Markerless motion capture systems as training device in neurological rehabilitation: a systematic review of their use, application, target population and efficacy

Effectiveness of proprioceptive neuromuscular facilitation exercise with virtual reality motion capture gaming system and concurrent feedback on early shoulder muscle activation in healthy individuals

This study explores the effect of proprioceptive neuromuscular facilitation (PNF) exercises combined with a virtual reality (VR) motion capture system and concurrent feedback (CF) on early shoulder muscle activation in healthy individuals. Thirty healthy participants sequentially performed three PNF diagonal 2 shoulder exercises: PNF alone, PNF with VR (PNF+VR), and PNF with VR and CF (PNF+VR+CF), with the latter two exercises presented in randomized order. Using wireless surface electromyography and 3-dimensonal (3D) inertial measurement units, the activation of the upper trapezius (UT), lower trapezius (LT), infraspinatus (INF), and serratus anterior (SA) muscles, as well as shoulder flexion, abduction, and external rotation range of motion, were recorded during three exercises. INF and LT muscles were activated earlier in abduction and flexion during PNF+VR and PNF+VR+CF exercises than in PNF alone (P<0.05). These muscles were also activated during the early external rotation phase during PNF alone (P<0.05). No statistically significant differences were observed in UT and SA muscle activation among PNF alone, PNF+VR, and PNF+VR+CF exercises (P>0.05). Integrating VR gaming elements and CF into PNF shoulder exercises may promote early muscle activation, offering a promising approach for advanced rehabilitation strategies that can potentially improve patient outcomes. VR motion capture systems have the potential to transform traditional therapeutic approaches by increasing enjoyment and engagement, which can, in turn, enhance patient motivation and adherence.

Concurrent validity and test reliability of the deep learning markerless motion capture system during the overhead squat

Marker-based optical motion capture systems have been used as a cardinal vehicle to probe and understand the underpinning mechanism of human posture and movement, but it is time-consuming for complex and delicate data acquisition and analysis, labor-intensive with highly trained operators. To mitigate such inherent issues, we developed an accurate and usable (5-min data collection and processing) deep-learning-based 3-Dimensional markerless motion capture system called "Ergo", designed for use in ecological digital healthcare environments. We investigated the concurrent validity and the test-retest reliability of the Ergo system measurement's whole body joint kinematics (time series joint angles and peak joint angles) data by comparing it with a standard marker-based motion capture system recorded during an overhead squat movement. The Ergo system demonstrated excellent agreement for time series joint angles ( R 2 = 0.88-0.99) and for peak joint angles ( I C C 2 , 1 = 0.75-1.0) when compared with the gold standard marker-based motion capture system. Additionally, we observed high test-retest reliability ( I C C 3 , 1 = 0.92-0.99). In conclusion, the deep learning-based markerless Ergo motion capture system considerably shows comparable performance with the Gold Standard marker-based motion capture system measurements in the concurrent accuracy, reliability, thereby making it a highly accessible choice for diverse universal users and ecological industries or environments.

Markerless Motion Capture to Quantify Functional Performance in Neurodegeneration: Systematic Review

BACKGROUND

Markerless motion capture (MMC) uses video cameras or depth sensors for full body tracking and presents a promising approach for objectively and unobtrusively monitoring functional performance within community settings, to aid clinical decision-making in neurodegenerative diseases such as dementia.

OBJECTIVE

The primary objective of this systematic review was to investigate the application of MMC using full-body tracking, to quantify functional performance in people with dementia, mild cognitive impairment, and Parkinson disease.

METHODS

A systematic search of the Embase, MEDLINE, CINAHL, and Scopus databases was conducted between November 2022 and February 2023, which yielded a total of 1595 results. The inclusion criteria were MMC and full-body tracking. A total of 157 studies were included for full-text screening, out of which 26 eligible studies that met the selection criteria were included in the review. .

RESULTS

Primarily, the selected studies focused on gait analysis (n=24), while other functional tasks, such as sit to stand (n=5) and stepping in place (n=1), were also explored. However, activities of daily living were not evaluated in any of the included studies. MMC models varied across the studies, encompassing depth cameras (n=18) versus standard video cameras (n=5) or mobile phone cameras (n=2) with postprocessing using deep learning models. However, only 6 studies conducted rigorous comparisons with established gold-standard motion capture models.

CONCLUSIONS

Despite its potential as an effective tool for analyzing movement and posture in individuals with dementia, mild cognitive impairment, and Parkinson disease, further research is required to establish the clinical usefulness of MMC in quantifying mobility and functional performance in the real world.

Comparison of the Clinical Effectiveness of Telerehabilitation with Traditional Rehabilitation Methods in Multiple Sclerosis Patients: A Systematic Review

Background: The rehabilitation process for multiple sclerosis (MS) patients is long and complex, which can lead to reduced rehabilitation outcomes and reduced quality improvement. Thus, there is a need to use new methods to boost traditional rehabilitation. Innovations such as telerehabilitation can be helpful to remove the obstacles to treatment, but evidence for their effectiveness is limited. The objective of this work was to compare the clinical effectiveness of telerehabilitation with traditional interventions in MS patients. Methods: Seven bibliographic databases (PubMed, Cochran Library, Scopus, Science Direct, Web of Science, Embase, and ProQuest) were used in this research. The initial search resulted in the extraction of 8,239 articles; after the review of the title, abstract, and full text, 11 articles were selected. In addition, backward reference list checking of the selected studies was conducted. Studies that were related to our objectives were included. Quality assessment was performed using the CONSORT checklist. Then, data extraction was done using the form set by the researcher in Word 2016 software. Results: Overall, telerehabilitation achieved more positive effects compared to traditional rehabilitation on physical (n = 6), cognitive (n = 3), cognitive, and physical outcomes (n = 2), respectively. Synchronous telerehabilitation was more effective than other modalities (n = 8). In four studies, virtual reality-based telerehabilitation was used. Also, telerehabilitation in home offered better clinical outcomes compared to rehabilitation center (n = 9). Conclusions: This review provides evidence for the potential effectiveness of telerehabilitation for the improvement of clinical outcomes in MS patients. However, more robust randomized controlled trials are needed to confirm the observed positive effects.

Wearable Loop Sensors for Knee Flexion Monitoring: Dynamic Measurements on Human Subjects

Goals: We have recently introduced a new class of wearable loop sensors for joint flexion monitoring that overcomes limitations in the state-of-the-art. Our previous studies reported a proof-of-concept on a cylindrical phantom limb, under static scenarios and with a rigid sensor. In this work, we evaluate our sensors, for the first time, on human subjects, under dynamic scenarios, using a flexible textile-based prototype tethered to a network analyzer. An untethered version is also presented and validated on phantoms, aiming towards a fully wearable design. Methods: Three dynamic activities (walking, brisk walking, and full flexion/extension, all performed in place) are used to validate the tethered sensor on ten (10) adults. The untethered sensor is validated upon a cylindrical phantom that is bent manually at random speed. A calibration mechanism is developed to derive the sensor-measured angles. These angles are then compared to gold-standard angles simultaneously captured by a light detection and ranging (LiDAR) depth camera using root mean square error (RMSE) and Pearson's correlation coefficient as metrics. Results: We find excellent correlation (≥ 0.981) to gold-standard angles. The sensor achieves an RMSE of 4.463° ± 1.266° for walking, 5.541° ± 2.082° for brisk walking, 3.657° ± 1.815° for full flexion/extension activities, and 0.670° ± 0.366° for the phantom bending test. Conclusion: The tethered sensor achieves similar to slightly higher RMSE as compared to other wearable flexion sensors on human subjects, while the untethered version achieves excellent RMSE on the phantom model. Concurrently, our sensors are reliable over time and injury-safe, and do not obstruct natural movement. Our results set the ground for future improvements in angular resolution and for realizing fully wearable designs, while maintaining the abovementioned advantages over the state-of-the-art.

