Marker-based Motion Capture System Research Articles

Assessing Spatiotemporal Behavior of Human Gait: A Comparative Study Between Low-Cost Smartphone-Based Mocap and OptiTrack Systems

AbstractThis study assessed the accuracy of a low-cost marker-based motion capture system with smartphone devices to estimate the spatiotemporal behavior of human gait in comparison with the performance of the commercial OptiTrack system. Initially, three test subjects were selected for the study, and after equipping them with passive retroreflective markers, they were recorded for gait velocities of 1.50, 1.90, and 2.30 $$m\bullet {s}^{-1}$$ m ∙ s - 1 while collecting kinematic data and videos. The results showed that the smartphone motion capture system exhibited significant spatiotemporal tracking and accuracy in the x-y trajectories and estimation of joint relative angles of the hip, knee, and ankle joints (θ1, θ2, and θ3, respectively) compared to the commercial OptiTrack system. In this comparison, an average goodness-of-FIT and normalized root mean square error of over 88.93% and 2.71% were obtained, respectively, for the joint relative angles of the hip and knee (θ1 and θ2) in all tests performed. However, the accuracy of the joint relative angle of the ankle (θ3, average FIT: 71.04% and nRMSE: 4.26%) was lower because of the low capture rate of the retroreflective markers in the smartphone system and the higher relative velocity in the lower extremities of the test subjects, which generated noise in the calculation of x-y trajectories. This decrease in accuracy has been reported in other studies. However, both motion capture systems experienced marker data loss at the hip, highlighting the need for improvement in the spatial distribution of the optical devices. The OptiTrack system demonstrated better optical redundancy but still required improvements. In contrast, the smartphone system, with its inherent limitations in terms of optical redundancy and spatial distribution, can be enhanced by incorporating multiple cameras for a three-dimensional view. Despite these limitations, the low-cost smartphone system showed optimal performance with minimal errors compared with the commercial system, making it a cost-effective option with potential for further development. The rapid advancement of smartphone technology and its accessibility make it an attractive choice for motion capture applications.

Real-time multi-camera 3D human pose estimation at the edge for industrial applications

There is an increasing interest in exploiting human pose estimation (HPE) software in human–machine interaction systems. Nevertheless, adopting such a computer vision application in real industrial scenarios is challenging. To overcome occlusion limitations, it requires multiple cameras, which in turn require multiple, distributed, and synchronized HPE software nodes running on resource-constrained edge devices. We address this challenge by presenting a real-time distributed 3D HPE platform, which consists of a set of 3D HPE software nodes on edge devices (i.e., one per camera) to redundantly extrapolate the human pose from different points of view. A centralized aggregator collects the pose information through a shared communication network and merges them, in real time, through a pipeline of filtering, clustering and association algorithms. It addresses network communication issues (e.g., delay and bandwidth variability) through a two-levels synchronization, and supports both single and multi-person pose estimation. We present the evaluation results with a real case of study (i.e., HPE for human–machine interaction in an intelligent manufacturing line), in which the platform accuracy and scalability are compared with state-of-the-art approaches and with a marker-based infra-red motion capture system.

Clinical gait analysis using video-based pose estimation: Multiple perspectives, clinical populations, and measuring change.

Gait dysfunction is common in many clinical populations and often has a profound and deleterious impact on independence and quality of life. Gait analysis is a foundational component of rehabilitation because it is critical to identify and understand the specific deficits that should be targeted prior to the initiation of treatment. Unfortunately, current state-of-the-art approaches to gait analysis (e.g., marker-based motion capture systems, instrumented gait mats) are largely inaccessible due to prohibitive costs of time, money, and effort required to perform the assessments. Here, we demonstrate the ability to perform quantitative gait analyses in multiple clinical populations using only simple videos recorded using low-cost devices (tablets). We report four primary advances: 1) a novel, versatile workflow that leverages an open-source human pose estimation algorithm (OpenPose) to perform gait analyses using videos recorded from multiple different perspectives (e.g., frontal, sagittal), 2) validation of this workflow in three different populations of participants (adults without gait impairment, persons post-stroke, and persons with Parkinson's disease) via comparison to ground-truth three-dimensional motion capture, 3) demonstration of the ability to capture clinically relevant, condition-specific gait parameters, and 4) tracking of within-participant changes in gait, as is required to measure progress in rehabilitation and recovery. Importantly, our workflow has been made freely available and does not require prior gait analysis expertise. The ability to perform quantitative gait analyses in nearly any setting using only low-cost devices and computer vision offers significant potential for dramatic improvement in the accessibility of clinical gait analysis across different patient populations.

