Assessment Of Movement Quality Research Articles

A reliable and valid assessment of upper limb movement quality after stroke: the observational Drinking Task Assessment.

To develop and evaluate the reliability and validity of a new observational Drinking Task Assessment (DTA) designed to assess quality of movement in task performance after stroke. Reliability and validity. The DTA measures movement time and movement quality (smoothness, trunk, shoulder, elbow, and grasp movements) on a 4-level ordinal scale. Thirty participants with chronic stroke were assessed independently by 2 therapists. Intra-class correlation (ICC), standard error of measurement (SEM) and minimal real difference (MRD), weighted kappa, percentage of agreement, and Svensson method were used for reliability assessment. Motion capture-based kinematics and established clinical scales were used to evaluate validity. The absolute SEM and MRD for movement time were 0.4 and 1 s (11%), respectively. The ICC (≥ 0.93) and weighted kappa (0.71-1.0) showed good to excellent agreement for intra- and inter-rater reliability. DTA showed strong correlations with Fugl-Meyer Assessment (0.74), Action Research Arm Test (0.93), and kinematic measures of smoothness (0.93), trunk displacement (0.91), elbow extension (0.73), and shoulder movements (0.56), indicating good construct validity. The new DTA proved to be a reliable and valid tool for assessment of movement quality during task performance after stroke.

Badminton Movement Quality Assessment Through Kinect Sensor Data Using NDT-GCN Algorithm

Badminton Movement Quality Assessment Through Kinect Sensor Data Using NDT-GCN Algorithm

Movement Quality Assessment of Army Reserve Officers' Training Corps Cadets: A Report of Validity and Normative Data.

Movement quality screening in early-career military populations, like Army Reserve Officers' Training Corps (AROTC) cadets, could decrease the negative impact of musculoskeletal injury observed within the military. Movement quality screening techniques should be valid before being pursued in the field. Normative data describing movement quality of AROTC cadets are also needed. Therefore, the aims of this study were to determine criterion validity of several movement quality assessments and report normative jump-landing kinematics of AROTC cadets. This cross-sectional research was approved by the Institutional Review Board. As part of a larger study, 20 AROTC cadets (21.3 ± 3.4 years; 1.7 ± 0.1 m; 73.8 ± 14.8 kg) had 3-dimensional (3D) and 2-dimensional (2D) kinematic data collected simultaneously while performing a jump-landing task. Variables of interest were 3D hip and knee sagittal, frontal, and transverse joint angles at maximum knee flexion. An experienced rater calculated sagittal and frontal 2D joint angles at maximum knee flexion. Averages of 2D and 3D angles were calculated to describe normative data and for further data analysis. Bivariate correlations between 3D and 2D variables were used to determine criterion validity. Moderate correlations were found between 2D and 3D hip frontal plane angles (P = .05, r =-0.33), 2D and 3D knee sagittal plane angles (P = .04, r = 0.35), and 2D and 3D knee frontal plane angles (P = .03, r = -0.36). Normative values of knee and hip kinematics demonstrated averages of 17.58° of knee adduction, 16.48° of knee external rotation, 11.57° of hip abduction, 10.76° of hip internal rotation, and 103.47° of knee flexion during landings. However, ranges demonstrated that landing patterns vary within AROTC cadets. The normative values of 3D jump-landing kinematic data indicate that movement quality varies greatly within AROTC cadets, and some cadets display potentially injurious movements. Therefore, screening movement quality could be beneficial to determine musculoskeletal injury risk in AROTC cadets. Based on the correlations discovered in this study, we recommend the 2D techniques used in this study be researched further as they may serve as alternatives to expensive, timely 3D techniques that could be better utilized in military environments.

3D human pose data augmentation using Generative Adversarial Networks for robotic-assisted movement quality assessment.

