Motion Capture Measurements Research Articles

Estimation of joint kinetics during manual material handling using inertial motion capture: a follow-up study

Abstract Musculoskeletal models based on inertial motion capture and ground reaction force (GRF) prediction hold great potential for field-based risk assessment of manual material handling. However, previous evaluations have identified inaccuracies in the methodology?s estimation of spinal forces, while the accuracy of other key outcome variables is currently unclear. This study evaluated knee, shoulder, and L5-S1 joint reaction forces (JRFs) derived from a musculoskeletal model based on inertial motion capture and GRF prediction against a model based on simultaneously collected optical motion capture and force plate measurements. Data from 19 healthy subjects performing lifts with various horizontal locations, deposit heights and asymmetry angles were analyzed, and the consistency and absolute agreement of the model estimates statistically compared. Despite varying levels of agreement across tasks and variables, considerable absolute differences were identified for the L5-S1 axial compression (RMSE = 63.0-94.2%BW) and anteroposterior shear forces (RMSE = 40.9-80.6%BW) as well as the bilateral knee JRFs (RMSE = 78.9-117%BW). Glenohumeral JRFs and vertical GRFs exhibited the highest overall consistency (r = 0.33-0.91, median 0.78) and absolute agreement (RMSE = 7.63-34.9%BW), while the L5-S1 axial compression forces also showed decent consistency (r = 0.04-0.89, median 0.80). The findings generally align with prior evaluations, indicating persistent challenges with the accuracy of key outcome variables. While the modelling framework shows promise, further development of the methodology is encouraged to enhance its applicability in ergonomic evaluations.

Wood Chippers

The position and shape of the feed channel (FC) in low-power woodchippers significantly affect the way machine operators' upper limbs move. The execution of operator's movements can be accomplished in different working areas: comfortable, acceptable, or not recommended due to overloads in the musculoskeletal system. Three groups of male subjects from Central Europe, divided according to anthropometric dimensions from centile groups C5, C50 and C95, were selected to assess upper limb movement, by adopting a Motion Capture measurement method. The tests have shown that the limb closer to the FC (left hand in this study) carries out all the movements in the allowed zone (AZ), while between 79% and 96% of the limb movement is contained within the comfort zone (CZ). The right limb has a greater amplitude of motion outside the CZ working area – from 0% to 69% and for AZ – in the CZ can vary by up to 51%. It was found that commercial low-power woodchippers deployed in urban areas, typically induce overloads in the musculoskeletal system of the operator during the use. Based on the research results, the authors see the need to improve FC position adjustment for the operators of these machines.

The Validity and Usability of Markerless Motion Capture and Inertial Measurement Units for Quantifying Dynamic Movements

ABSTRACT Purpose Motion capture technology is quickly evolving providing researchers, clinicians, and coaches with more access to biomechanics data. Markerless motion capture and inertial measurement units (IMUs) are continually developing biomechanics tools that need validation for dynamic movements before widespread use in applied settings. This study evaluated the validity of a markerless motion capture, IMU, and red, green, blue, and depth (RGBD) camera system as compared to marker-based motion capture during countermovement jumps, overhead squats, lunges, and runs with cuts. Methods Thirty adults were recruited for this study (sex: 18 females, 12 males; age: 25.4 ± 8.6 years; height: 1.71 ± 0.08 m; weight: 71.6 ± 11.5 kg). Data were collected simultaneously with four motion capture technologies (i.e., Vicon – marker-based, Theia/Optitrack – markerless, APDM Opals – IMUs, and Vald HumanTrak – RGBD camera). System validity for lower and upper body joint angles was evaluated using bias, root mean squared error (RMSE), precision, maximum absolute error, and intraclass correlation coefficients. System usability was descriptively analyzed. Results Overall, markerless motion capture had the highest validity (sagittal plane RMSE: 3.20-15.66°; frontal plane RMSE: 2.12-9.14°; transverse plane RMSE: 3.160-56.61°), followed by the IMU system (sagittal plane RMSE: 8.11-28.37°; frontal plane RMSE: 3.26-16.98°; transverse plane RMSE: 5.08-116.75°), and lastly the RGBD system (sagittal plane bias: 0.55-129.48°; frontal plane bias: 1.35-52.06°). Conclusions Markerless motion capture and IMUs have moderate validity for joint kinematics, while the RGBD system did not have adequate validity. Markerless systems have lower data processing time, require moderate technical expertise, but have high data storage size. IMUs are easier to use, can collect data in any location, but require participant set-up. Overall, individuals using motion capture should consider the specific movements, testing locations, and technical expertise available prior to selecting a system.

