Table 1.
Details of the selected studies
| Study | Patient types | Sample size (n) | MMC system | Measurement items | Content of measurement | Context of measurement | Primary results | Results interpretation |
|---|---|---|---|---|---|---|---|---|
| Cho et al. 2009 [28] | PD | Patients with PD (7); healthy controls (7) | Sony HDR-HC3 camcorder | Gait pattern | Recognition of PD gait by algorithm combining PCA with LDA | Laboratory | The proposed system can identify healthy adults and patients with PD by their gaits with high reliability | Video-based analysis helps in discriminating the gait patterns of PD patients and healthy adults |
| Adde et al. 2010 [41] | CP | Infants with high risk of CP (30) | Digital video camera | Quantity of motion, velocity and acceleration of the centroid of motion | Comparison of quantity of motion and centroid of motion in infants who developed into CP with those who did not develop into CP | Hospital | Quantity of motion mean, median, and standard deviation were significantly higher in the group of infants who did not develop CP than in the group who did develop CP | Quantitative variables related to the variability of the center of infant movement and to the amount of motion predicted later CP in young infants with high sensitivity and specificity |
|
Bahat et al. [61] |
Chronic neck pain | Patients with chronic neck pain (25); asymptomatic participants (42) | Customized VR assessment system | Cervical ROM (flexion, extension, rotation, and lateral flexion) | Comparison of cervical movement in patients with chronic neck pain, versus in healthy controls | Laboratory | Significant group differences for 3 of the kinematic measures: Vpeak, Vmean, and number of velocity peaks | “Goal-directed fast cervical movements performed by patients with chronic neck pain were characterized by lower velocity and decreased smoothness compared with asymptomatic participants” [61] |
| Chen et al. 2011 [29] | PD | Patients with PD (12); healthy adults (12) | CCD video camera | Gait parameters including gait cycle time, stride length, walking velocity, and cadence | Quantification of gait parameters | Structured environment |
KPCA-based method achieved a classification accuracy of 80.51% in identifying different gaits |
Kinematic data extracted from video might allow clinicians to obtain the quantitative gait parameters and assess the progression of PD |
| Khan et al. 2013 [14] | PD | Patients diagnosed with advanced PD (13); healthy controls (6) | Video recordings, analyzed by CV algorithm | Index-finger motion in finger tapping, features including speed, amplitude, rhythm, and fatigue in tapping were computed | SVM classification to categorize the patient group between UPDRS-FT symptom severity levels, and to discriminate between PD patients and healthy controls | Medical facility | The proposed CV-based SVM scheme discriminated between control and patient group with an average of 94.5% accuracy | The ML framework offers good classification performance in distinguishing symptom severity levels based on clinical ratings, as well as in identifying PD patients and the healthy controls |
| Lowes et al. 2013 [65] | Dystrophinopathy | Patients with dystrophinopathy (5); healthy controls (5) | Kinect | Upper extremity functional reaching volume, velocity, and rate of fatigue | Validity and Reliability of the MMC system in capturing upper extremity functional reaching volume, movement velocity, and rate of UE fatigue in individuals with dystrophinopathy | Laboratory | Preliminary test-retest reliability of the MMC method for 2 sequential trials was excellent for functional reaching volume | “The newly available gaming technology has potential to be used to create a low-cost, accessible, and functional upper extremity outcome measure for use with children and adults with dystrophinopathy” [65] |
| O’Keefe et al. 2013 [60] | FXS | Males with FXS (13); healthy controls (7) | BioStage™ | Motion parameters (frequency and total traveled distance) of body segments during 30 s of story listening while standing in the observation space | Comparison between groups, MMC system results were compared with scores on video-capture methodology and behavioral rating scales | Laboratory | Arm and foot travel distances were significantly greater in the FXS group compared with the controls | “Motion parameters obtained from the markerless system can quantify increased movement in subjects with FXS relative to controls” [60] |
| Olesh et al. 2014 [46] | Stroke | Patients with stroke (9) | Kinect | 10 movements of the upper extremity | Quantitative scores derived from motion capture were compared to qualitative clinical scores produced by trained human raters | Laboratory | Strong linear relationship was found between qualitative scores and quantitative scores derived from both standard and low-cost motion capture system | “The low-cost motion capture combined with an automated scoring algorithm is a feasible method to assess objectively upper-arm impairment post stroke” [46] |
| Gritsenko et al. 2015 [9] | Breast cancer | Women with mastectomy (4) or lumpectomy (16) for breast cancer | Kinect | Active and passive shoulder motions | Regression coefficients for active movements were used to identify participants with clinically significant shoulder ROM limitation | Laboratory | Participants had good ROM in the shoulder ipsilateral to the breast surgery at the time of testing. Three participants showed clinically significant shoulder motion limitations | Findings support the use of MMC approach as part of the automated screening tool to identify people who have shoulder motion impairment |