Kinematic synergies show good consistency when extracted with a low-cost markerless device and a marker-based motion tracking system

Recently, markerless tracking systems, such as RGB-Depth cameras, have spread to overcome some of the limitations of the gold standard marker-based tracking systems. Although these systems are valuable substitutes for human motion analysis, as they guarantee higher flexibility, faster setup time and lower costs, their tracking accuracy is lower with respect to marker-based systems. Many studies quantified the error made by markerless systems in terms of body segment length estimation, articular angles, and biomechanics, concluding that they are appropriate for many clinical applications related to motion analysis. We propose an innovative approach to compare a markerless tracking system (Kinect V2) with a gold standard marker-based system (Vicon), based on motor control assessment. We quantified kinematic synergies from the tracking data of fifteen participants performing multi-directional upper limb movements. Kinematic synergy analysis decomposes the kinematic data into a reduced set of motor primitives that describe how the central nervous system coordinates motion at spatial and temporal level. Synergies were extracted with the recently released mixed-matrix factorization algorithm. Four synergies were extracted from both marker-based and markerless datasets and synergies were grouped in 6 clusters for each dataset. Cosine similarity in each cluster was ⩾0.60 in both systems, showing good consistency of synergies. Good matching was found between synergies extracted from markerless and from marker-based data, with a cosine similarity between matched synergies ⩾0.60 in 5 out 6 synergies. These results showed that the markerless sensor can be feasible for kinematic synergy analysis for gross movements, as it correctly estimates the number of synergies and in most cases also their spatial and temporal organization.

Comparison of kinematics and joint moments calculations for lower limbs during gait using markerless and marker-based motion capture

Objective: This study aimed at quantifying the difference in kinematic and joint moments calculation for lower limbs during gait utilizing a markerless motion system (TsingVA Technology, Beijing, China) in comparison to values estimated using a marker-based motion capture system (Nokov Motion Capture System, Beijing, China). Methods: Sixteen healthy participants were recruited for the study. The kinematic data of the lower limb during walking were acquired simultaneously based on the markerless motion capture system (120 Hz) and the marker-based motion capture system (120 Hz). The ground reaction force was recorded synchronously using a force platform (1,200 Hz). The kinematic and force data were input into Visual3D for inverse dynamics calculations. Results: The difference in the lower limb joint center position between the two systems was the least at the ankle joint in the posterior/anterior direction, with the mean absolute deviation (MAD) of 0.74 cm. The least difference in measuring lower limb angles between the two systems was found in flexion/extension movement, and the greatest difference was found in internal/external rotation movement. The coefficient of multiple correlations (CMC) of the lower limb three joint moments for both systems exceeded or equaled 0.75, except for the ad/abduction of the knee and ankle. All the Root Mean Squared Deviation (RMSD) of the lower limb joint moment are below 18 N·m. Conclusion: The markerless motion capture system and marker-based motion capture system showed a high similarity in kinematics and inverse dynamic calculation for lower limbs during gait in the sagittal plane. However, it should be noted that there is a notable deviation in ad/abduction moments at the knee and ankle.

Wearable Loop Sensor for Bilateral Knee Flexion Monitoring

We have previously reported wearable loop sensors that can accurately monitor knee flexion with unique merits over the state of the art. However, validation to date has been limited to single-leg configurations, discrete flexion angles, and in vitro (phantom-based) experiments. In this work, we take a major step forward to explore the bilateral monitoring of knee flexion angles, in a continuous manner, in vivo. The manuscript provides the theoretical framework of bilateral sensor operation and reports a detailed error analysis that has not been previously reported for wearable loop sensors. This includes the flatness of calibration curves that limits resolution at small angles (such as during walking) as well as the presence of motional electromotive force (EMF) noise at high angular velocities (such as during running). A novel fabrication method for flexible and mechanically robust loops is also introduced. Electromagnetic simulations and phantom-based experimental studies optimize the setup and evaluate feasibility. Proof-of-concept in vivo validation is then conducted for a human subject performing three activities (walking, brisk walking, and running), each lasting 30 s and repeated three times. The results demonstrate a promising root mean square error (RMSE) of less than 3° in most cases.

A global bibliometric and visualized analysis of gait analysis and artificial intelligence research from 1992 to 2022

Gait is an important basic function of human beings and an integral part of life. Many mental and physical abnormalities can cause noticeable differences in a person's gait. Abnormal gait can lead to serious consequences such as falls, limited mobility and reduced life satisfaction. Gait analysis, which includes joint kinematics, kinetics, and dynamic Electromyography (EMG) data, is now recognized as a clinically useful tool that can provide both quantifiable and qualitative information on performance to aid in treatment planning and evaluate its outcome. With the assistance of new artificial intelligence (AI) technology, the traditional medical environment has undergone great changes. AI has the potential to reshape medicine, making gait analysis more accurate, efficient and accessible. In this study, we analyzed basic information about gait analysis and AI articles that met inclusion criteria in the WoS Core Collection database from 1992-2022, and the VosViewer software was used for web visualization and keyword analysis. Through bibliometric and visual analysis, this article systematically introduces the research status of gait analysis and AI. We introduce the application of artificial intelligence in clinical gait analysis, which affects the identification and management of gait abnormalities found in various diseases. Machine learning (ML) and artificial neural networks (ANNs) are the most often utilized AI methods in gait analysis. By comparing the predictive capability of different AI algorithms in published studies, we evaluate their potential for gait analysis in different situations. Furthermore, the current challenges and future directions of gait analysis and AI research are discussed, which will also provide valuable reference information for investors in this field.

Clinical assessment of upper limb impairments and functional capacity in Parkinson's disease: a systematic review

BACKGROUND

 Parkinson's disease (PD) may progressively reduce the upper limb's functionality. Currently, there is no standardized upper limb functional capacity assessment in PD in the rehabilitation field.

OBJECTIVE

 To identify specific outcome measurements to assess upper limbs in PD and access functional capacity.

METHODS

 We systematically reviewed and analyzed the literature in English published from August/2012 to August/2022 according to PRISMA. The following keywords were used in our search: "upper limbs" OR "upper extremity" and "Parkinson's disease." Two researchers searched independently, including studies accordingly to our inclusion and exclusion criteria. Registered at PROSPERO CRD42021254486.

RESULTS

 We found 797 studies, and 50 were included in this review (n = 2.239 participants in H&Y stage 1-4). The most common upper limbs outcome measures found in the studies were: (i) UPDRS-III and MDS-UPDRS to assess the severity and progression of PD motor symptoms (tremor, bradykinesia, and rigidity) (ii) Nine Hole Peg Test and Purdue Pegboard Test to assess manual dexterity; (iii) Spiral test and Funnel test to provoke and assess freezing of upper limbs; (iv) Technology assessment such as wearables sensors, apps, and other device were also found.

CONCLUSION

 We found evidence to support upper limb impairments assessments in PD. However, there is still a large shortage of specific tests to assess the functional capacity of the upper limbs. The upper limbs' functional capacity is insufficiently investigated during the clinical and rehabilitation examination due to a lack of specific outcome measures to assess functionality.

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.

Approaches for Hybrid Coregistration of Marker-Based and Markerless Coordinates Describing Complex Body/Object Interactions

Full-body motion capture is essential for the study of body movement. Video-based, markerless, mocap systems are, in some cases, replacing marker-based systems, but hybrid systems are less explored. We develop methods for coregistration between 2D video and 3D marker positions when precise spatial relationships are not known a priori. We illustrate these methods on three-ball cascade juggling in which it was not possible to use marker-based tracking of the balls, and no tracking of the hands was possible due to occlusion. Using recorded video and motion capture, we aimed to transform 2D ball coordinates into 3D body space as well as recover details of hand motion. We proposed four linear coregistration methods that differ in how they optimize ball-motion constraints during hold and flight phases, using an initial estimate of hand position based on arm and wrist markers. We found that minimizing the error between ball and hand estimate was globally suboptimal, distorting ball flight trajectories. The best-performing method used gravitational constraints to transform vertical coordinates and ball-hold constraints to transform lateral coordinates. This method enabled an accurate description of ball flight as well as a reconstruction of wrist movements. We discuss these findings in the broader context of video/motion capture coregistration.