Shoulder Motion Overestimated by Mallet Scores.

Brachial plexus birth injuries (BPBI) occur in up 0.4 to 4.6 per 1000 live births. Weakness about the shoulder and development of glenohumeral joint contractures are common sequalae of BPBI. Shoulder function in children with BPBI is frequently assessed using the modified Mallet classification to evaluate upper extremity motion deficits. The purpose of this study was to assess the accuracy of the abduction, external rotation, and hand-to-mouth Mallet classification scores in children with BPBI using motion capture. A retrospective study of 107 children with BPBI who underwent motion capture assessment and Mallet scores on the same date were reviewed. Motion capture measurements were used to calculate humerothoracic elevation and external rotation joint angles in the abduction/hand-to-mouth and external rotation positions, respectively. The humerothoracic joint angles were converted to the corresponding Mallet scores. Discrepancies between the Mallet scores determined by clinicians and those determined by motion capture were assessed. For abduction, 24.3% of Mallet scores were misclassified during clinical examination. Of the misclassified scores, 22 were overestimated by 1 point and 4 were underestimated by 1 point compared with motion capture. For external rotation, 72.9% of Mallet scores were misclassified during clinical examination. Only 5 patients had an HT elevation that was less than 40 degrees, with 4 of them (80%) having a Mallet hand-to-mouth score of 4. There were no differences in proportion of patients with HT elevation less than 40 degrees who had a Mallet score of 4 or a Mallet score less than 4. There was better agreement in global abduction Mallet scores compared with external rotation and hand-to-mouth Mallet scores. This difference was likely due to the complex compensatory strategies that patients employ while performing external rotation and hand-to-mouth positions. The inaccuracy of the clinically determined Mallet scores is alarming given that they are frequently utilized to assist with surgical indications and are commonly used as outcome measures. Level IV Case series.

Comparative assessment of heel rise detection for consistent gait phase separation

BackgroundAccurate identification of gait events is crucial to reliable gait analysis. Heel rise, a key event marking the transition from mid-stance to terminal stance, poses challenges in precise detection due to its gradual nature. This leads to variability in accuracy across studies utilizing diverse measuring techniques. Research questionHow do different HR detection methods compare when assessed against the underlying heel motion pattern and visual detection across varying speed, footwear conditions, and individuals? MethodsLeveraging data from over 10,000 strides in diverse scenarios with 15 healthy subjects, we evaluated methods based on measurements from optical motion capture (OMC), force plates, and shank-mounted inertial measurement units (IMUs). The evaluation of these methods included an assessment of their precision and consistency with the heel marker's motion pattern and agreement with visually detected heel rise. ResultsOMC-based heel rise detection methods, utilizing the heel marker's vertical acceleration and jerk, consistently identified the same point in the heel motion pattern, outperforming velocity-based methods and our new position-based method resembling traditional footswitch-based heel rise detection. Variability in velocity and position-based methods derives from subtle heel rise variations after mid-stance, exhibiting individual differences. Our proposed IMU-based methods show promise by closely matching OMC-based accuracy. SignificanceThe results have significant implications for gait analysis, providing insights into heel rise event detection's complexities. Accurate HR identification is crucial for gait phase separation, and our findings, especially with the robust heel marker's jerk-based method, enhance precision and consistency across walking conditions. Moreover, our successful development and validation of IMU-based algorithm offer cost-effective and mobile alternative for HR detection, expanding their potential use in comprehensive gait analysis.