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 (120Hz) and the marker-based motion capture system (120Hz). The ground reaction force was recorded synchronously using a force platform (1,200Hz). 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 0.74cm. 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 18N·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.

Inter-trial variability is higher in 3D markerless compared to marker-based motion capture: Implications for data post-processing and analysis

Markerless motion capture has recently attracted significant interest in clinical gait analysis and human movement science. Its ease of use and potential to streamline motion capture recordings bear great potential for out-of-the-laboratory measurements in large cohorts. While previous studies have shown that markerless systems can achieve acceptable accuracy and reliability for kinematic parameters of gait, they also noted higher inter-trial variability of markerless data. Since increased inter-trial variability can have important implications for data post-processing and analysis, this study compared the inter-trial variability of simultaneously recorded markerless and marker-based data. For this purpose, the data of 18 healthy volunteers were used who were instructed to simulate four different gait patterns: physiological, crouch, circumduction, and equinus gait. Gait analysis was performed using the smartphone-based markerless system OpenCap and a marker-based motion capture system. We compared the inter-trial variability of both systems and also evaluated if changes in inter-trial variability may depend on the analyzed gait pattern. Compared to the marker-based data, we observed an increase of inter-trial variability for the markerless system ranging from 6.6% to 22.0% for the different gait patterns. Our findings demonstrate that the markerless pose estimation pipelines can introduce additionally variability in the kinematic data across different gait patterns and levels of natural variability. We recommend using averaged waveforms rather than single ones to mitigate this problem. Further, caution is advised when using variability-based metrics in gait and human movement analysis based on markerless data as increased inter-trial variability can lead to misleading results.

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

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

Conventional video recordings dependably quantify whole-body lifting strategy using the Stoop-Squat-Index: A methods comparison against motion capture and a reliability study

Whole-body lifting strategies could be derived from conventional video recordings using the Stoop-Squat-Index, which quantifies the ratio between trunk forward lean and lower extremity joint flexion from 0 (full squat) to 100 (full stoop). The purpose of this study was to compare Stoop-Squat-Indices derived from conventional video recordings to those from a three-dimensional marker-based motion capture system and to evaluate interrater and intrarater reliability of the video-based approach.Thirty healthy participants lifted a 5-kg box under different conditions (freestyle, squat, stoop). Kinematic data were recorded using a Vicon motion capture system (serving as reference standard) and an iPad camera. Stoop-Squat-Indices over the entire lifting cycle were derived separately from both approaches. Agreement was assessed using mean differences (video minus motion capture) and limits of agreement. Reliability was investigated by calculating intraclass correlation coefficients (ICC) and minimal detectable changes (MDC) over the course of the lifting cycle. Systematic errors were identified with Statistical Parametric Mapping-based T-tests.Systematic errors between the video-based and the motion capture-based approach were observed among all conditions. Mean differences in Stoop-Squat-Indices over the lifting cycle ranged from −6.9 to 3.2 (freestyle), from −1.8 to 5.3 (squat) and from −2.8 to −1.1 (stoop). Limits of agreement were lower when the box was close to the floor, and higher towards upright standing. Reliability of the video-based approach was excellent for most of the lifting cycle, with ICC above 0.995 and MDC below 3.5.These findings support using a video-based assessment of Stoop-Squat-Indices to quantify whole-body lifting strategy in field.

Improving Gait Analysis Techniques with Markerless Pose Estimation Based on Smartphone Location.