In the realm of human motion recognition systems, the augmentation of 3D human pose data plays a pivotal role in enriching and enhancing the quality of original datasets through the generation of synthetic data. This augmentation is vital for addressing the current research gaps in diversity and complexity, particularly when dealing with rare or complex human movements. Our study introduces a groundbreaking approach employing Generative Adversarial Networks (GANs), coupled with Support Vector Machine (SVM) and DenseNet, further enhanced by robot-assisted technology to improve the precision and efficiency of data collection. The GANs in our model are responsible for generating highly realistic and diverse 3D human motion data, while SVM aids in the effective classification of this data. DenseNet is utilized for the extraction of key features, facilitating a comprehensive and integrated approach that significantly elevates both the data augmentation process and the model's ability to process and analyze complex human movements. The experimental outcomes underscore our model's exceptional performance in motion quality assessment, showcasing a substantial improvement over traditional methods in terms of classification accuracy and data processing efficiency. These results validate the effectiveness of our integrated network model, setting a solid foundation for future advancements in the field. Our research not only introduces innovative methodologies for 3D human pose data enhancement but also provides substantial technical support for practical applications across various domains, including sports science, rehabilitation medicine, and virtual reality. By combining advanced algorithmic strategies with robotic technologies, our work addresses key challenges in data augmentation and motion quality assessment, paving the way for new research and development opportunities in these critical areas.

Measurement properties of the Body Awareness Scale Movement Quality (BAS MQ) in persons on the autism spectrum: A preliminary Rasch analysis

BackgroundPersons on the autism spectrum exhibit poorer body awareness than neurotypical persons. Since movement quality may be regarded as an expression of body awareness, assessment of movement quality is important. Sound assessments of measurement properties are essential if reliable decisions about body awareness interventions for persons on the autism spectrum are to be made, but there is insufficient research. ObjectiveTo assess measurement properties of the Body Awareness Scale Movement Quality (BAS MQ) in an autism and a neurotypical reference group. MethodsPersons on the autism spectrum (n=108) and neurotypical references (n=32) were included. All were assessed with BAS MQ. Data were analyzed according to the Rasch model. ResultsBAS MQ was found to have acceptable unidimensionality, supported by the fit statistics. The hierarchical ordering showed that coordination ability was the most difficult, followed by stability and relating. Response category functioning worked as intended for 19 out of 23 items. There were few difficult items, which decreased targeting. Reliability measures were good. BAS MQ discriminated between the autism and the reference groups, with the autism group exhibiting poorer movement quality, reflecting clinical observations and previous research. ConclusionsBAS MQ was found to have acceptable measurement properties, though suffering from problems with targeting item difficulty to person ability for persons on the autism spectrum. The BAS MQ may, along with experienced movement quality, contribute to clinically relevant information of persons on the autism spectrum, although we encourage refinements and further analyses to improve its measurement properties.

Algorithmic assessment of shoulder function using smartphone video capture and machine learning

Tears within the stabilizing muscles of the shoulder, known as the rotator cuff (RC), are the most common cause of shoulder pain—often presenting in older patients and requiring expensive advanced imaging for diagnosis. Despite the high prevalence of RC tears within the elderly population, there is no previously published work examining shoulder kinematics using markerless motion capture in the context of shoulder injury. Here we show that a simple string pulling behavior task, where subjects pull a string using hand-over-hand motions, provides a reliable readout of shoulder mobility across animals and humans. We find that both mice and humans with RC tears exhibit decreased movement amplitude, prolonged movement time, and quantitative changes in waveform shape during string pulling task performance. In rodents, we further note the degradation of low dimensional, temporally coordinated movements after injury. Furthermore, a logistic regression model built on our biomarker ensemble succeeds in classifying human patients as having a RC tear with > 90% accuracy. Our results demonstrate how a combined framework bridging animal models, motion capture, convolutional neural networks, and algorithmic assessment of movement quality enables future research into the development of smartphone-based, at-home diagnostic tests for shoulder injury.

Reliability and Measurement Error of the Qualitative Analysis of Single Leg Loading (QASLS) Tool For Unilateral Tasks.