A Contemporary analysis of tackle height: Implications for head injury risk and tackler technique in Rugby Union

Rugby tackles pose significant head injury risks, raising concerns for players' well-being. Despite advocating for lower tackle heights for safety, limited research exists explaining how tackle height influences head characteristics and tackler technique. This study aimed to investigate the effects of two tackle heights, aligned with recent law changes in the community game, on head and joint kinematics for both tackler and the ball carrier. To strengthen ecological validity, innovative techniques were employed, including markerless motion capture and Inertial Measurement Units (IMUs), for tracking of the tackler's joint kinematics and head accelerations until ground contact, addressing a key limitation in previous research. Ethical approval was granted by Cardiff Metropolitan University. Ten male participants (mean ± SD age: 22 ± 3 years, stature: 184 ± 10 cm, mean mass: 94 ± 15 kg) alternated between tackler and ball carrier roles, executing upper and lower body tackles. Theia3D markerless motion capture software (Theia3Dv2022.1.0.2309, Theia Markerless, Inc., Kingston, ON, Canada) and Vicon IMeasureU Blue Trident dual-G IMUs measured tackle height's impact on head and joint kinematics. Discrete joint angles at key events and continuous joint angles normalised from 0% at Step 1 to 100% at Contact were exported. Peak head accelerations and continuous normalised head acceleration data were exported. Statistical analyses included a paired t-test for discrete joint angle data and the Wilcoxon Rank Sum Test for peak linear and angular accelerations. Cohen’s d and effect sizes were calculated for respective analyses. One-dimensional statistical parametric mapping (SPM) and one-dimensional statistical non-parametric mapping (SnPM) facilitated statistical differences across continuous variables of interest. Significant increases (P < 0.05) were noted in ball carriers' inertial head kinematics in the upper body tackle, while no such distinctions were observed for tacklers. The low tackle condition consistently exhibited substantial reductions in bilateral hip flexion, particularly during contact (Effect size; lead: −1.866; rear: −1.977). Lumbopelvic flexion significantly decreased in each event for the low tackle condition, with the largest effect size during contact (−3.91). These findings underscore the heightened inertial loading with higher tackles, indicating an elevated risk of head injury, despite current tackle height regulations. The adjustments made by the tackler in response to varying tackle heights are not evident in knee flexion. Increased knee flexion during the preparatory phase could alleviate the necessity for such a flexed lumbopelvic angle. This study, utilising innovative techniques, highlights the potential for more ecologically valid approaches to biomechanical research in rugby tackling.

Pole balancing on the fingertip: model-motivated machine learning forecasting of falls.

Introduction: There is increasing interest in developing mathematical and computational models to forecast adverse events in physiological systems. Examples include falls, the onset of fatal cardiac arrhythmias, and adverse surgical outcomes. However, the dynamics of physiological systems are known to be exceedingly complex and perhaps even chaotic. Since no model can be perfect, it becomes important to understand how forecasting can be improved, especially when training data is limited. An adverse event that can be readily studied in the laboratory is the occurrence of stick falls when humans attempt to balance a stick on their fingertips. Over the last 20 years, this task has been extensively investigated experimentally, and presently detailed mathematical models are available. Methods: Here we use a long short-term memory (LTSM) deep learning network to forecast stick falls. We train this model to forecast stick falls in three ways: 1) using only data generated by the mathematical model (synthetic data), 2) using only stick balancing recordings of stick falls measured using high-speed motion capture measurements (human data), and 3) using transfer learning which combines a model trained using synthetic data plus a small amount of human balancing data. Results: We observe that the LTSM model is much more successful in forecasting a fall using synthetic data than it is in forecasting falls for models trained with limited available human data. However, with transfer learning, i.e., the LTSM model pre-trained with synthetic data and re-trained with a small amount of real human balancing data, the ability to forecast impending falls in human data is vastly improved. Indeed, it becomes possible to correctly forecast 60%-70% of real human stick falls up to 2.35 s in advance. Conclusion: These observations support the use of model-generated data and transfer learning techniques to improve the ability of computational models to forecast adverse physiological events.

