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Distinguishing pathological gait is challenging in neurology because of the difficulty of capturing total body movement and its analysis. We aimed to obtain a convenient recording with an iPhone and establish an algorithm based on deep learning. From May 2021 to November 2022 at Yamagata University Hospital, Shiga University, and Takahata Town, patients with idiopathic normal pressure hydrocephalus (n = 48), Parkinson’s disease (n = 21), and other neuromuscular diseases (n = 45) comprised the pathological gait group (n = 114), and the control group consisted of 160 healthy volunteers. iPhone application TDPT-GT captured the subjects walking in a circular path of about 1 meter in diameter, a markerless motion capture system, with an iPhone camera, which generated the three-axis 30 frames per second (fps) relative coordinates of 27 body points. A light gradient boosting machine (Light GBM) with stratified k-fold cross-validation (k = 5) was applied for gait collection for about 1 min per person. The median ability model tested 200 frames of each person’s data for its distinction capability, which resulted in the area under a curve of 0.719. The pathological gait captured by the iPhone could be distinguished by artificial intelligence.

Single camera markerless motion capture has the potential to facilitate at home movement assessment due to the ease of setup, portability, and affordable cost of the technology. However, it is not clear what the current healthcare applications of single camera markerless motion capture are and what information is being collected that may be used to inform clinical decision making. This review aims to map the available literature to highlight potential use cases and identify the limitations of the technology for clinicians and researchers interested in the collection of movement data. Studies were collected up to 14 January 2022 using Pubmed, CINAHL and SPORTDiscus using a systematic search. Data recorded included the description of the markerless system, clinical outcome measures, and biomechanical data mapped to the International Classification of Functioning, Disability and Health Framework (ICF). Studies were grouped by patient population. A total of 50 studies were included for data collection. Use cases for single camera markerless motion capture technology were identified for Neurological Injury in Children and Adults; Hereditary/Genetic Neuromuscular Disorders; Frailty; and Orthopaedic or Musculoskeletal groups. Single camera markerless systems were found to perform well in studies involving single plane measurements, such as in the analysis of infant general movements or spatiotemporal parameters of gait, when evaluated against 3D marker-based systems and a variety of clinical outcome measures. However, they were less capable than marker-based systems in studies requiring the tracking of detailed 3D kinematics or fine movements such as finger tracking. Single camera markerless motion capture offers great potential for extending the scope of movement analysis outside of laboratory settings in a practical way, but currently suffers from a lack of accuracy where detailed 3D kinematics are required for clinical decision making. Future work should therefore focus on improving tracking accuracy of movements that are out of plane relative to the camera orientation or affected by occlusion, such as supination and pronation of the forearm.

Kinematic analysis is a useful and widespread tool used in research and clinical biomechanics for the quantification of human movement. Common marker-based optical motion capture systems are time intensive and require highly trained operators to obtain kinematic data. Markerless motion capture systems offer an alternative method for the measurement of kinematic data with several practical benefits. This work compared the kinematics of human gait measured using a deep learning algorithm-based markerless motion capture system to those from a standard marker-based motion capture system. Thirty healthy adult participants walked on a treadmill while data were simultaneously recorded using eight video cameras and seven infrared optical motion capture cameras, providing synchronized markerless and marker-based data for comparison. The average root mean square distance (RMSD) between corresponding joint centers was less than 2.5 cm for all joints except the hip, which was 3.6 cm. Lower limb segment angles relative to the global coordinate system indicated the global segment pose estimates from both systems were very similar, with RMSD of less than 5.5° for all segment angles except those that represent rotations about the long axis of the segment. Lower limb joint angles captured similar patterns for flexion/extension at all joints, ab/adduction at the knee and hip, and toe-in/toe-out at the ankle. These findings indicate that the markerless system would be a suitable alternative technology in cases where the practical benefits of markerless data collection are preferred.

The emergence of pose estimation algorithms represents a potential paradigm shift in the study and assessment of human movement. Human pose estimation algorithms leverage advances in computer vision to track human movement automatically from simple videos recorded using common household devices with relatively low-cost cameras (e.g., smartphones, tablets, laptop computers). In our view, these technologies offer clear and exciting potential to make measurement of human movement substantially more accessible; for example, a clinician could perform a quantitative motor assessment directly in a patient’s home, a researcher without access to expensive motion capture equipment could analyze movement kinematics using a smartphone video, and a coach could evaluate player performance with video recordings directly from the field. In this review, we combine expertise and perspectives from physical therapy, speech-language pathology, movement science, and engineering to provide insight into applications of pose estimation in human health and performance. We focus specifically on applications in areas of human development, performance optimization, injury prevention, and motor assessment of persons with neurologic damage or disease. We review relevant literature, share interdisciplinary viewpoints on future applications of these technologies to improve human health and performance, and discuss perceived limitations.