Conversion of Upper-Limb Inertial Measurement Unit Data to Joint Angles: A Systematic Review

Inertial measurement units (IMUs) have become the mainstay in human motion evaluation outside of the laboratory; however, quantification of 3-dimensional upper limb motion using IMUs remains challenging. The objective of this systematic review is twofold. Firstly, to evaluate computational methods used to convert IMU data to joint angles in the upper limb, including for the scapulothoracic, humerothoracic, glenohumeral, and elbow joints; and secondly, to quantify the accuracy of these approaches when compared to optoelectronic motion analysis. Fifty-two studies were included. Maximum joint motion measurement accuracy from IMUs was achieved using Euler angle decomposition and Kalman-based filters. This resulted in differences between IMU and optoelectronic motion analysis of 4° across all degrees of freedom of humerothoracic movement. Higher accuracy has been achieved at the elbow joint with functional joint axis calibration tasks and the use of kinematic constraints on gyroscope data, resulting in RMS errors between IMU and optoelectronic motion for flexion-extension as low as 2°. For the glenohumeral joint, 3D joint motion has been described with RMS errors of 6° and higher. In contrast, scapulothoracic joint motion tracking yielded RMS errors in excess of 10° in the protraction-retraction and anterior-posterior tilt direction. The findings of this study demonstrate high-quality 3D humerothoracic and elbow joint motion measurement capability using IMUs and underscore the challenges of skin motion artifacts in scapulothoracic and glenohumeral joint motion analysis. Future studies ought to implement functional joint axis calibrations, and IMU-based scapula locators to address skin motion artifacts at the scapula, and explore the use of artificial neural networks and data-driven approaches to directly convert IMU data to joint angles.

Personalized Use of an Adjustable Movement-Controlled Video Game in Obstetric Brachial Plexus Palsy during Physiotherapy Sessions at School: A Case Report

This case study explores the use of a personalized, adjustable Kinect exergame in 10 physiotherapy sessions for a 10-year-old girl with incomplete right-sided obstetric brachial plexus palsy (OBPP). The aim was to observe the impact on the patient's upper limb mobility that could be achieved through maximization of the player's motivation, possibly due to continuous game parameter adjustments. It had been achieved that the patient was playing 87% of the total gaming time with a personally challenging setting that increased her arm speed from one to four movements. Strength in abduction and flexion were increased by 8 N and 7 N, respectively. Furthermore, the patient showed better muscular balance and an increase of 50% in speed of the Jebsen-Taylor hand function test (JTHFT). The patient reported high levels of motivation, low perception of fatigue, and just slight discomfort. The study found that the use of personalized video games as a complement to conventional physiotherapy can be successful in OBPP patients when the game allows for the adjustment of the difficulty level as a response to personal performance. Predefined difficulty levels and automatic performance analysis can be helpful. Results are promising; however, further research is needed to confirm the results.

Deep Learning for Multiple Sclerosis Differentiation Using Multi-Stride Dynamics in Gait

OBJECTIVE

Multiple sclerosis (MS) is a chronic neurological condition of the central nervous system leading to various physical, mental and psychiatric complexities. Mobility limitations are amongst the most frequent and early markers of MS. We evaluated the effectiveness of a DeepMS2G (deep learning (DL) for MS differentiation using multistride dynamics in gait) framework, which is a DL-based methodology to classify multi-stride sequences of persons with MS (PwMS) from healthy controls (HC), in order to generalize over newer walking tasks and subjects.

METHODS

We collected single-task Walking and dual-task Walking-while-Talking gait data using an instrumented treadmill from a balanced collection of 20 HC and 20 PwMS. We utilized domain knowledge-based spatiotemporal and kinetic gait features along with two normalization schemes, namely standard size-based and multiple regression normalization strategies. To differentiate between multi-stride sequences of HC and PwMS, we compared 16 traditional machine learning and DL algorithms. Further, we studied the interpretability of our highest-performing models; and discussed the association between the lower extremity function of participants and our model predictions.

RESULTS

We observed that residual neural network (ResNet) based models with regression-based normalization were the top performers across both task and subject generalization classification designs. Considering regression-based normalization, a multi-scale ResNet attained a subject classification accuracy and F 1-score of 1.0 when generalizing from single-task Walking to dual-task Walking-while-Talking; and a ResNet resulted in the top subject-wise accuracy and F 1 of 0.83 and 0.81 (resp.), when generalizing over unseen participants.

CONCLUSION

We used advanced DL and dynamics across domain knowledge-based spatiotemporal and kinetic gait parameters to successfully classify MS gait across distinct walking trials and unseen participants.

SIGNIFICANCE

Our proposed DL algorithms might contribute to efforts to automate MS diagnoses.

Parkinson's disease therapy: what lies ahead?

The worldwide prevalence of Parkinson's disease (PD) has been constantly increasing in the last decades. With rising life expectancy, a longer disease duration in PD patients is observed, further increasing the need and socioeconomic importance of adequate PD treatment. Today, PD is exclusively treated symptomatically, mainly by dopaminergic stimulation, while efforts to modify disease progression could not yet be translated to the clinics. New formulations of approved drugs and treatment options of motor fluctuations in advanced stages accompanied by telehealth monitoring have improved PD patients care. In addition, continuous improvement in the understanding of PD disease mechanisms resulted in the identification of new pharmacological targets. Applying novel trial designs, targeting of pre-symptomatic disease stages, and the acknowledgment of PD heterogeneity raise hopes to overcome past failures in the development of drugs for disease modification. In this review, we address these recent developments and venture a glimpse into the future of PD therapy in the years to come.

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.

A systematic review of the applications of markerless motion capture (MMC) technology for clinical measurement in rehabilitation

BACKGROUND

Markerless motion capture (MMC) technology has been developed to avoid the need for body marker placement during motion tracking and analysis of human movement. Although researchers have long proposed the use of MMC technology in clinical measurement-identification and measurement of movement kinematics in a clinical population, its actual application is still in its preliminary stages. The benefits of MMC technology are also inconclusive with regard to its use in assessing patients' conditions. In this review we put a minor focus on the method's engineering components and sought primarily to determine the current application of MMC as a clinical measurement tool in rehabilitation.

METHODS

A systematic computerized literature search was conducted in PubMed, Medline, CINAHL, CENTRAL, EMBASE, and IEEE. The search keywords used in each database were "Markerless Motion Capture OR Motion Capture OR Motion Capture Technology OR Markerless Motion Capture Technology OR Computer Vision OR Video-based OR Pose Estimation AND Assessment OR Clinical Assessment OR Clinical Measurement OR Assess." Only peer-reviewed articles that applied MMC technology for clinical measurement were included. The last search took place on March 6, 2023. Details regarding the application of MMC technology for different types of patients and body parts, as well as the assessment results, were summarized.

RESULTS

A total of 65 studies were included. The MMC systems used for measurement were most frequently used to identify symptoms or to detect differences in movement patterns between disease populations and their healthy counterparts. Patients with Parkinson's disease (PD) who demonstrated obvious and well-defined physical signs were the largest patient group to which MMC assessment had been applied. Microsoft Kinect was the most frequently used MMC system, although there was a recent trend of motion analysis using video captured with a smartphone camera.

CONCLUSIONS

This review explored the current uses of MMC technology for clinical measurement. MMC technology has the potential to be used as an assessment tool as well as to assist in the detection and identification of symptoms, which might further contribute to the use of an artificial intelligence method for early screening for diseases. Further studies are warranted to develop and integrate MMC system in a platform that can be user-friendly and accurately analyzed by clinicians to extend the use of MMC technology in the disease populations.

Computer-assisted approaches for measuring, segmenting, and analyzing functional upper extremity movement: a narrative review of the current state, limitations, and future directions

The analysis of functional upper extremity (UE) movement kinematics has implications across domains such as rehabilitation and evaluating job-related skills. Using movement kinematics to quantify movement quality and skill is a promising area of research but is currently not being used widely due to issues associated with cost and the need for further methodological validation. Recent developments by computationally-oriented research communities have resulted in potentially useful methods for evaluating UE function that may make kinematic analyses easier to perform, generally more accessible, and provide more objective information about movement quality, the importance of which has been highlighted during the COVID-19 pandemic. This narrative review provides an interdisciplinary perspective on the current state of computer-assisted methods for analyzing UE kinematics with a specific focus on how to make kinematic analyses more accessible to domain experts. We find that a variety of methods exist to more easily measure and segment functional UE movement, with a subset of those methods being validated for specific applications. Future directions include developing more robust methods for measurement and segmentation, validating these methods in conjunction with proposed kinematic outcome measures, and studying how to integrate kinematic analyses into domain expert workflows in a way that improves outcomes.

Finding Effective Adjustment Levels for Upper Limb Exergames: Focus Group Study With Children With Physical Disabilities

BACKGROUND

We developed the Blexer system consisting of a database and a web interface for therapists that can host different types of adaptive and personally configurable virtual reality exergames based on Kinect (Microsoft Corp) motion capture to provide entertaining exercises for children with motor disabilities. It allows for parameter adjustment and the monitoring of results remotely, thereby providing a useful tool to complement traditional physical therapy sessions with home exercises.