The Functions of Phasic Wing-Tip Folding on Flapping-Wing Aerodynamics.

Insects produce a variety of highly acrobatic maneuvers in flight owing to their ability to achieve various wing-stroke trajectories. Among them, beetles can quickly change their flight velocities and make agile turns. In this work, we report a newly discovered phasic wing-tip-folding phenomenon and its aerodynamic basis in beetles. The wings' flapping trajectories and aerodynamic forces of the tethered flying beetles were recorded simultaneously via motion capture cameras and a force sensor, respectively. The results verified that phasic active spanwise-folding and deployment (PASFD) can exist during flapping flight. The folding of the wing-tips of beetles significantly decreased aerodynamic forces without any changes in flapping frequency. Specifically, compared with no-folding-and-deployment wings, the lift and forward thrust generated by bilateral-folding-and-deployment wings reduced by 52.2% and 63.0%, respectively. Moreover, unilateral-folding-and-deployment flapping flight was found, which produced a lateral force (8.65 mN). Therefore, a micro-flapping-wing mechanism with PASFD was then designed, fabricated, and tested in a motion capture and force measurement system to validate its phasic folding functions and aerodynamic performance under different operating frequencies. The results successfully demonstrated a significant decrease in flight forces. This work provides valuable insights for the development of flapping-wing micro-air-vehicles with high maneuverability.

Sensor location influences the associations between IMU and motion capture measurements of impact landing in healthy male and female runners at multiple running speeds

ABSTRACT This study investigated the relationships between inertial measurement unit (IMU) acceleration at multiple body locations and 3D motion capture impact landing measures in runners. Thirty healthy runners ran on an instrumented treadmill at five running speeds (9–17 km/h) during 3D motion capture. Axial and resultant acceleration were collected from IMUs at the distal and proximal tibia, distal femur and sacrum. Relationships between peak acceleration from each IMU location and patellofemoral joint (PFJ) peak force and loading rate, impact peak and instantaneous vertical loading rate (IVLR) were investigated using linear mixed models. Acceleration was positively related to IVLR at all lower limb locations (p < 0.01). Models predicted a 1.9–3.2 g peak acceleration change at the tibia and distal femur, corresponding with a 10% IVLR change. Impact peak was positively related to acceleration at the distal femur only (p < 0.01). PFJ peak force was positively related to acceleration at the distal (p = 0.03) and proximal tibia (p = 0.03). PFJ loading rate was positively related to the tibia and femur acceleration in males only (p < 0.01). These findings suggest multiple IMU lower limb locations are viable for measuring peak acceleration during running as a meaningful indicator of IVLR.

"Knees Out" or "Knees In"? Volitional Lateral vs. Medial Hip Rotation During Barbell Squats.

Chiu, LZF. "Knees out" or "Knees in"? Volitional lateral versus medial hip rotation during barbell squats. J Strength Cond Res 38(3): 435-443, 2024-Medial or lateral hip rotation may be present during barbell squats, which could affect the hip frontal and transverse plane moments. Male (n = 14) and female (n = 18) subjects performed squats using their normal technique and with volitional medial and lateral hip rotation. Hip net joint moments (NJM) were calculated from 3-dimensional motion capture and force platform measurements. Statistical significance was set for omnibus tests (α = 0.05) and Bonferroni's corrected for pairwise comparisons (αt-test = 0.0056). Normal squats required hip extensor, adductor, and lateral rotator NJM. Lateral rotation squats had smaller hip extensor (p = 0.002) and lateral rotator (p < 0.001) NJM and larger hip adductor (p < 0.001) NJM than normal squats. Medial rotation squats had smaller hip extensor (p = 0.002) and adductor (p < 0.001) NJM and larger hip lateral rotator (p < 0.001) NJM than normal squats. These differences exceeded the minimum effects worth detecting. As gluteus maximus exerts hip extensor and lateral rotator moments, and the adductor magnus exerts hip extensor and adductor moments, these muscles combined would be required to meet these hip demands, supporting previous research that has established these muscles as the primary contributors to the hip extensor NJM. Lateral rotation squats reduce hip lateral rotator and increase hip adductor NJM, which may be hypothesized as preferentially loading adductor magnus. Medial rotation squats increase hip lateral rotator and decrease hip adductor NJM; therefore, this variant may shift loading to the gluteus maximus.