Marker-based 3D motion capture systems, widely used for gait analysis, are accurate but have disadvantages such as cost and accessibility. Whereas markerless pose estimation has emerged as a convenient and cost-effective alternative for gait analysis, challenges remain in achieving optimal accuracy. Given the limited research on the effects of camera location and orientation on data collection accuracy, this study investigates how camera placement affects gait assessment accuracy utilizing five smartphones. This study aimed to explore the differences in data collection accuracy between marker-based systems and pose estimation, as well as to assess the impact of camera location and orientation on accuracy in pose estimation. The results showed that the differences in joint angles between pose estimation and marker-based systems are below 5°, an acceptable level for gait analysis, with a strong correlation between the two datasets supporting the effectiveness of pose estimation in gait analysis. In addition, hip and knee angles were accurately measured at the front diagonal of the subject and ankle angle at the lateral side. This research highlights the significance of careful camera placement for reliable gait analysis using pose estimation, serving as a concise reference to guide future efforts in enhancing the quantitative accuracy of gait analysis.

Comparison of Shoulder Range of Motion Quantified with Mobile Phone Video-Based Skeletal Tracking and 3D Motion Capture-Preliminary Study.

The accuracy of human pose tracking using smartphone camera (2D-pose) to quantify shoulder range of motion (RoM) is not determined. Twenty healthy individuals were recruited and performed shoulder abduction, adduction, flexion, or extension, captured simultaneously using a smartphone-based human pose estimation algorithm (Apple's vision framework) and using a skin marker-based 3D motion capture system. Validity was assessed by comparing the 2D-pose outcomes against a well-established 3D motion capture protocol. In addition, the impact of iPhone positioning was investigated using three smartphones in multiple vertical and horizontal positions. The relationship and validity were analysed using linear mixed models and Bland-Altman analysis. We found that 2D-pose-based shoulder RoM was consistent with 3D motion capture (linear mixed model: R2 > 0.93) but was somewhat overestimated by the smartphone. Differences were dependent on shoulder movement type and RoM amplitude, with adduction the worst performer among all tested movements. All motion types were described using linear equations. Correction methods are provided to correct potential out-of-plane shoulder movements. Shoulder RoM estimated using a smartphone camera is consistent with 3D motion-capture-derived RoM; however, differences between the systems were observed and are likely explained by differences in thoracic frame definitions.

Comparison of markerless and marker-based motion capture systems using 95% functional limits of agreement in a linear mixed-effects modelling framework

Biomechanics analysis of human movement has been proven useful for maintenance of health, injury prevention, and rehabilitation in both sports and clinical populations. A marker-based motion capture system is considered the gold standard method of measurement for three dimensional kinematics measurements. However, the application of markers to anatomical bony points is a time consuming process and constrained by inter-, intra-tester and session reliability issues. The emergence of novel markerless motion capture systems without the use of reflective markers is a rapidly growing field in motion analysis. However an assessment of the level of agreement of a markerless system with an established gold standard marker-based system is needed to ensure the applicability of a markerless system. An extra layer of complexity is involved as the kinematics measurements are functional responses. In this paper a new approach is proposed to generate 95% functional limits of agreement (fLoA) using the linear mixed-effects modelling framework for hierarchical study designs. This approach is attractive as it will allow practitioners to extend their use of linear mixed models to assess agreement in method comparison studies in all domains where functional responses are recorded.

Validation of machine vision and action sport cameras for 3D motion analysis model reconstruction

The study investigated the feasibility of using action sport cameras for motion analysis research. Data acquired from two different marker-based motion capture systems and six different camera combinations were analyzed for motion reconstruction accuracy. Two different calibration procedures were used to determine the influence on marker position reconstruction. Static and dynamic calibration mean merit score differences between the reference and experimental camera systems were 0.4 mm and 1.3 mm, respectively. Angular displacement difference between the reference and experimental camera systems range between 0.1 and 2.0 degrees. A systematic bias (− 0.54 to 0.19 degrees) was determined between the reference and the experimental camera systems for range of motion. The mean of the multi-trial findings suggests the machine vision camera system calibrated with a dynamic procedure generated highly accurate three-dimensional reconstructed ROM data (0.5 degree) followed closely by the four action sport cameras implementing a static calibration procedure (0.5 degree). The overall findings suggest the selected machine vision and action sport camera systems produced comparable results to the reference motion analysis system. However, the combination of camera type, processing software, and calibration procedure can influence motion reconstruction accuracy.