Movement quality assessment is popular within clinical and sporting practice, due to the contribution diminished or suboptimal movement quality is believed to have on musculoskeletal (MSK) injury risk. Various movement quality assessments exist, many are limited to bilateral or jumping movements evaluation. Qualitative analysis of single leg loading (QASLS) is a new clinical assessment tool for unilateral tasks that utilizes a dichotomous scoring system of ten questions relating to the segmental body regions of the trunk, lower and upper limb. To determine the intra and inter-rater, within- and between-session reliability of the QASLS tool during two unilateral movement tasks, and provide insight to measurement error and smallest detectable difference (SDD). Reliability Study. Fifteen healthy females (mean age 19 years SD2; height 167 cm, +/- 6; weight 56 kg, +/- 6) completed two unilateral tasks, single leg squat (SLS) and single leg landing (SLL), within session data collection occurred on the same day, with between session data collection occurring seven days later. Tasks were scored with the QASLS tool via video playback. Intra-Class correlation coefficients (ICCk,3) were used to measure within and between session reliability, and Kappa coefficients and percentage of exact agreement (PEA%) were used to determine intra and inter-rater reliability. Standard error of measurement (SEM) and the SDD for the compound score of each limb was calculated. Within session reliability of QASLS scores was good (ICC = 0.82-0.86) for SLS and moderate (ICC = 0.67-0.87) for SLL. Between session reliability was moderate (ICC = 0.69-0.87) for SLS and excellent (ICC = 0.92-0.93) for SLL. SEM was less than 1 point, and SDD for compound score ranging from 1.0-2.5 points. Intra-rater agreement of compound QASLS score was near perfect (k = 0.85-100; PEA% 90-100%) and agreement of individual components was substantial- near perfect (k = 0.13-0.74; PEA% 78-100%). Inter-rater agreement for compound QASLS scores ranged from non-substantial (k = 0.13-0.74; PEA% 43.3-90%) for SLS and non-slight (k =0.03-0.17; PEA% 43.3-60%) for SLL. The QASLS movement analysis tool can be used to analyze movement quality during two unilateral loading tasks with moderate to excellent within and between session reliability. PEA% was acceptable for inter-rater agreement, however rater education training is recommended to develop more acceptable levels of reliability. 3.

A comparative study of the 2D- and 3D-based skeleton avatar technology for assessing physical activity and functioning among healthy older adults.

Background: Maintaining physical activity (PA) and functioning (mobility, balance) is essential for older adults' well-being and quality of life. However, current methods (functional tests, self-reports) and available techniques (accelerometers, sensors, advanced movement analysis systems) for assessing physical activity and functioning have shown to be less reliable, time- and resource-consuming with limited routine usage in clinical practice. There is a need to simplify the assessment of physical activity and functioning among older adults both in health care and clinical studies. This work presents a study on using Skeleton Avatar Technology (SAT) for this assessment. SAT analyzes human movement videos using artificial intelligence (AI). The study compares handy SAT based on 2D camera technology (2D SAT) with previously studied 3D SAT for assessing physical activity and functioning in older adults. Objective: To explore whether 2D SAT yields accurate results in physical activity and functioning assessment in healthy older adults, statistically compared to the accuracy of 3D SAT. Method: The mobile pose estimation model provided by Tensorflow was used to extract 2D skeletons from the video recordings of functional test movements. Deep neural networks were used to predict the outcomes of functional tests (FT), expert-based movement quality assessment (EA), accelerometer-based assessments (AC), and self-assessments of PA (SA). To compare the accuracy with 3D SAT models, statistical analysis was used to test whether the difference in the predictions between 2D and 3D models is significant or not. Results: Overall, the accuracy of 2D SAT is lower than 3D SAT in predicting FTs and EA. 2D SAT was able to predict AC with 7% Mean Absolute Error (MAE), and self-assessed PA (SA) with 16% MAE. On average MAE was 4% higher for 2D than for 3D SAT. There was no significant difference found between the 2D and the 3D model for AC and for two FTs (30 seconds chair stand test, 30sCST and Timed up and go, TUG). A significant difference was found for the 2D- and 3D-model of another FT (4-stage balance test, 4SBT). Conclusion: Altogether, the results show that handy 2D SAT might be used for assessing physical activity in older adults without a significant loss of accuracy compared to time-consuming standard tests and to bulky 3D SAT-based assessments. However, the accuracy of 2D SAT in assessing physical functioning should be improved. Taken together, this study shows promising results to use 2D SAT for assessing physical activity in healthy older adults in future clinical studies and clinical practice.