Markerless motion capture technology to quantify functional performance in dementias: a systematic review

AbstractBackgroundQuantifying functional performance objectively and unobtrusively could offer vital insights for clinical decision making on the level of support required by people living with dementia and other neurodegenerative conditions. Markerless motion capture (MMC) is more suitable for deployment in clinical and real‐world settings because single sensor MMC systems are less expensive and less complex compared to marker‐based systems. The aim of this study was to complete a systematic review of published literature on the use of MMC with full‐body tracking in people with dementias.MethodWe systematically searched for relevant articles using the search terms motion capture, motion analysis and movement analysis combined with dementia, mild cognitive impairment (MCI) and Parkinson’s disease (PD) which yielded a total of 1103 results. Inclusion criteria were MMC and full‐body tracking. Studies evaluating marker‐based motion capture, body‐worn sensors or Inertial Measurement Units, virtual reality, kinetics and interventions like DBS or exercises were excluded. 134 studies were included for full article screening out of which 17 eligible studies that met the selection criteria were included in the review.ResultMMC devices used in the reviewed studies included Microsoft Kinect (n = 15) and unspecified 2D motion camera (n = 2). Patient groups included in the studies were PD (n = 12), dementia (n = 4) and MCI (n = 1). Functional performance components evaluated were gait (n = 16), sit to stand (n = 2), turning (n = 1) and stepping in place (n = 1). About half the studies used advanced machine learning methods whereas the other half used descriptive statistical methods. The lack of standardisation in the measures used prevented us from comparing and synthesizing study results.ConclusionAlthough MMC has the potential to be used for quantifying functional performance in people with dementia, MCI and PD, there appears to be significant variation in the methods used in its evaluation. Although features of mobility were measured, none of the reviewed studies analysed activities of daily living tasks, a key component of functional performance. Standardising the process for extracting features and assessing accuracy would enable more effective validation of real‐world impact. Further research is required in people with neurodegenerative conditions for MMC to be routinely used in clinical and real‐world applications.

Multi-task artificial neural networks and their extrapolation capabilities to predict full-body 3D human posture during one- and two-handed load-handling activities

Machine-learning based human posture-prediction tools can potentially be robust alternatives to motion capture measurements. Existing posture-prediction approaches are confined to two-handed load-handling activities performed at heights below 120 cm from the floor and to predicting a limited number of body-joint coordinates/angles. Moreover, the extrapolating power of these tools beyond the range of the input dataset they were trained for (e.g., for underweight, overweight, or left-handed individuals) has not been investigated. In this study, we trained/validated/tested two posture-prediction (for full-body joint coordinates and angles) artificial neural networks (ANNs) using both 70%/15%/15% random-hold-out and leave-one-subject-out methods, based on a comprehensive kinematic dataset of forty-one full-body skin markers collected from twenty right-handed normal-weight (BMI = 18–26 kg/m2) subjects. Subjects performed 204 one- and two-handed unloaded activities at different vertical (0 to 180 cm from the floor) and horizontal (up to 60 cm lateral and/or anterior) destinations. Subsequently, the extrapolation capability of the trained/validated/tested ANNs was evaluated using data collected from fifteen additional subjects (unseen by the ANNs); three individuals in five groups: underweight, overweight, obese, left-handed individuals, and subjects with a hand-load. Results indicated that the ANNs predicted body joint coordinates and angles during various activities with errors of ∼ 25 mm and ∼ 10°, respectively; considerable improvements when compared to previous posture-prediction ANNs. Extrapolation errors of our ANNs generally remained within the error range of existing ANNs with obesity and being left-handed having, respectively, the most and least compromising effects on their accuracy. These easy-to-use ANNs appear, therefore, to be robust alternatives to common posture-measurement approaches.