A webcam-based machine learning approach for three-dimensional range of motion evaluation.

Joint range of motion (ROM) is an important quantitative measure for physical therapy. Commonly relying on a goniometer, accurate and reliable ROM measurement requires extensive training and practice. This, in turn, imposes a significant barrier for those who have limited in-person access to healthcare. The current study presents and evaluates an alternative machine learning-based ROM evaluation method that could be remotely accessed via a webcam. To evaluate its reliability, the ROM measurements for a diverse set of joints (neck, spine, and upper and lower extremities) derived using this method were compared to those obtained from a marker-based optical motion capture system. Data collected from 25 healthy adults demonstrated that the webcam solution exhibited high test-retest reliability, with substantial to almost perfect intraclass correlation coefficients for most joints. Compared with the marker-based system, the webcam-based system demonstrated substantial to almost perfect inter-rater reliability for some joints, and lower inter-rater reliability for other joints (e.g., shoulder flexion and elbow flexion), which could be attributed to the reduced sensitivity to joint locations at the apex of the movement. The proposed webcam-based method exhibited high test-retest and inter-rater reliability, making it a versatile alternative for existing ROM evaluation methods in clinical practice and the tele-implementation of physical therapy and rehabilitation.

Effect of lace-up ankle brace on the tibiotalar and subtalar joint during the landing.

Objective: Ankle braces can affect the kinematics of the ankle joint during landing tasks. Previous studies were primarily relied on traditional marker-based motion capture systems, which pose limitations in non-invasively capturing the motion of the talus bone. The effect of ankle braces on the in vivo kinematics of the tibiotalar and subtalar joints during landing remains unknown. This study used a high-speed dual fluoroscopic imaging system (DFIS) and magnetic resonance imaging (MRI) to investigate effect of ankle braces on the in vivo kinematics of the tibiotalar and subtalar joints during landing. Methods: Fourteen healthy participants were recruited for this study. During the experiment, static three-dimensional MRI data were collected for each participant, and 3D ankle joint models for the calcaneus, talus, and tibia were constructed. The DFIS was used to capture the images of each participant performing a single-leg landing-jump task at a height of 40cm. The images were captured once with and without a brace in the fatigue condition, which was induced by running. The six-degree-of-freedom (6DOF) kinematic data were obtained by 2D-3D registration. Results: The flexion-extension range of motion (ROM) (42.73 ± 4.76° vs. 38.74 ± 5.43°, p = 0.049) and anterior-posterior translation ROM (16.86 ± 1.74mm vs. 15.03 ± 1.73 mm, p = 0.009) of the tibiotalar joint were decreased. The maximum inversion angle (-3.71 ± 2.25° vs. 2.11 ± 1.83°, p = 0.047) of the subtalar joint was decreased. Conclusion: The ankle brace limited the flexion-extension ROM of the tibiotalar joints and the inversion angle of the subtalar joint during landing.

Concurrent validity of smartphone-based markerless motion capturing to quantify lower-limb joint kinematics in healthy and pathological gait

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.

Quantitative Comparison of Hand Kinematics Measured with a Markerless Commercial Head-Mounted Display and a Marker-Based Motion Capture System in Stroke Survivors.

