed to assess kinematics of the upper limb estimated with a markerless motion capture system (2D video cameras along with commercially available pose estimation software Theia3D) compared to those measured with marker-based system during “in the field” boxing. A total of three elite boxers equipped with retroreflective markers were instructed to perform specific sequences of shadow boxing trials. Their movements were simultaneously recorded with 12 optoelectronic and 10 video cameras, providing synchronized data to be processed further for comparison. Comparative assessment showed higher differences in 3D joint center positions at the elbow (more than 3 cm) compared to the shoulder and wrist (&lt;2.5 cm). In the case of joint angles, relatively weaker agreement was observed along internal/external rotation. The shoulder joint revealed better performance across all the joints. Segment velocities displayed good-to-excellent agreement across all the segments. Overall, segment velocities exhibited better performance compared to joint angles. The findings indicate that, given the practicality of markerless motion capture system, it can be a promising alternative to analyze sports-performance.