Inertial Measurement Unit Sensor-to-Segment Calibration Comparison for Sport-Specific Motion Analysis.

Wearable inertial measurement units (IMUs) can be utilized as an alternative to optical motion capture as a method of measuring joint angles. These sensors require functional calibration prior to data collection, known as sensor-to-segment calibration. This study aims to evaluate previously described sensor-to-segment calibration methods to measure joint angle range of motion (ROM) during highly dynamic sports-related movements. Seven calibration methods were selected to compare lower extremity ROM measured using IMUs to an optical motion capture system. The accuracy of ROM measurements for each calibration method varied across joints and sport-specific tasks, with absolute mean differences between IMU measurement and motion capture measurement ranging from <0.1° to 24.1°. Fewer significant differences were observed at the pelvis than at the hip, knee, or ankle across all tasks. For each task, one or more calibration movements demonstrated non-significant differences in ROM for at least nine out of the twelve ROM variables. These results suggest that IMUs may be a viable alternative to optical motion capture for sport-specific lower-extremity ROM measurement, although the sensor-to-segment calibration methods used should be selected based on the specific tasks and variables of interest for a given application.

Accuracy of Video-Based Gait Analysis Using Pose Estimation During Treadmill Walking Versus Overground Walking in Persons After Stroke.

Video-based pose estimation is an emerging technology that shows significant promise for improving clinical gait analysis by enabling quantitative movement analysis with little costs of money, time, or effort. The objective of this study is to determine the accuracy of pose estimation-based gait analysis when video recordings are constrained to 3 common clinical or in-home settings (ie, frontal and sagittal views of overground walking and sagittal views of treadmill walking). Simultaneous video and motion capture recordings were collected from 30 persons after stroke during overground and treadmill walking. Spatiotemporal and kinematic gait parameters were calculated from videos using an open-source human pose estimation algorithm and from motion capture data using traditional gait analysis. Repeated-measures analyses of variance were then used to assess the accuracy of the pose estimation-based gait analysis across the different settings, and the authors examined Pearson and intraclass correlations with ground-truth motion capture data. Sagittal videos of overground and treadmill walking led to more accurate measurements of spatiotemporal gait parameters versus frontal videos of overground walking. Sagittal videos of overground walking resulted in the strongest correlations between video-based and motion capture measurements of lower extremity joint kinematics. Video-based measurements of hip and knee kinematics showed stronger correlations with motion capture versus ankle kinematics for both overground and treadmill walking. Video-based gait analysis using pose estimation provides accurate measurements of step length, step time, and hip and knee kinematics during overground and treadmill walking in persons after stroke. Generally, sagittal videos of overground gait provide the most accurate results. Many clinicians lack access to expensive gait analysis tools that can help identify patient-specific gait deviations and guide therapy decisions. These findings show that video-based methods that require only common household devices provide accurate measurements of a variety of gait parameters in persons after stroke and could make quantitative gait analysis significantly more accessible.

Validation of a Wearable System for Lower Extremity Assessment.