Upper-limb paresis is common after stroke. An important tool to assess motor recovery is to use marker-based motion capture systems to measure the kinematic characteristics of patients' movements in ecological scenarios. These systems are, however, very expensive and not readily available for many rehabilitation units. Here, we explored whether the markerless hand motion capabilities of the cost-effective Oculus Quest head-mounted display could be used to provide clinically meaningful measures. A total of 14 stroke patients executed ecologically relevant upper-limb tasks in an immersive virtual environment. During task execution, we recorded their hand movements simultaneously by means of the Oculus Quest's and a marker-based motion capture system. Our results showed that the markerless estimates of the hand position and peak velocity provided by the Oculus Quest were in very close agreement with those provided by a marker-based commercial system with their regression line having a slope close to 1 (maximum distance: mean slope = 0.94 ± 0.1; peak velocity: mean slope = 1.06 ± 0.12). Furthermore, the Oculus Quest had virtually the same sensitivity as that of a commercial system in distinguishing healthy from pathological kinematic measures. The Oculus Quest was as accurate as a commercial marker-based system in measuring clinically meaningful upper-limb kinematic parameters in stroke patients.

Gait analysis comparison between manual marking, 2D pose estimation algorithms, and 3D marker-based system.

Recent advances in Artificial Intelligence (AI) and Computer Vision (CV) have led to automated pose estimation algorithms using simple 2D videos. This has created the potential to perform kinematic measurements without the need for specialized, and often expensive, equipment. Even though there's a growing body of literature on the development and validation of such algorithms for practical use, they haven't been adopted by health professionals. As a result, manual video annotation tools remain pretty common. Part of the reason is that the pose estimation modules can be erratic, producing errors that are difficult to rectify. Because of that, health professionals prefer the use of tried and true methods despite the time and cost savings pose estimation can offer. In this work, the gait cycle of a sample of the elderly population on a split-belt treadmill is examined. The Openpose (OP) and Mediapipe (MP) AI pose estimation algorithms are compared to joint kinematics from a marker-based 3D motion capture system (Vicon), as well as from a video annotation tool designed for biomechanics (Kinovea). Bland-Altman (B-A) graphs and Statistical Parametric Mapping (SPM) are used to identify regions of statistically significant difference. Results showed that pose estimation can achieve motion tracking comparable to marker-based systems but struggle to identify joints that exhibit small, but crucial motion. Joints such as the ankle, can suffer from misidentification of their anatomical landmarks. Manual tools don't have that problem, but the user will introduce a static offset across the measurements. It is proposed that an AI-powered video annotation tool that allows the user to correct errors would bring the benefits of pose estimation to professionals at a low cost.

Concurrent assessment of a smartphone-based markerless and marker-based motion capture system in pathological gait

Concurrent assessment of a smartphone-based markerless and marker-based motion capture system in pathological gait

Comparing Trunk Kinematics Computed by Optical Marker-Based Motion Capture System and Inertial Measurement Units During Overground Trips

Falls are the most common cause of non-fatal injuries, and trips are responsible for high percentages of those falls in the United States. Traditional method for estimating trunk kinematics during overground trips uses optical marker-based motion capture systems. However, their cost and space requirements can often be barriers in this research field. Inexpensive and portable inertial measurement units may be an appropriate alternative. This study compared trunk flexion angle and angular velocity at touchdown of the initial recovery step after laboratory-induced trips while walking captured by the optical markerbased motion capture system versus IMUs. Our results provide evidence that a sternum-worn IMU can provide trunk kinematic measurements of clinical relevance and may be used to provide meaningful data to understand kinematic responses to trips or trip-induced falls that occur in real life.

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.

Markerless human pose estimation for biomedical applications: a survey

Markerless Human Pose Estimation (HPE) proved its potential to support decision making and assessment in many fields of application. HPE is often preferred to traditional marker-based Motion Capture systems due to the ease of setup, portability, and affordable cost of the technology. However, the exploitation of HPE in biomedical applications is still under investigation. This review aims to provide an overview of current biomedical applications of HPE. In this paper, we examine the main features of HPE approaches and discuss whether or not those features are of interest to biomedical applications. We also identify those areas where HPE is already in use and present peculiarities and trends followed by researchers and practitioners. We include here 25 approaches to HPE and more than 40 studies of HPE applied to motor development assessment, neuromuscolar rehabilitation, and gait &amp;amp; posture analysis. We conclude that markerless HPE offers great potential for extending diagnosis and rehabilitation outside hospitals and clinics, toward the paradigm of remote medical care.