| Lee et al. 2015 [64] | AC of shoulder | Healthy volunteers (15); patients with AC (12) | Kinect | Shoulder ROM | Validity of measure shoulder ROM in AC by calculating the agreement of Kinect measurements with measurements obtained using a goniometer, and assessment of its utility for the diagnosis of AC | Laboratory | Measurements of the shoulder ROM using Kinect showed excellent agreement with those taken using a goniometer | “Kinect can be used to measure shoulder ROM and to diagnose AC as an alternative to a goniometer” [64] |
| Tupa et al. 2015 [30] | PD | Patients with PD (18); healthy age-matched individuals (18); students (15) | Kinect | Leg length, normalized average stride length, and gait velocity | A two-layer sigmoidal neural network was used for the classification of gait features (stride length and gait velocity) | Laboratory | Results showed high classification accuracy for the given set of individuals with PD and the age-matched controls | Kinect has potential to be used in the detection of abnormal gait and the recognition of PD |
| Sá et al. 2015 [56] | Schizophrenia | Clinically stable outpatients with schizophrenia (13); healthy controls (16) | BioStage™ | Kinematic parameters and motor patterns during a functional task | Comparison of the kinematic parameters and motor patterns of patients with schizophrenia and those of healthy subjects | Laboratory | Patients with schizophrenia displayed a less developed movement pattern during performance of overarm throwing | “The presence of a less mature movement pattern can be an indicator of neuro-immaturity and a marker for atypical neurological development in schizophrenia” [56] |
| Kim et al. 2016 [47] | Stroke | Patients with hemiplegic stroke (41) | Kinect | Upper extremity motion of 13 of 33 items of upper extremity motor FMA | Correlation of the prediction accuracy for each of the 13 items between real FMA scores and scores using Kinect were analyzed | Laboratory | Prediction accuracies ranged from moderate to good in each item. Correlations were high for the summed score for the 13 items between real FMA scores and scores obtained using Kinect | “Kinect can be a valid way to assess upper extremity function, which may be useful in the setting of unsupervised home-based rehabilitation” [47] |
| Matsenet al. 2016 [75] | Variety of diagnoses (cuff disease, instability, arthritis) | Patients with a variety of diagnoses, including cuff disease, instability, arthritis (32); control healthy subjects (10) | Kinect | Shoulder active ROM | Correlation of Kinect shoulder active ROM measurement with SST | Laboratory | The total SST score was strongly correlated with the range of active abduction. The ability to perform each of the individual SST functions was strongly correlated with active motion | “Kinect provides a clinically practical method for objective measurement of active shoulder motion” [75] |
| Chin et al. 2017 [10] | CP | Children with USCP (31); typically developing children (21) | Kinect v2 | Proprioception | Comparison of proprioceptive ability in children with USCP versus that in typically developing children | Laboratory | Children with USCP showed significant impairments in proprioception compared with typically developing children | The use of MMC technology can clearly identify differences in proprioceptive ability between typically developing children and children with UCSP |
| de Bie et al. 2017 [63] | ALS | Patients diagnosed with ALS (10) | Kinect | Upper extremity reachable workspace RSA | Evaluation of longitudinal changes in upper extremity reachable workspace RSA versus the ALSFRS-R, ALSFRS-R upper extremity sub-scale and FVC | Laboratory | RSA measures were able to detect changes in the upper limbs while the ALSFRS-R could not. The RSA measures were also able to detect a declining trend similar to that of FVC | “Kinect-measured RSA can detect declines in upper extremity ability with more granularity than current tools” [63] |
| Bakhti et al. 2018 [48] | Stroke | Individuals with hemiparetic stroke (19) | Kinect | Movements of 25 predefined body “joints” that approximately correspond to the center of the anatomical joint or body part | Use of ICC and linear regression analysis to quantify the degree to which an ultrasound 3D motion capture system motion capture system and Kinect measurements were related | Laboratory | PANU scores determined by the Kinect were similar to those determined by the ultrasound 3D motion capture system | “The Kinect sensor can accurately and reliably determine the PANU score in clinical routine” [48] |
| Bonnechère et al. 2018 [49] | Stroke | Healthy young adults (40); elderly adults (22); and patients with chronic stroke (10) | Kinect | Parameters including length, angle, velocity, angular velocity, volume, sphere, and surface of upper limb motion | The different scores and parameters were compared for the three groups | Laboratory | Highly significant differences were found for both the shoulders’ total angle, the velocity for young adults and elderly individuals, and patients with stroke | Results of the evaluation could be useful in monitoring patients’ conditions during rehabilitation, while further studies are needed to select which parameters are the most relevant |