Remote assessment and diagnosis of functional impairment caused by osteoarthritis (OA) of the knee can achieve early intervention of patients' functional impairment, prevent the deterioration of OA of the knee, and provide functional remote screening for patients with knee OA. This study introduced an inertial measurement unit (IMU) sensor-based system to assess lower extremity function and perform gait analysis. Then, we compared its accuracy to gold-standard motion capture and gait measurement systems. Nine adults were selected to participate in a comparative study of gait assessment outcomes using an IMU sensor-based wearable system, a gold-standard motion capture system, and a pressure-based gait analysis system. The subject walked on a path that incorporated all three systems. Data analysis was performed on spatiotemporal gait parameters, including velocity, cycle time, cadence, and stride length. This was followed by gait phases, including stance, swing, double stance, and single limb support phases. Data were processed using the data processing software of each system. An independent sample t-test was conducted for inter-group comparison to analyze the data. The spatiotemporal gait parameters of the systems demonstrated excellent consistency, and the gait phases showed high consistency. Compared to the gold-standard pressure-based gait analysis system (the GATERite system), the mean gait cycle time results were 1.124 s vs. 1.127 s (p = 0.404); cadence was 93.333 steps/min vs. 94.189 steps/min (p = 0.482); stance phase was 60.89% vs. 63.26% (p < 0.001); swing phase was 39.11% vs. 36.74% (p < 0.001); stride length was 1.404 m vs. 1.420 m (p = 0.743); speed was 1.093 m/s vs. 1.110 m/s (p = 0.725). Compared to the gold-standard video-based motion capture system, the root mean square error was 2.7° for the hip angle and 2.6° for the knee angle. This IMU-based wearable system delivered precise measuring results to evaluate patients with knee OA. This technology can also be used to guide rehabilitation exercises for patients with knee OA.

Defining humeral axial rotation with optical motion capture and inertial measurement units during functional task assessment.

Humeral motion can be challenging to measure and analyze. Typically, Euler/Cardan sequences are used for humeral angle decomposition, but choice of rotation sequence has substantial effects on outcomes. A new method called True axial rotation calculation may be more precise. The objective of this study is to compare humeral axial rotation measured from two systems (optical motion capture and inertial measurement units (IMUs)) and calculated with two methods (Euler angles and True axial). Motion of torso and dominant humerus of thirty participants free from any upper limb impairments was tracked using both systems. Each participant performed a functional tasks protocol. Humeral axial rotation was calculated with Euler decomposition and the True axial method. Waveforms were compared with two-way ANOVA statistical parametric mapping. A consistent pattern emerged: axial rotation was not different between motion capture systems when using the True axial method (p > .05), but motion capture systems showed relatively large magnitude differences (~ 20-30°) when using Euler angle calculation. Between-calculation method differences were large for both motion capture systems. Findings suggest that the True axial rotation method may result in more consistent findings that will allow for precise measurements and comparison between motion capture systems. Two methods for calculating humeral axial rotation measured from optical motion capture and inertial measurement units were compared.

Muscle function during cross-country skiing at different speed and incline conditions.

The human musculoskeletal system is well adapted to use energy-efficient muscle-tendon mechanics during walking and running, but muscle behaviour during on-snow locomotion is unknown. Here, we examined muscle and muscle-tendon unit behaviour during diagonal-style cross-country roller skiing at three speed and incline conditions to examine whether skiers can exploit energy-saving mechanisms of the muscle-tendon unit. We assessed lower leg muscle and muscle-tendon unit mechanics and muscle activity in 13 high-level skiers during treadmill roller skiing using synchronised ultrasound, motion capture, electromyography and ski-binding force measurements. Participants skied using diagonal style at 2.5 and 3.5 m s-1 up 5deg, and at 2.5 m s-1 up 10deg. We found an uncoupling of muscle and joint behaviour during most parts of the propulsive kick phase in all conditions (P<0.01). Gastrocnemius muscle fascicles actively shortened ∼0.9 cm during the kick phase, while the muscle-tendon unit went through a stretch-shortening cycle. Peak muscle-tendon unit shortening velocity was 5 times faster than fascicle velocity (37.5 versus 7.4 cm s-1, P<0.01). Steeper incline skiing was achieved by greater muscle activity (24%, P=0.04) and slower fascicle shortening velocity (3.4 versus 4.5 cm s-1, P<0.01). Faster speed was achieved by greater peak muscle activity (23%, P<0.01) and no change in fascicle shortening velocity. Our data show that, during diagonal-style cross-county skiing, muscle behaviour is uncoupled from the joint movement, which enables beneficial contractile conditions and energy utilisation with different slopes and speeds. Active preloading at the end of the glide phase may facilitate these mechanisms.