OBJECTIVE

The aim of this study was to observe the motor benefits achieved through the use of a video exergame and the importance and implications of correctly setting the game's difficulty parameters.

METHODS

This was an observational case study of 6 children with different physical disabilities receiving physical therapy at school combined with the use of a fully configurable exergame under research that forms a part of the Blexer environment. The game integrates 4 repeatedly appearing upper limb exercises with individually adjustable difficulties (intermittent arm rising, arm forward and backward movement, rising and holding of one arm, and trunk control in all directions). The outcomes were 3 assessments of 2 efficacy measures: Box and Block Test and Jebsen Taylor Hand Function Test.

RESULTS

A total of 5 children with cerebral palsy (mean 8.4, SD 2.7 years; Gross Motor Function Classification II-2/5, 40%; III-2/5, 40%; and IV-1/5, 20%) and 1 child with obstetric brachial plexus palsy (aged 8 years; Mallet Classification III) received between 8 and 11 sessions of training (10-20 minutes per session), depending on age, motivation, and fatigue. Significant associations were observed between game parameter settings and improvements in motor function, on the one hand, and between the type of improvement and disability severity, on the other: with adjusted game parameters goal and time in the range of 70% to 100%, only less affected children improved in the Box and Block Test (+11 blocks vs -1 block), and more affected children improved more in the Jebsen Taylor Hand Function Test (+90 seconds vs +27 seconds).

CONCLUSIONS

When defining the difficulty parameters for an exergame, we suggest a classification in levels ranging from very easy to very hard. For practical use, we suggest setting the difficulty for the player to an easy or medium level rather than high-commitment goals, as this leads to a longer playtime with more fun and, therefore, seems to improve the results of the game and, consequently, mobility.

Feasibility of a combined intermittent theta-burst stimulation and video game-based dexterity training in Parkinson's disease

BACKGROUND

Persons with Parkinson's disease (PD) often exhibit difficulties with dexterity during the performance of activities of daily living (ADL), inter alia due to dysfunctional supplementary motor area (SMA). Combined intermittent theta-burst stimulation (iTBS) over the SMA followed by video game-based training (VBT) may therefore improve dexterity related ADL. The VBT may induce high flow levels related to high performance during the training. The aim of this study is to evaluate the feasibility of a combined iTBS-VBT intervention in persons with PD.

METHODS

A total of nine persons with PD (mean age 63.3 ± 8.76 years) with self-reported difficulties with dexterity related ADL were included in this pilot iTBS-VBT study. All participants received either iTBS or sham stimulation over the SMA followed by a 45-min VBT, three times a week for a total of three weeks. Feasibility was measured by means of the adherence rate and the system usability (System Usability Scale). Moreover, flow was measured after the last VBT session.

RESULTS

Adherence rate was excellent with 100%. High system usability scores (i.e., mean 80%, range 55-97.5) and a significant Spearman's correlation with the Flow State Scale (r = .762, p = .017) further point to the high feasibility of the VBT. Neither demographic variables nor difficulties in dexterity related ADL affected the usability of the VBT.

CONCLUSION

This study demonstrates the high feasibility of a combined iTBS-VBT intervention. Moreover, the level of self-reported usability was related to flow experience. Whether this kind of combined iTBS-VBT intervention improves dexterity will be evaluated in a randomized controlled trial. Trial registration clincaltrials.gov NCT04699149, date of registration 1. June 2021.

Nintendo Switch Joy-Cons' Infrared Motion Camera Sensor for Training Manual Dexterity in People with Multiple Sclerosis: A Randomized Controlled Trial

BACKGROUND

The Nintendo Switch^® (NS) is the ninth video game console developed by Nintendo^®. Joy-Cons^® are the primary game controllers for the NS^® video game console, and they have an infrared motion camera sensor that allows capturing the patient's hands without the need to place sensors or devices on the body. The primary aim of the present study was to evaluate the effects of the NS^®, combined with a conventional intervention, for improving upper limb (UL) grip muscle strength, coordination, speed of movements, fine and gross dexterity, functionality, quality of life, and executive function in multiple sclerosis (MS) patients. Furthermore, we sought to assess satisfaction and compliance levels.

METHODS

A single-blinded, randomized clinical trial was conducted. The sample was randomized into two groups: an experimental group who received treatment based on Dr Kawashima's Brain Training^® for the NS^® (20 min) plus conventional rehabilitation (40 min), and a control group who received the same conventional rehabilitation (60 min) for the ULs. Both groups received two 60 min sessions per week over an eight-week period. Grip strength, the Box and Blocks Test (BBT), the Nine Hole Peg Test (NHPT), the QuickDASH, the Multiple Sclerosis Impact Scale (MSIS-29), the Trail Making Test (TMT), and the Stroop Color and Word Test (SCWT) were used pre- and post-treatment. Side effects and attendance rates were also recorded.

RESULTS

Intragroup analysis showed significant improvements for the experimental group in the post-treatment assessments for grip strength in the more affected side (p = 0.033), the BBT for the more (p = 0.030) and the less affected side (p = 0.022), the TMT (A section) (p = 0.012), and the QuickDASH (p = 0.017). No differences were observed for the control group in intragroup analysis, but they were observed in the NHPT for the more affected side (p = 0.012). The intergroup analysis did not show differences between both groups.

CONCLUSIONS

Our results show that an eight-week experimental protocol, after using Dr Kawashima's Brain Training^® and the right-side Joy-Con controller for the NS^®, combined with a conventional intervention, showed improvements in grip strength, coordination, fine and gross motor function, executive functions, and upper limb functionality in the experimental group. However, no differences were observed when both groups were compared in the intergroup analysis. The addition of Brain Training^® for the NS^® for the upper limb rehabilitation did not show side effects and was rated with a high satisfaction and excellent compliance in people with MS.

TRIAL REGISTRATION

This randomized controlled trial has been registered at ClinicalTrials Identifier: NCT04171908, November 2019.

Kinect-Based Rehabilitation Systems for Stroke Patients: A Scoping Review

Method

This study was conducted according to Arksey and O'Malley's framework. To investigate the evidence on the effects of Kinect-based rehabilitation, a search was executed in five databases (Web of Science, PubMed, Cochrane Library, Scopus, and IEEE) from 2010 to 2020.

Results

Thirty-three articles were finally selected by the inclusion criteria. Most of the studies had been conducted in the US (22%). In terms of the application of Kinect-based rehabilitation for stroke patients, most studies had focused on the rehabilitation of upper extremities (55%), followed by balance (27%). The majority of the studies had developed customized rehabilitation programs (36%) for the rehabilitation of stroke patients. Most of these studies had noted that the simultaneous use of Kinect-based rehabilitation and other physiotherapy methods has a more noticeable effect on performance improvement in patients.

Conclusion

The simultaneous application of Kinect-based rehabilitation and other physiotherapy methods has a stronger effect on the performance improvement of stroke patients. Better effects can be achieved by designing Kinect-based rehabilitation programs tailored to the characteristics and abilities of stroke patients.

Machine Learning-Based Analysis of Digital Movement Assessment and ExerGame Scores for Parkinson's Disease Severity Estimation

Neurodegenerative Parkinson's Disease (PD) is one of the common incurable diseases among the elderly. Clinical assessments are characterized as standardized means for PD diagnosis. However, relying on medical evaluation of a patient's status can be subjective to physicians' experience, making the assessment process susceptible to human errors. The use of ICT-based tools for capturing the status of patients with PD can provide more objective and quantitative metrics. In this vein, the Personalized Serious Game Suite (PGS) and intelligent Motor Assessment Tests (iMAT), produced within the i-PROGNOSIS European project (www.i-prognosis.eu), are explored in the current study. More specifically, data from 27 patients with PD at Stage 1 (9) and Stage 3 (18) produced from their interaction with PGS/iMAT are analyzed. Five feature vector (FV) scenarios are set, including features from PGS or iMAT scores or their combination, after also taking into consideration the age of patients with PD. These FVs are fed into three machine learning classifiers, i.e., K-Nearest Neighbor (KNN), Support Vector Machines (SVM), and Random Forest (RF), to infer the stage of each patient with PD. A Leave-One-Out Cross-Validation (LOOCV) method is adopted for testing the classification performance. The experimental results show that a high (>90%) classification accuracy is achieved from both data sources (PGS/iMAT), justifying the effectiveness of PGS/iMAT to efficiently reflect the motor skill status of patients with PD and further potentiating PGS/iMAT enhancement with a machine learning a part to infer for the stage of patients with PD. Clearly, this integrated approach provides new opportunities for remote monitoring of the stage of patients with PD, contributing to a more efficient organization and set up of personalized interventions.