Spatiotemporal parameters can characterize the gait patterns of individuals, allowing assessment of their health status and detection of clinically meaningful changes in their gait. Video-based markerless motion capture is a user-friendly, inexpensive, and widely applicable technology that could reduce the barriers to measuring spatiotemporal gait parameters in clinical and more diverse settings. Two studies were performed to determine whether gait parameters measured using markerless motion capture demonstrate concurrent validity with those measured using marker-based motion capture and a pressure-sensitive gait mat. For the first study, thirty healthy young adults performed treadmill gait at self-selected speeds while marker-based motion capture and synchronized video data were recorded simultaneously. For the second study, twenty-five healthy young adults performed over-ground gait at self-selected speeds while footfalls were recorded using a gait mat and synchronized video data were recorded simultaneously. Kinematic heel-strike and toe-off gait events were used to identify the same gait cycles between systems. Nine spatiotemporal gait parameters were measured by each system and directly compared between systems. Measurements were compared using Bland-Altman methods, mean differences, Pearson correlation coefficients, and intraclass correlation coefficients. The results indicate that markerless measurements of spatiotemporal gait parameters have good to excellent agreement with marker-based motion capture and gait mat systems, except for stance time and double limb support time relative to both systems and stride width relative to the gait mat. These findings indicate that markerless motion capture can adequately measure spatiotemporal gait parameters of healthy young adults during treadmill and over-ground gait.

The clinical uptake and influence of gait analysis has been hindered by inherent limitations of marker-based motion capture systems, which have long been the standard method for the collection of gait data including kinematics. Markerless motion capture offers an alternative method for the collection of gait kinematics that presents several practical benefits over marker-based systems. This work aimed to determine the reliability of lower limb gait kinematics from video based markerless motion capture using an established experimental protocol for testing reliability. Eight healthy adult participants performed three sessions of five over-ground walking trials in their own self-selected clothing, separated by an average of 8.5 days, while eight synchronized and calibrated cameras recorded video. Three-dimensional pose estimates from the video data were used to compute lower limb joint angles. Inter-session variability, inter-trial variability, and the variability ratio were used to assess the reliability of the gait kinematics. Compared to repeatability studies based on marker-based motion capture, inter-trial variability was slightly greater than previously reported for some angles, with an average across all joint angles of 2.5°. Inter-session variability was smaller on average than all previously reported values, with an average across all joint angles of 2.8°. Variability ratios were all smaller than those previously reported with an average of 1.1, indicating that the multi-session protocol increased the total variability of joint angles by 10% of the inter-trial variability. These results indicate that gait kinematics can be reliably measured using markerless motion capture.

Markerless motion capturing has the potential to provide a low-cost and accessible alternative to traditional marker-based systems for real-world biomechanical assessment. However, before these systems can be put into practice, we need to rigorously evaluate their accuracy in estimating joint kinematics for various gait patterns. This study evaluated the accuracy of a low-cost, open-source, and smartphone-based markerless motion capture system, namely OpenCap, for measuring 3D joint kinematics in healthy and pathological gait compared to a marker-based system. 21 healthy volunteers were instructed to walk with four different gait patterns: physiological, crouch, circumduction, and equinus gait. Three-dimensional kinematic data were simultaneously recorded using the markerless and a marker-based motion capture system. The root mean square error (RMSE) and the peak error were calculated between every joint kinematic variable obtained by both systems. We found an overall RMSE of 5.8 (SD: 1.8 degrees) and a peak error of 11.3 degrees (SD: 3.9). A repeated measures ANOVA with post hoc tests indicated significant differences in RMSE and peak errors between the four gait patterns (p ¡ 0.05). Physiological gait presented the lowest, crouch and circumduction gait the highest errors. Our findings indicate a roughly comparable accuracy to IMU-based approaches and commercial markerless multi-camera solutions. However, errors are still above clinically desirable thresholds of two to five degrees. While our findings highlight the potential of markerless systems for assessing gait kinematics, they also underpin the need to further improve the underlying deep learning algorithms to make markerless pose estimation a valuable tool in clinical settings.

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.

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

Markerless motion capture allows whole-body movements to be captured without the need for physical markers to be placed on the body. This enables motion capture analyses to be conducted in more ecologically valid environments. However, the influences of varied clothing on video-based markerless motion capture assessments remain largely unexplored. This study investigated two types of clothing conditions, “Sport” (gym shirt and shorts) and “Street” (unrestricted casual clothing), on gait parameters during overground walking by 29 participants at self-selected speeds using markerless motion capture. Segment lengths, gait spatiotemporal parameters, and lower-limb kinematics were compared between the two clothing conditions. Mean differences in segment length for the forearm, upper arm, thigh, and shank between clothing conditions ranged from 0.2 cm for the forearm to 0.9 cm for the thigh (p < 0.05 for thigh and shank) but below typical marker placement errors (1 – 2 cm). Seven out of 9 gait spatiotemporal parameters demonstrated statistically significant differences between clothing conditions (p < 0.05), however, these differences were approximately ten times smaller than minimal detectable changes in movement-related pathologies including multiple sclerosis and cerebral palsy. Hip, knee, and ankle joint angle root-mean-square deviation values averaged 2.6° and were comparable to previously reported average inter-session variability for this markerless system (2.8°). The results indicate that clothing, a potential limiting factor in markerless motion capture performance, would negligibly alter meaningful clinical interpretations under the conditions investigated.