Criteria Used to Determine Unrestricted Return to Activity After ACL Reconstruction in Pediatric and Adolescent Patients: A Systematic Review.

The rates of anterior cruciate ligament (ACL) graft failure or contralateral ACL rupture range from 17% to 30% in pediatric patients after ACL reconstruction (ACLR). A contributing factor to the high reinjury rate in this population may be the limited evidence regarding appropriate criteria for allowing unrestricted return to activity (RTA) postoperatively. To review the literature and identify the most commonly used criteria when determining unrestricted RTA after ACLR in pediatric patients. Systematic review; Level of evidence, 4. A search was performed of the Medline/PubMed, Cochrane Central Register of Controlled Trials, Embase, CINAHL, and SPORTDiscus databases using the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. The authors identified studies that included pediatric patients (<19 years of age) and specified the criteria used to determine RTA after ACLR. A total of 27 articles met all criteria for review, of which 13 studies only used 1 criterion when determining RTA. Objective criteria were the most common type of criteria for RTA (17 studies). Strength tests (15 studies) and hop tests (10 studies) were the most commonly used tasks when deriving RTA criteria. Only 2 studies used validated questionnaires to assess the patient's physiological readiness for RTA, and only 2 studies used an objective assessment of movement quality before RTA. Only 14 of the 27 reviewed studies reported using >1 criterion when determining RTA. Furthermore, few studies used patient-reported outcome measures or lower limb kinematics as RTA criteria, indicating that more research is needed to validate these metrics in the pediatric population.

Human Movement Quality Assessment Using Sensor Technologies in Recreational and Professional Sports: A Scoping Review.

The use of sensor technology in sports facilitates the data-driven evaluation of human movement not only in terms of quantity but also in terms of quality. This scoping review presents an overview of sensor technologies and human movement quality assessments in ecologically-similar environments. We searched four online databases to identify 16 eligible articles with either recreational and/or professional athletes. A total of 50% of the studies used inertial sensor technology, 31% vision-based sensor technology. Most of the studies (69%) assessed human movement quality using either the comparison to an expert’s performance, to an exercise definition or to the athletes’ individual baseline performance. A total of 31% of the studies used expert-based labeling of the movements to label data. None of the included studies used a control group-based study design to investigate impact on training progress, injury prevention or behavior change. Although studies have used sensor technology for movement quality assessment, the transfer from the lab to the field in recreational and professional sports is still emerging. Hence, research would benefit from impact studies of technology-assisted training interventions including control groups as well as investigating features of human movement quality in addition to kinematic parameters.

Concurrent validity of a custom computer vision algorithm for measuring lumbar spine motion from RGB-D camera depth data

Using RGB-D cameras as an alternative motion capture device can be advantageous for biomechanical spine motion assessments of movement quality and dysfunction due to their lower cost and complexity. In this study, we evaluated RGB-D camera performance relative to gold-standard optoelectronic motion capture equipment. Twelve healthy young adults (6M, 6F) were recruited to perform repetitive spine flexion-extension, while wearing infrared reflective marker clusters placed over their T10-T12 spinous processes and sacrum, and motion capture data were recorded simultaneously by both systems. Custom computer vision algorithms were developed to extract spine angles from depth data. Root mean square error (RMSE) was calculated for continuous Euler angles, and intraclass correlation coefficients (ICC2,1) were calculated between minimum and maximum angles and range of motion in all movement planes. RMSE was low (RMSE ≤ 2.05°) and reliability was good to excellent (0.849 ≤ ICC2,1 ≤ 0.979) across all movement planes. In conclusion, the proposed algorithm for tracking 3D lumbar spine motion during a sagittal movement task from one RGB-D camera is reliable in comparison to gold-standard motion tracking equipment. Future research will investigate accuracy and validity in a wider variety of movements, and will also investigate the development of novel methods to measure spine motion without using infrared reflective markers.