Feasibility and Efficacy of a Virtual Reality Game-Based Upper Extremity Motor Function Rehabilitation Therapy in Patients with Chronic Stroke: A Pilot Study

BACKGROUND

The objective of the present study was to develop a virtual reality protocol based on activities of daily living and conventional rehabilitation, using Leap Motion Controller to improve motor function in upper extremity rehabilitation in stroke patients. At the same time, the purpose was to explore its efficacy in the recovery of upper extremity motor function in chronic stroke survivors, and to determine feasibility, satisfaction and attendance rate; Methods: A prospective pilot experimental clinical trial was conducted. The outcome measures used were the grip strength, the Action Research Arm Test (ARAT), the Block and Box Test (BBT), the Short Form Health Survey-36 Questionnaire, a satisfaction questionnaire and attendance rate; Results: Our results showed statistically significant changes in the variables grip strength, BBT and ARAT as well as high levels of satisfaction and attendance; Conclusions: This virtual reality platform represents an effective tool in aspects of upper extremity functionality rehabilitation in patients with chronic stroke, demonstrating feasibility and high levels of attendance and satisfaction.

Applications and limitations of current markerless motion capture methods for clinical gait biomechanics

BACKGROUND

Markerless motion capture has the potential to perform movement analysis with reduced data collection and processing time compared to marker-based methods. This technology is now starting to be applied for clinical and rehabilitation applications and therefore it is crucial that users of these systems understand both their potential and limitations. This literature review aims to provide a comprehensive overview of the current state of markerless motion capture for both single camera and multi-camera systems. Additionally, this review explores how practical applications of markerless technology are being used in clinical and rehabilitation settings, and examines the future challenges and directions markerless research must explore to facilitate full integration of this technology within clinical biomechanics.

METHODOLOGY

A scoping review is needed to examine this emerging broad body of literature and determine where gaps in knowledge exist, this is key to developing motion capture methods that are cost effective and practically relevant to clinicians, coaches and researchers around the world. Literature searches were performed to examine studies that report accuracy of markerless motion capture methods, explore current practical applications of markerless motion capture methods in clinical biomechanics and identify gaps in our knowledge that are relevant to future developments in this area.

RESULTS

Markerless methods increase motion capture data versatility, enabling datasets to be re-analyzed using updated pose estimation algorithms and may even provide clinicians with the capability to collect data while patients are wearing normal clothing. While markerless temporospatial measures generally appear to be equivalent to marker-based motion capture, joint center locations and joint angles are not yet sufficiently accurate for clinical applications. Pose estimation algorithms are approaching similar error rates of marker-based motion capture, however, without comparison to a gold standard, such as bi-planar videoradiography, the true accuracy of markerless systems remains unknown.

CONCLUSIONS

Current open-source pose estimation algorithms were never designed for biomechanical applications, therefore, datasets on which they have been trained are inconsistently and inaccurately labelled. Improvements to labelling of open-source training data, as well as assessment of markerless accuracy against gold standard methods will be vital next steps in the development of this technology.

Efficacy of a technology-based client-centred training system in neurological rehabilitation: a randomised controlled trial

Background

A client-centred task-oriented approach has advantages towards motivation and adherence to therapy in neurorehabilitation, but it is costly to integrate in practice. An intelligent Activity-based Client-centred Training (i-ACT), a low-cost Kinect-based system, was developed which integrates a client-centred and task-oriented approach. The objectives were (1) to investigate the effect of additional i-ACT training on functioning. And (2) to assess whether training with i-ACT resulted in more goal oriented training.

Methods

A single-blind randomised controlled trial was performed in 4 Belgian rehabilitation centres with persons with central nervous system deficits. Participants were randomly allocated through an independent website-based code generator using blocked randomisation (n = 4) to an intervention or control group. The intervention group received conventional care and additional training with i-ACT for 3 × 45 min/week during 6 weeks. The control group received solely conventional care. Functional ability and performance, quality of life (QoL), fatigue, trunk movement, and shoulder active range of motion (AROM) were assessed at baseline, after 3 weeks and 6 weeks of training, and 6 weeks after cessation of training. Data were analysed using non-parametric within and between group analysis.

Results

47 persons were randomised and 45 analysed. Both intervention (n = 25) and control (n = 22) group improved over time on functional ability and performance as measured by the Wolf Motor Function Test, Manual Ability Measure-36, and Canadian Occupational Performance Measure, but no major differences were found between the groups on these primary outcome measures. Regarding QoL, fatigue, trunk movement, and shoulder AROM, no significant between group differences were found. High adherence for i-ACT training was found (i.e. 97.92%) and no adverse events, linked to i-ACT, were reported. In the intervention group the amount of trained personal goals (88%) was much higher than in the control group (46%).

Conclusions

Although additional use of i-ACT did not have a statistically significant added value regarding functional outcome over conventional therapy, additional i-ACT training provides more individualised client-centred therapy, and adherence towards i-ACT training is high. A higher intensity of i-ACT training may increase therapy effects, and should be investigated in future research.

Trial registration: ClinicalTrials.gov Identifier NCT02982811. Registered 29 November 2016.

Co-designing a remote rehabilitation tool for Parkinson's disease: exploratory values and challenges

BACKGROUND

Impaired motor function is one of the early symptoms shown in patients with Parkinson Disease (PD). For this reason, rehabilitative interventions have been used for many years to improve motor and non-motor symptoms. Among them, the use of music therapy has shown benefits in helping to overcome some of the most common motor dysfunction. Addressing the challenge of providing access to this type of therapy, this document presents the collaborative design process to develop a remote training support tool for PD based on music therapy.

METHODS

A qualitative study with creative co-design methods was used in which different groups of healthcare professionals, patients, and relatives participated in six iterative sessions. Workshops were designed and structured to incrementally discover requirements and needs and validate the proposed prototype ideas.

RESULTS

The study provided key aspects that were used for the development and validation of the proposed prototypes for the remote music-based training support tool for PD. Up to 20 factors that had a positive and/or negative influence on patient access to training were detected. These factors were classified into three common themes: daily activities and independence, participation in treatment and barriers to daily treatment, and self-management and personalization of information and telecommunication technologies (ICT).

CONCLUSIONS

This paper shows the results of a collaborative design process aimed at identifying the different factors, relevant to patients with PD, to improve their access to remote ICT-based training therapy and their expectations regarding alternative therapies, such as music. The participatory design methods and the iterative model used helped overcome many of the traditionally barriers that this type of technological support solutions usually have, facilitating the future participation.

The Reliability of the Microsoft Kinect and Ambulatory Sensor-Based Motion Tracking Devices to Measure Shoulder Range-of-Motion: A Systematic Review and Meta-Analysis

Advancements in motion sensing technology can potentially allow clinicians to make more accurate range-of-motion (ROM) measurements and informed decisions regarding patient management. The aim of this study was to systematically review and appraise the literature on the reliability of the Kinect, inertial sensors, smartphone applications and digital inclinometers/goniometers to measure shoulder ROM. Eleven databases were screened (MEDLINE, EMBASE, EMCARE, CINAHL, SPORTSDiscus, Compendex, IEEE Xplore, Web of Science, Proquest Science and Technology, Scopus, and PubMed). The methodological quality of the studies was assessed using the consensus-based standards for the selection of health Measurement Instruments (COSMIN) checklist. Reliability assessment used intra-class correlation coefficients (ICCs) and the criteria from Swinkels et al. (2005). Thirty-two studies were included. A total of 24 studies scored "adequate" and 2 scored "very good" for the reliability standards. Only one study scored "very good" and just over half of the studies (18/32) scored "adequate" for the measurement error standards. Good intra-rater reliability (ICC > 0.85) and inter-rater reliability (ICC > 0.80) was demonstrated with the Kinect, smartphone applications and digital inclinometers. Overall, the Kinect and ambulatory sensor-based human motion tracking devices demonstrate moderate-good levels of intra- and inter-rater reliability to measure shoulder ROM. Future reliability studies should focus on improving study design with larger sample sizes and recommended time intervals between repeated measurements.