The assessment of presence and performance in an AR environment for motor imitation learning: A case-study on violinists

The acquisition of advanced gestures is a challenge in various domains of proficient sensorimotor performance. For example, orchestral violinists must move in sync with the lead violinist's gestures. To help train these gestures, an educational music play-back system was developed using a HoloLens 2 simulated AR environment and an avatar representation of the lead violinist. This study aimed to investigate the impact of using a 2D or 3D representation of the lead violinist's avatar on students' learning experience in the AR environment.To assess the learning outcome, the study employed a longitudinal experiment design, in which eleven participants practiced two pieces of music in four trials, evenly spaced over a month. Participants were asked to mimic the avatar's gestures as closely as possible when it came to using the bow, including bowing, articulations, and dynamics. The study compared the similarities between the avatar's gestures and those of the participants at the biomechanical level, using motion capture measurements, as well as the smoothness of the participants' movements. Additionally, presence and perceived difficulty were assessed using questionnaires.The results suggest that using a 3D representation of the avatar leads to better gesture resemblance and a higher experience of presence compared to a 2D representation. The 2D representation, however, showed a learning effect, but this was not observed in the 3D condition. The findings suggest that the 3D condition benefits from stereoscopic information that enhances spatial cognition, making it more effective in relation to sensorimotor performance. Overall, the 3D condition had a greater impact on performance than on learning.This work concludes with recommendations for future efforts directed towards AR-based advanced gesture training to address the challenges related to measurement methodology and participants' feedback on the AR application.

Human motion analysis and measurement techniques: current application and developing trend

Human motion analysis and measurement technology have been widely used in the fields of medical treatment, sports science, and rehabilitation. In clinical practice, motion analysis has been applied in the diagnosis and individualized treatment planning of various musculoskeletal diseases, and it is also an important objective scientific method to evaluate the therapeutic effect and the effectiveness of medical equipment. This study aimed to introduce the common modern motion capture measurement technology and equipment, the clinical application and limitations of motion analysis, and the possible development trend of motion analysis measuring techniques in the future. Motion analysis and measurement systems and medical image measurement and analysis technology have made landmark improvements over the past few decades in terms of orthopaedical biomechanics. Nevertheless, limitations still exist, both subjective and objective. All these drawbacks have promoted the exploration of the integrated methods that have now been widely used in motion analysis. The results of the case study about the subject-specific finite element modeling of the foot and sports shoe complex have also shown great consistency. Nevertheless, several possible future directions for motion analysis measuring techniques still exist. In the future, the progress of motion analysis and measurement methods will simultaneously drive the progress of orthopedics, rehabilitation, precision personalized medicine, and medical engineering.

Concurrent Validity of Measures of Upper Extremity Function Derived from Videogame-Based Motion Capture for Children with Hemiplegia.

Objective: Pediatric hemiplegia is associated with wide-ranging deficits in arm and hand motor function, neg-atively impacting participation in daily occupations and quality of life. This study investigated whether performance measures generated during therapy videogame play by children with hemiplegia can be valid indicators of upper extremity motor function. Materials and Methods: Ten children with hemiplegia used a custom therapy game system alternatively using their affected and non-affected hand to provide motion capture data that spans a wide range of motor function status. The children also completed a series of standardized outcome measure assessments with each hand, including the Quality of Upper Extremity Skills Test, the Jebsen Taylor Hand Function Test, and the Wolf Motor Function Test. Results: Statistical analysis using the nonparametric Spearman rank correlation revealed high and significant correlation between videogame-derived motion capture measures, characterizing the speed and smoothness of movements, and the standardized outcome measure assessments. Conclusion: The results suggest that a low-cost motion capture system can be used to monitor a child's motor function status and progress during a therapy program.