Visual assessment of movement quality: a study on intra- and interrater reliability of a multi-segmental single leg squat test

BackgroundThe Single Leg Squat test (SLS) is a common tool used in clinical examination to set and evaluate rehabilitation goals, but there is not one established SLS test used in the clinic. Based on previous scientific findings on the reliability of the SLS test and with a methodological rigorous setup, the aim of the present study was to investigate the intra- and interrater reliability of a standardised multi-segmental SLS test.MethodsWe performed a study of measurement properties to investigate the intra- and interrater reliability of a standardised multi-segmental SLS test including the assessment of the foot, knee, pelvis, and trunk. Novice and experienced physiotherapists rated 65 video recorded SLS tests from 34 test persons. We followed the Quality Appraisal for Reliability Studies checklist.ResultsRegardless of the raters experience, the interrater reliability varied between “moderate” for the knee variable (ĸ = 0.41, 95% CI 0.10–0.72) and “almost perfect” for the foot (ĸ = 1.00, 95% CI 1.00–1.00). The intrarater reliability varied between “slight” (pelvic variable; ĸ = 0.17, 95% CI -0.22-0.55) to “almost perfect” (foot variable; ĸ = 1.00, 95% CI 1.00–1.00; trunk variable; ĸ = 0.82, 95% CI 0.66–0.97). A generalised kappa coefficient including the values from all raters and segments reached “moderate” interrater reliability (ĸ = 0.52, 95% CI 0.43–0.61), the corresponding value for the intrarater reliability reached “almost perfect” (ĸ = 0.82, 95% CI 0.77–0.86).ConclusionsThe present study shows a “moderate” interrater reliability and an “almost perfect” intrarater reliability for the variable all segments regardless of the raters experience. Thus, we conclude that the proposed standardised multi-segmental SLS test is reliable enough to be used in an active population.

Relationship Between Movement Quality and Physical Performance in Elite Adolescent Australian Football Players.

Bennett, H, Fuller, J, Milanese, S, Jones, S, Moore, E, and Chalmers, S. The relationship between movement quality and physical performance in elite adolescent Australian football players. J Strength Cond Res 36(10): 2824-2829, 2022-The assessment of movement quality is commonplace in competitive sport to profile injury risk and guide exercise prescription. However, the relationship between movement quality scores and physical performance measures is unclear. Moreover, whether improvements in these measures are associated remain unknown. Over a 4-year period, 918 individual elite adolescent Australian Rules Footballers completed the Functional Movement Screen (FMS) and physical performance testing (5- and 20-m sprint, vertical jump, planned agility, and 20-m shuttle run test), allowing the analysis of relationships between FMS parameters and performance measures. In addition, 235 athletes completed testing over 2 consecutive years, allowing the analysis of relationships between changes in these outcomes. Small associations were observed between FMS composite score, hurdle step performance, in-line lunge performance, trunk stability push-up performance, rotary stability, and measures of speed, power, agility, and aerobic fitness (ρ = 0.071-0.238). Across consecutive seasons, significant improvements were observed in the deep squat subtest ( d = 0.21), FMS composite score ( d = 0.17), and 5- ( d = 0.16) and 20-m sprint times ( d = 0.39). A negative association between change in rotary stability and change in jump height (ρ = -0.236) from one season to the next was detected. Results suggest FMS scores have limited relationships with measures of performance in footballers. To optimize athletic performance, once acceptable movement capabilities have been established, training should not prioritize improving movement quality over improvements in strength, power, and change of direction ability.