Portable, open-source solutions for estimating wrist position during reaching in people with stroke

Arm movement kinematics may provide a more sensitive way to assess neurorehabilitation outcomes than existing metrics. However, measuring arm kinematics in people with stroke can be challenging for traditional optical tracking systems due to non-ideal environments, expense, and difficulty performing required calibration. Here, we present two open-source methods, one using inertial measurement units (IMUs) and another using virtual reality (Vive) sensors, for accurate measurements of wrist position with respect to the shoulder during reaching movements in people with stroke. We assessed the accuracy of each method during a 3D reaching task. We also demonstrated each method's ability to track two metrics derived from kinematics-sweep area and smoothness-in people with chronic stroke. We computed correlation coefficients between the kinematics estimated by each method when appropriate. Compared to a traditional optical tracking system, both methods accurately tracked the wrist during reaching, with mean signed errors of 0.09 ± 1.81 cm and 0.48 ± 1.58 cm for the IMUs and Vive, respectively. Furthermore, both methods' estimated kinematics were highly correlated with each other (p < 0.01). By using relatively inexpensive wearable sensors, these methods may be useful for developing kinematic metrics to evaluate stroke rehabilitation outcomes in both laboratory and clinical environments.

MarkerLess Motion Capture: ML-MoCap, a low-cost modular multi-camera setup

Motion capture systems are extensively used to track human movement to study healthy and pathological movements, allowing for objective diagnosis and effective therapy of conditions that affect our motor system. Current motion capture systems typically require marker placements which is cumbersome and can lead to contrived movements.Here, we describe and evaluate our developed markerless and modular multi-camera motion capture system to record human movements in 3D. The system consists of several interconnected single-board microcomputers, each coupled to a camera (i.e., the camera modules), and one additional microcomputer, which acts as the controller. The system allows for integration with upcoming machine-learning techniques, such as DeepLabCut and AniPose. These tools convert the video frames into virtual marker trajectories and provide input for further biomechanical analysis.The system obtains a frame rate of 40 Hz with a sub-millisecond synchronization between the camera modules. We evaluated the system by recording index finger movement using six camera modules. The recordings were converted via trajectories of the bony segments into finger joint angles. The retrieved finger joint angles were compared to a marker-based system resulting in a root-mean-square error of 7.5 degrees difference for a full range metacarpophalangeal joint motion.Our system allows for out-of-the-lab motion capture studies while eliminating the need for reflective markers. The setup is modular by design, enabling various configurations for both coarse and fine movement studies, allowing for machine learning integration to automatically label the data. Although we compared our system for a small movement, this method can also be extended to full-body experiments in larger volumes.

Developing an intelligent activity-based client-centred training system with a user-centred approach

BACKGROUND

In neurorehabilitation, clinicians and managers are searching for new client-centred task-oriented applications which can be administered without extra costs and effort of therapists, and increase the client's motivation.

OBJECTIVE

To develop and evaluate a prototype of an intelligent activity-based client-centred training (i-ACT) system based on Microsoft Kinect®.

METHODS

Within an iterative user centred process, the i-ACT prototype was developed and necessary features were established for use in neurological settings. After the test trial with a high fidelity prototype, the value, usefulness, and credibility were evaluated.

RESULTS

Seven therapists participated in focus groups and 54 persons with neurological problems participated in test trials. A prototype was established based on the user's experience. Results show that clients and therapists acknowledge the value and usefulness (clients 5.71/7; therapists 4.86/7), and credibility (clients 21.00/27; therapists 14.50/27) of i-ACT.

CONCLUSIONS

Therapists want to be able to record an endless range of movements and activities which enables individualised exercise programs for persons with disabilities. For therapists it is important that the system provides feedback about the quality of movement and not only results. In future work, clinical trials will be performed towards feasibility and effectiveness of i-ACT in neurorehabilitation and other rehabilitation domains.

Motivation, Usability, and Credibility of an Intelligent Activity-Based Client-Centred Training System to Improve Functional Performance in Neurological Rehabilitation: An Exploratory Cohort Study

(1) Background: technology-based training systems are increasingly integrated in neurorehabilitation but are rarely combined with a client-centred task-oriented approach. To provide a low-cost client-centred task-oriented system, the intelligent activity-based client-centred task-oriented training (i-ACT) was developed. The objective was to evaluate the usability, credibility and treatment expectancy of i-ACT, and the motivation towards i-ACT use in rehabilitation over time. Additionally, this study will evaluate the upper limb treatment effects after training with i-ACT. (2) Methods: a mixed-method study was performed in four rehabilitation centres. Training with i-ACT was provided during six weeks, three times per week, forty-five minutes per day, additional to conventional care. (3) Results: seventeen persons with central nervous system diseases were included. High scores were seen in the system usability scale (score ≥ 73.8/100), credibility (score ≥ 22.0/27.0)/expectancy (score ≥ 15.8/27.0) questionnaire, and intrinsic motivation inventory (score ≥ 5.2/7.0), except the subscale pressure (score ≤ 2.0/7.0). Results from the interviews corroborate these findings and showed that clients and therapists believe in the i-ACT system as an additional training support system. Upper limb functional ability improved significantly (p < 0.05) over time on the Wolf motor function test. (4) Conclusion: i-ACT is a client-centred task-oriented usable and motivational system which has the potential to enhance upper limb functional training in persons with neurological diseases.

Wearable sensors in the diagnosis and study of Parkinson's disease symptoms: a systematic review

Nowadays, there are several diseases which affect different systems of the body, producing changes in the correct functioning of the organism and the people lifestyles. One of them is Parkinson's disease (PD), which is defined as a neurodegenerative disorder provoked by the destruction of dopaminergic neurons in the brain, resulting in a set of motor and non-motor symptoms. As this disease affects principally to ancient people, several researchers have studied different treatments and therapies for stopping neurodegeneration and diminishing symptoms, to improve the quality patients' lives. The most common therapies created for PD are based on pharmacological treatment for controlling the degeneration advance and the physical ones which do not reveal the progress of patients. For this reason, this review paper opens the possibility for using wearable motion capture systems as an option for the control and study of PD. Therefore, it aims to (1) study the different wearable systems used for capture the movements of PD patients and (2) determine which of them bring better results for monitoring and assess PD people. For the analysis, it uses papers based on experiments that prove the functioning of several motion systems in different aspects as monitoring, treatment and diagnose of the disease. As a result, it works with 30 papers which describe the factors mentioned before. Additionally, the paper uses journals and literature review about the pathology, its characteristics and the function of wearable sensors for the correct understanding of the topic.

Effects of camera viewing angles on tracking kinematic gait patterns using Azure Kinect, Kinect v2 and Orbbec Astra Pro v2

BACKGROUND

Depth sensors could be a portable, affordable, marker-less alternative to three-dimension motion capture systems for gait analysis, but the effects of camera viewing angles on their joint angle tracking performance have not been fully investigated.

RESEARCH QUESTIONS

This study evaluated the accuracies of three depth sensors [Azure Kinect (AK); Kinect v2 (K2); Orbbec Astra (OA)] for tracking kinematic gait patterns during treadmill walking at five camera viewing angles (0°/22.5°/45°/67.5°/90°).

METHODS

Ten healthy subjects performed fifteen treadmill walking trials (3 speeds × 5 viewing angles) using the three depth sensors to measure joint angles in sagittal hip, frontal hip, sagittal knee, and sagittal ankle. Ten walking steps were recorded and averaged for each walking trial. Range of motion in terms of maximum and minimum joint angles measured by the depth sensors were compared with the Vicon motion capture system as the gold standard. Depth sensors tracking accuracies were compared against the Vicon reference using root-mean-square error (RMSE) on the joint angle time series. Effects of different walking speeds, viewing angles, and depth sensors on the tracking accuracy were observed using three-way repeated-measure analysis of variance (ANOVA).

RESULTS

ANOVA results on RMSE showed significant interaction effects between viewing angles and depth sensors for sagittal hip [F(8,72) = 4.404, p = 0.005] and for sagittal knee [F(8,72)=13.211, p < 0.001] joint angles. AK had better tracking performance when subjects walked at non-frontal camera viewing angles (22.5°/45°/67.5°/90°); while K2 performed better at frontal viewing angle (0°). The superior tracking performance of AK compared with K2/OA might be attributed to the improved depth sensor resolution and body tracking algorithm.

SIGNIFICANCE

Researchers should be cautious about camera viewing angle when using depth sensors for kinematic gait measurements. Our results demonstrated Azure Kinect had good tracking performance of sagittal hip and sagittal knee joint angles during treadmill walking tests at non-frontal camera viewing angles.

Tenodesis Grasp Detection in Egocentric Video

OBJECTIVE

Cervical spinal cord injury (cSCI) can impair motor function in the upper limbs. Video from wearable cameras (egocentric video) has the potential to provide monitoring of rehabilitation outcomes at home, but methods for automated analysis of this data are needed. Wrist flexion and extension are essential elements to track grasping strategies after cSCI, as they may reflect the use of the tenodesis grasp, a common compensatory strategy. However, there is no established method to evaluate wrist flexion and extension from egocentric video.

METHODS

We propose a machine-learning-based approach comprising three steps-hand detection, pose estimation, and arm orientation estimation-to estimate wrist angle data, leading to the detection of tenodesis grasp.

RESULTS

The hand detection in conjunction with the pose estimation algorithm correctly located wrist and index finger metacarpophalangeal coordinates in 63% and 76% of 15,319 annotated frames, respectively, extracted from egocentric videos of individuals with cSCI performing activities of daily living in a home simulation laboratory. The arm orientation algorithm had a mean absolute error of 2.76 +/- 0.39 degrees in 12,863 labeled frames. Using these estimates, the presence of a tenodesis grasp was correctly detected in 72% +/- 11% of frames in videos of 6 activities.

CONCLUSION

The results provided a clear indication of which participants relied on tenodesis grasp and which did not.

SIGNIFICANCE

This paradigm provides the first method that can enable clinicians and researchers to monitor the use of the tenodesis grasp by individuals with cSCI at home, with implications for remote therapeutic guidance.

Evidence Based Position Paper on Physical and Rehabilitation Medicine professional practice for persons with cerebral palsy

INTRODUCTION

Cerebral palsy (CP) is a group of the most common developmental disorders affecting movement and posture of the body, causing activity limitations and participation restrictions. The motor disorders of persons with CP are often accompanied by disturbances of sensation, cognition, communication and perception. The symptoms of CP are very diverse and persons with CP are usually presented with a mixed type of symptoms. The non-progressive disturbances can be attributed to disorders that were developed during pregnancy, birth and/or infant stage.

AIM

The aim of this study was to improve Physical and Rehabilitation Medicine physician´s professional practice for persons with cerebral palsy in order to improve their functionality, social and community integration, and to reduce activity limitations and/or participation restrictions.

MATERIAL AND METHODS

A systematic review of the literature including an eighteen-year period and consensus procedure by means of a Delphi process was performed and involved the delegates of all European countries represented in the Union of European Medical Specialists Physical and Rehabilitation Medicine (UEMS PRM) Section.

RESULTS

As the result of a Consensus Delphi procedure process 74 recommendations are presented together with the systematic literature review.

CONCLUSIONS

The PRM physician´s role for persons with cerebral palsy is to lead and coordinate the multiprofessional team, working in an interdisciplinary way. They should propose and manage the complex but individual PRM programme developed in conjunction with other health professionals, medical specialists and importantly in agreement with the patient, their family and care giver. This should be, according to the specific medical diagnosis to improve patients´ health, functioning, social and education status, considering all impairments, comorbidities and complications, activity limitations and participation restrictions.

Assistive HCI-Serious Games Co-design Insights: The Case Study of i-PROGNOSIS Personalized Game Suite for Parkinson's Disease

Human-Computer Interaction (HCI) and games set a new domain in understanding people's motivations in gaming, behavioral implications of game play, game adaptation to player preferences and needs for increased engaging experiences in the context of HCI serious games (HCI-SGs). When the latter relate with people's health status, they can become a part of their daily life as assistive health status monitoring/enhancement systems. Co-designing HCI-SGs can be seen as a combination of art and science that involves a meticulous collaborative process. The design elements in assistive HCI-SGs for Parkinson's Disease (PD) patients, in particular, are explored in the present work. Within this context, the Game-Based Learning (GBL) design framework is adopted here and its main game-design parameters are explored for the Exergames, Dietarygames, Emotional games, Handwriting games, and Voice games design, drawn from the PD-related i-PROGNOSIS Personalized Game Suite (PGS) (www.i-prognosis.eu) holistic approach. Two main data sources were involved in the study. In particular, the first one includes qualitative data from semi-structured interviews, involving 10 PD patients and four clinicians in the co-creation process of the game design, whereas the second one relates with data from an online questionnaire addressed by 104 participants spanning the whole related spectrum, i.e., PD patients, physicians, software/game developers. Linear regression analysis was employed to identify an adapted GBL framework with the most significant game-design parameters, which efficiently predict the transferability of the PGS beneficial effect to real-life, addressing functional PD symptoms. The findings of this work can assist HCI-SG designers for designing PD-related HCI-SGs, as the most significant game-design factors were identified, in terms of adding value to the role of HCI-SGs in increasing PD patients' quality of life, optimizing the interaction with personalized HCI-SGs and, hence, fostering a collaborative human-computer symbiosis.

Use of a technology-based system to motivate older adults in performing physical activity: a feasibility study

Background

Maintaining or initiating regular physical activity (PA) is important for successful aging. Technology-based systems may support and stimulate older adults to initiate and persevere in performing PA. The aim of the current study was to assess to which extent a customised Kinect system is 1) a credible tool to increase PA in older adults, 2) motivating to perform PA by older adults, and 3) easy to be used in older adults.

Methods

A mixed-method cross-sectional feasibility study was performed in 5 aged care facilities in Flanders, Belgium. Aged participants were asked to perform a 20–30 min test with the intelligent Activity-based Client-centred Training (i-ACT) system. After the test, the ‘Credibility and Expectancy Questionnaire’ (CEQ), the ‘Intrinsic Motivation Inventory’(IMI), the System Usability Scale (SUS), and semi-structured interviews were conducted in the older adults. Feedback was gathered using the thinking aloud method in both aged participants and healthcare professionals.

Results

A total of 48 older adults (20 males and 28 females, mean age = 81.19 (SD = 8.10)), were included. The scores pertaining to system credibility and expectancy, system usability, and motivation towards use were moderate to good. Participants reported that they liked using the i-ACT system, but that the context could be more attractive by adding more visualisations. Twelve professionals stated that they observed involvement in older adults but think that i-ACT is better used in day care centres.

Conclusions

This study indicates that i-ACT is a usable and motivational system to engage older adults to perform PA and therefore supports successful aging. Future research is necessary to investigate the efficacy of i-ACT to perform PA and the transfer to regain and/or maintain engagement in ADLs that older adults find meaningful and purposeful at an older age. Also, further development of i-ACT is advisable to adapt the i-ACT system towards implementation at the home of older adults.

Trial registration

ClinicalTrial.gov ID NCT04489563, 23 July 2020 - Retrospectively registered.

Usability study of wearable inertial sensors for exergames (WISE) for movement assessment and exercise

BACKGROUND

Accurate assessment of movement limitations and compliance monitoring of exercises to restore movement are necessary to tailor treatments for individuals with motor deficits. Although several commercial-grade technologies are available to clinicians for evaluating movement limitations, they require one-on-one time-consuming assessments with limited reproducibility across care settings. To address these limitations, a wearable inertial sensors for exergames (WISE) system has been designed with: (I) an animated virtual coach to deliver instruction and (II) a subject-model whose movements are animated by real-time sensor measurements from the WISE system worn by a subject. This paper examines the WISE system's accuracy and usability for the assessment of upper limb range of motion (ROM).

METHODS

Seventeen neurologically intact subjects were recruited to participate in a usability study of the WISE system. The subjects performed five shoulder and elbow exercises for each arm instructed by the animated virtual coach. The accuracy of ROM measurements obtained with the WISE system versus those obtained with the Kinect™ were compared using the root mean square error (RMSE) of the computed joint angles. The subjects additionally completed a system usability scale (SUS) to evaluate the usability of the virtual coach for tutoring ROM exercises.

RESULTS

The absolute agreement between the WISE and Kinect devices was moderate to very good and it was limited because the Kinect sensor suffers from occlusion. The Bland-Altman limits of agreement for the exercises in the coronal and transverse planes were within the acceptable limits of ±10°. The SUS response data produced relatively high third and first quartile scores of 97.5 and 82.5, respectively, with the interquartile range of 15 and the minimum score of 65, suggesting that the subjects were interested in using the animated virtual coach for tutoring ROM exercises.