Automated classification of movement quality using the Microsoft Kinect V2 sensor

Practitioners commonly perform movement quality assessment through qualitative assessment protocols, which can be time-intensive and prone to inter-rater measurement bias. The advent of portable and inexpensive marker-less motion capture systems can improve assessment through objective joint kinematic analysis. The current study aimed to evaluate various machine learning models that used kinematic features from Kinect position data to classify a performer's Movement Competency Screen (MCS) score. A Kinect V2 sensor collected position data from 31 physically active males as they performed bilateral squat, forward lunge, and single-leg squat; and the movement quality was rated according to the MCS criteria. Features were extracted and selected from domain knowledge-based kinematic variables as model input. Multiclass logistic regression (MLR) was then performed to translate joint kinematics into MCS score. Performance indicators were calculated after a 10-fold cross validation of each model developed from Kinect-based kinematic variables. The analyses revealed that the models' sensitivity, specificity, and accuracy ranged from 0.66 to 0.89, 0.58 to 0.86, and 0.74 to 0.85, respectively. In conclusion, the Kinect-based automated movement quality assessment is a suitable, novel, and practical approach to movement quality assessment.

Intertester and Intratester Reliability of Functional Movement Tests by Trained Testers in Community-Dwelling Older Adults With Knee Osteoarthritis: A Cross-sectional Study.

Movement quality of the lower extremities is considered an indicator of functional status in older adults with knee osteoarthritis (OA), and visual assessment of movement quality during functional movement tests can identify movement dysfunction and predict factors associated with lower extremity injuries. This study investigated the intertester and intratester reliability of trained testers for functional movement tests in community-dwelling older adults with knee OA. This study was a cross-sectional study. A total of 43 older adults with knee OA were recruited for this study and performed 2 functional movement tests: squat and step-down. Movement quality during the functional movement tests was visually rated in segmental (trunk, pelvic, knee, and foot) and overall regions and was scored from 0 (acceptable) to 3 (marked dysfunction). Percentage of agreement and weighted κ coefficients were used to explore the reliability between and within testers. The intertester reliability of the functional movement tests showed weak to strong agreement (weighted κ values from 0.44 to 0.88); the percentage agreement ranged from 52.78% to 93.02%. The intratester reliability of the functional movement tests showed weak to strong agreement (weighted κ values from 0.57 to 0.88); the percentage agreement ranged from 69.77% to 93.02%. Visual assessment of functional movement tests showed weak to strong intertester and intratester reliability to examine movement quality in community-dwelling older adults with knee OA. Thus, while it may be useful in a clinical setting to assess movement dysfunction segmentally and generally in older adults with knee OA; greater efforts would be needed to ensure high levels of agreement of functional movement tests.

Machine Learning for 3D Kinematic Analysis of Movements in Neurorehabilitation.

Recent advances in the machine learning field, especially in deep learning, provide the opportunity for automated, detailed, and unbiased analysis of motor behavior. Although there has not yet been wide use of these techniques in the motor rehabilitation field, they have great potential. In this review, I describe how the current state of machine learning can be applied to 3D kinematic analysis, and how this will have an impact on neurorehabilitation. Applications of deep learning methods, in the form of convolutional neural networks, have been revolutionary for image analysis such as face recognition and object detection in images, exceeding human level performance. Recent studies have shown applicability of these deep learning approaches to human posture and movement classification. It is to be expected that portable stereo-camera systems will bring 3D pose estimation into the clinical setting and allow the assessment of movement quality in response to interventions. Advances in machine learning can help automate the process of obtaining 3D kinematics of human movements and to identify/classify patterns of movement.

Human motion quality assessment toward sophisticated sports scenes based on deeply-learned 3D CNN model

Video may be subject to various distortions during acquisition, processing, compression, storage, transmission, and reproduction, and it results in reduced visual quality. In complex sports scenes under big data environment, the human body's movements are even more so. The quality of human motion can intuitively affect the human visual experience. Therefore, it is necessary to determine an intelligent quality assessment model to evaluate human motion in complex motion scenarios under big data environment. It can be used to dynamically monitor and adjust video quality, and it can be used for algorithms and parameter settings in motion image processing systems. With the popularity of deep learning, convolutional neural networks have become a very important method in the field of computer vision research. Based on the 2D-CNN algorithm, we propose a 3D convolutional neural network model for human motion quality assessment in complex motion scenarios. The model captures the pose characteristics, motion trajectory, video brightness and contrast in time and space. The model feeds back the reference and distorted video pairs into the network, with each output layer acting as a feature map. The local similarity between the feature maps obtained from the reference video and the distorted video is then calculated and combined to obtain a global image quality score. Experiments show that the model can achieve competitive performance in big data environment for video quality assessment.

Hierarchical Learning-Guided human motion quality assessment in big data environment

Image information may be distorted during acquisition, processing, compression, and transmission. It is necessary to propose an intelligent image quality assessment model toward big data environment to quantify the degree of distortion of the image. This paper proposes a quality assessment model for human motion images. In complex scenes, the human body's action posture can be taken as an important feature point. Usually, in different scenes, the parts that affect the quality of the human body's posture are different. In other words, the weights of feature points that affect quality are different in different scenarios. However, due to the categorization of human movements, we can learn the quality assessment methods of different types of movements through sample training. Inspired by feature learning in the field of machine learning, we propose a hierarchical quality learning approach. We cast quality assessment as quality feature learning and layer by layer. The hierarchical quality learning method is based on deep reinforcement learning. The key part is that the method focuses on the region that containing more information on the features of the quality and enlarges the region layer by layer. Finally, we can determine the part of the body that affects the quality assessment. We compare this method with the subjective quality assessment results of the human observers and find that the proposed method achieves effective performance in big data environment to evaluate human motion quality.

Concurrent validity of a wearable IMU for objective assessments of functional movement quality and control of the lumbar spine

Inertial measurement units (IMUs) are being recognized in clinical and rehabilitation settings for their ability to assess movement-related disorders of the spine for better guidance of treatment-planning and tracking of recovery. This study evaluated the Mbientlab MetaMotionR IMUs, relative to Vicon motion capture equipment in measuring local dynamic stability of the spine (quantified using maximum finite-time Lyapunov exponent; λmax), lumbopelvic coordination (quantified using mean absolute relative phase; MARP), and intersegmental motor variability (quantified using deviation phase; DP) of lumbopelvic segments in 10 participants during 35 cycles of repetitive spine flexion–extension (FE). Intraclass correlations were strong between systems when using both the FE angle time-series and the sum of squares (SS) time-series to measure local dynamic stability (0.807 ≤ICC2,1λmax,FE ≤ 0.919; 0.738 ≤ ICC2,1λmax,SS ≤ 0.868), sagittal-plane lumbopelvic coordination (0.961 ≤ICC2,1MARP ≤ 0.963), and sagittal-plane lumbopelvic variability (0.961 ≤ICC2,1DP ≤ 0.963). It was concluded that the MetaMotionR IMUs can be reliably used for measuring features associated with spine movement quality and motor control during a repetitive FE task. Future work will assess the reliability of sensor placement, performance during multi-directional movements, and ability to discern clinical and healthy populations based on assessment of movement quality and control.

Motion quality in rotator cuff tear using an inertial measurement unit: new parameters for dynamic motion assessment

This study aimed to (1) figure out the difference in motion smoothness between a nonpathologic shoulder and the other with a rotator cuff tear by measuring the angular velocity using an inertial measurement unit (IMU) sensor and (2) propose the parameters to describe the difference because there is no literature on this topic. We enrolled patients with rotator cuff disease diagnosed by magnetic resonance imaging. The intact shoulder of participants was compared with the shoulder with rotator cuff tear by using IMU on the basis of the parameters that establishmotion smoothness: the number of peaks, the peak velocity-to-mean velocity ratio (PV/MV), and the number of sign reversals. In addition, subgroup analysis was performed with respect to tear size (small to medium vs. large to massive). In addition, correlations with the American Shoulder and Elbow Surgeonsscore and symptom duration (months) were evaluated. Among 24 patients (15 males, 9 females), all 3 parameters for the motion quality of patients with a rotator cuff tear exhibited significant difference. The parameters showed a significant difference between the small to medium tear group and the large to massive tear group. A negative correlation was found between symptom duration and parameters of motion quality, except for PV/MV. Motion quality assessment using IMU showed a marked difference in the shoulder with a rotator cuff tear compared with the intact shoulder. Hence, IMU-based parameters for dynamic motion quality could be considered as an option for assessing the function of the shoulder.