CONCLUSIONS

An animated virtual coach-based WISE system for mHealth is presented, tested, and validated for guided upper limb ROM exercises. Future studies with patient populations will facilitate the use of these devices in clinical and telerehabilitation settings.

Validation of motion tracking as tool for observational toothbrushing studies

Video observation (VO) is an established tool for observing toothbrushing behaviour, however, it is a subjective method requiring thorough calibration and training, and the toothbrush position is not always clearly visible. As automated tracking of motions may overcome these disadvantages, the study aimed to compare observational data of habitual toothbrushing as well as of post-instruction toothbrushing obtained from motion tracking (MT) to observational data obtained from VO. One-hundred-three subjects (37.4±14.7 years) were included and brushed their teeth with a manual (MB; n = 51) or a powered toothbrush (PB; n = 52) while being simultaneously video-filmed and tracked. Forty-six subjects were then instructed how to brush their teeth systematically and were filmed/tracked for a second time. Videos were analysed with INTERACT (Mangold, Germany); parameters of interest were toothbrush position, brushing time, changes between areas (events) and the Toothbrushing Systematic Index (TSI). Overall, the median proportion (min; max) of identically classified toothbrush positions (both sextant/surface correct) in a brushing session was 87.8% (50.0; 96.9), which was slightly higher for MB compared to PB (90.3 (50.0; 96.9) vs 86.5 (63.7; 96.5) resp.; p = 0.005). The number of events obtained from MT was higher than from VO (p < 0.001) with a moderate to high correlation between them (MB: ρ = 0.52, p < 0.001; PB: ρ = 0.87; p < 0.001). After instruction, both methods revealed a significant increase of the TSI regardless of the toothbrush type (p < 0.001 each). Motion tracking is a suitable tool for observing toothbrushing behaviour, is able to measure improvements after instruction, and can be used with both manual and powered toothbrushes.

A Wearable Pendant Sensor to Monitor Compliance with Range of Motion Lymphatic Health Exercise

Lymphedema is a chronic and debilitating condition affecting 1 in 1000 Americans and there is no known cure for it. The optimal lymph flow (TOLF) is an effective preventive exercise program designed to reduce the risks of lymphedema. This paper proposes a portable and wearable medical device to monitor compliance with the TOLF therapy. Specifically, the wearable pendant sensor (WPS), a low-fidelity prototype of the proposed design, is developed and tested in comparison with a markerless optical motion capture system (Kinect) for measurement accuracy during shoulder abduction-adduction and flexion-extension exercises. It is shown that the Kendall's Tau between the measurements obtained from the WPS and Kinect devices yields a correlation coefficient ρ = 0.807 for abduction-adduction exercise and ρ = 0.783 for flexion-extension exercise with a significance level of p < 0.001, indicating a strong correlation and high statistical significance. Following careful clinical assessment and validation, preliminary engineering design of this paper can be transformed into an Internet of Things (IoT)-based medical device to facilitate telemonitoring of TOLF therapy. Deployment of such an IoT-based device in patient homes can permit remote assessment of motor function to enhance treatment adherence.Clinical Relevance-This paper documents a WPS with potential to render an IoT-based medical device for monitoring adherence to TOLF exercise program to prevent the risk of post-operative lymphedema.

The effect of silicone ankle sleeves and lace-up ankle braces on neuromuscular control, joint torque, and cutting agility

Objective

To evaluate the effects of silicone ankle sleeves (SASs) and lace-up ankle braces (LABs) on neuromuscular control, net joint torques, and cutting agility in healthy, active individuals.

Design

Markerless motion-capture technology tracked subjects fitted with SASs, LABs, or no brace while they performed the movements: Y-excursion, left cutting, right cutting, single-leg drop vertical jump (SLDVJ), 45-degree bound, and single-leg squat (SLS).

Setting

University Laboratory.

Participants

Ten healthy, active individuals (5 males and 5 females, mean ± SD 23.60 ± 1.43 years of age).

Main outcome measures

Degrees of joint range of motion (ROM), Newton-meters of joint torque, time to perform a cutting maneuver.

Results

SASs and LABs resulted in significantly different knee and ankle ROM and hip internal rotation in the SLDVJ, SLS, Y-excursion, cutting maneuver, and 45-degree bound when compared to control (p < .05). Both ankle and knee torque were significantly reduced in the 45-degree bound and cutting movements with both types of PABs (p < .05). There were minimal differences between the SASs and LABs for all conditions. There were no statistically significant differences in cutting times for any of the 3 conditions.

Conclusion

Both SAS and LAB positively impacted neuromuscular control, reduced net joint torque, and neither impaired cutting agility when compared to control.

Validation of Marker-Less System for the Assessment of Upper Joints Reaction Forces in Exoskeleton Users

This paper presents the validation of a marker-less motion capture system used to evaluate the upper limb stress of subjects using exoskeletons for locomotion. The system fuses the human skeletonization provided by commercial 3D cameras with forces exchanged by the user to the ground through upper limbs utilizing instrumented crutches. The aim is to provide a low cost, accurate, and reliable technology useful to provide the trainer a quantitative evaluation of the impact of assisted gait on the subject without the need to use an instrumented gait lab. The reaction forces at the upper limbs' joints are measured to provide a validation focused on clinically relevant quantities for this application. The system was used simultaneously with a reference motion capture system inside a clinical gait analysis lab. An expert user performed 20 walking tests using instrumented crutches and force platforms inside the observed volume. The mechanical model was applied to data from the system and the reference motion capture, and numerical simulations were performed to assess the internal joint reaction of the subject's upper limbs. A comparison between the two results shows a root mean square error of less than 2% of the subject's body weight.

Effects of virtual reality associated with serious games for upper limb rehabilitation inpatients with multiple sclerosis: randomized controlled trial

BACKGROUND

Dexterity and activities of daily living limitations on the upper limb (UL) represent one of the most common problems in patients with multiple sclerosis (MS). The aim of this study was to evaluate the effectiveness of the specially developed Serious Games that make use of the Leap Motion Controller (LMC) as main user interface for improving UL grip muscle strength, dexterity, fatigue, quality of life, satisfaction and compliance.

METHODS

A single-blinded randomized controlled trial was conducted. The sample was randomized into two groups: an experimental group who received treatment based on serious games designed by the research team using the developed LMC based Serious Games for the UL plus conventional rehabilitation, and a control group who received the same conventional rehabilitation for the UL. Both groups received two 60 min sessions per week over a ten-week period. Grip muscle strength, coordination, speed of movements, fine and gross UL dexterity, fatigue, quality of life, satisfaction and compliance were assessed in both groups pre-treatment, post-treatment and in a follow-up period of 1 month without receiving any treatment.

RESULTS

In the experimental group compared to the control group, significant improvements were observed in the post-treatment assessment for coordination, speed of movements, fine and gross UL dexterity. Also, significant results were found in the follow-up in coordination, speed of movements, fine and gross for the more affected side.

CONCLUSIONS

An experimental protocol using an LMC based Serious Games designed for UL rehabilitation showed improvements for unilateral gross manual dexterity, fine manual dexterity, and coordination in MS patients with high satisfaction and excellent compliance.

TRIAL REGISTRATION

This randomized controlled trial has been registered at ClinicalTrials.gov Identifier: NCT04171908 , Nov 2019.

Monitoring Improvement in Infantile Cerebral Palsy Patients Using the 4DBODY System-A Preliminary Study

Monitoring the patient's condition during rehabilitation is the key to success in this form of treatment. This is especially important in patients with infantile cerebral palsy (ICP). Objective assessment can be achieved through modern optical measurement techniques. The 4DBODY system allows to capture dynamic movement with high accuracy. Eight patients with ICP participated in the study. The group underwent therapy lasting seven days using neurodevelopmental treatment (NDT) and functional training (FT). The patients' condition was monitored by the 4DBODY system. The measurements were taken three times: before the therapy, after, and then again after one week. We have developed the Trunk Mobility in the Frontal Plane Index (TMFPI) for its assessment. The results were compared with a score obtained using the Gross Motor Function Measure scale (GMFM 88). An improvement of the TMFPI parameter was observed in five patients, inconsistent results in two and deterioration in one. The reference GMFM score was higher in all patients relative to pre-treatment values. We found that surface scanning with the 4DBODY system allows to precisely track body movement in ICP patients. The decrease in the TMFPI parameter reflects the improvement in the dysfunction of body alignment, balance and symmetry of movement on the L and R body side.