| Butt et al. 2018 [15] | PD | Participants with PD (16); healthy people (12) | LMC | PSUP, OPCL, THFF, and POST | Comparison of parameters between a PD group and control group; Supervised learning methods SVM, LR, and NB for classification of patients with PD and healthy subjects | Laboratory | The best performing classifier was the NB. All the other subset features selected by the other feature selection methods, showed the worst classification performance in all ML classifiers (LR, NB, SVM) | “LMC is not yet able to track motor dysfunction characteristics from all MDS- UPDRS proposed exercises” [15] |
| Dranca et al. 2018 [31] | PD | Patients with PD (30) | Kinect | Gait step, limbs angle, and bent angles related to Parkinson disease | Classification of different PD stages by the features from FoG using classification algorithms | Hospital | The accuracy obtained for a particular case of a Bayesian Network classifier built from a set of 7 relevant features is 93.40% | “Using Kinect is adequate to build an inexpensive and comfortable system that classifies PD into three different stages related to FoG” [31] |
| Li et al. 2018 [25] | PD | Patients with PD (9) | Consumer grade video camera | 416 features including kinematics, frequency distribution extracted from 14 joint angle positions | Quantifying the severity of levodopa-induced dyskinesia by video-based features | Laboratory | Features achieved similar or superior performance to the UDysRS for detecting the onset and remission of dyskinesia | “The proposed system provides insight into the potential of computer vision and deep learning for clinical application in PD ” [25] |
| Li et al. 2018 [32] | PD | Patients with PD after DBS (24) | Ordinary 2D video camera | TUG sub-task segmentation | Frame classification algorithm to classify video frame in sub tasks of TUG test | Semi-controlled environments | Classification accuracies for the sub-tasks ‘Walk,’ ‘Walk-Back,’ and ‘Sit-Back’ are apparently higher than that of the other three sub-tasks | The results support that clinical parameters for the assessment of PD can be automatically acquired from TUG videos |
| Martinez et al. 2018 [26] | PD | Patients with PD (6); healthy subjects (6) | DARI system | BME of 16 different movements | UPDRS-III and BME of 16 different movements in six controls paired by age and sex were compared with those in PD populations with DBS in ‘on’ and ‘off’ states | Laboratory | A better performance in the BME was correlated with a lower UPDRS-III score. No statistically significant difference between patients in ‘on’ and ‘off’ states of DBS regarding BME | The DARI MMC system is accurate in PD classification |
| Pantzar-Castilla et al. 2018 [45] | CP | Participants with CP (18) | Kinect 2 for Xbox One | Gait variables (i.e., Knee flexion at initial contact; Maximum knee flexion at loading response; Minimum knee flexion in stance; Maximum knee flexion in swing) | Comparison of 2D MMC and 3D marker-based gait analysis methods for the selected variables | Laboratory | The reliability within 2D Markerless and 3D gait analysis was mostly good to excellent | 2D MMC is a convenient tool that could be used to assess the gait in children with CP |
| Rammer et al. 2018 [67] | Pediatric manual wheelchair users | Pediatric manual wheelchair users (30) | Kinect 2.0 | Upper extremity kinematics during manual wheelchair propulsion (i.e., joint range of motion and musculotendon excursion) | Kinematic parameters were used to develop and evaluate a markerless wheelchair propulsion biomechanical assessment system | Laboratory | Inter-trial repeatability of spatiotemporal parameters, joint range of motion, and musculotendon excursion were all found to be significant | “A markerless wheelchair propulsion kinematic assessment system is a repeatable measurement tool for pediatric manual wheelchair users” [67] |
| Langevin et al. 2019 [16] | PD | Patients with PD (127); healthy controls (127) | Webcam | Frequencies of hand movement in hand motor task | Comparison of the differences in the hand motion between the groups with and without PD | Home Setting | PD group had a mean frequency that is lower than the control group in the hand motor tasks | “Online framework that assesses features of PD could be introduced during a clinic visit to initially supplement the tool with personal support” [16] |
| Lee et al. 2019 [17] | PD | Participants with PD that are receiving benefit from DBS (8) | LMC | PSUP, OPCL, and THFF tasks during ‘on’ and ‘off’ condition, amplitude, frequency, velocity, slope, and variance were extracted from each movement | Correlation of the kinematic features with the overall bradykinesia severity score (average MDS-UPDRS ratings across tasks) | Laboratory | An exhaustive LOSOCV assessment identified PSUP, OPCL, and THFF as the best task combination for predicting overall bradykinesia severity | “Data obtained from the LMC can predict the overall bradykinesia severity in agreement with clinical observations and can provide reliable measurements over time” [17] |
| Liu et al. 2019 [18] | PD | Patients with PD (60) | Camera | Periodic pattern of hand movements in finger tapping, hand clasping and hand pro/supination | Correlation analysis on each feature parameter and clinical assessment scores; Classification of bradykinesia | Semi-controlled environment | Classification accuracy in 360 examination videos is 89.7% | Reliable assessment results on Parkinsonian bradykinesia can be produced from video with minimal device requirement |
| Sato et al. 2019 [33] | PD | Patients with PD (117 in phase I and 2 in phase II); healthy controls (117) | Home video camera |
Cadence , gait frequency, gait speed, step length, step width, foot clearance |
Estimation of cadence of periodic gait steps from the sequential gait features using the short-time pitch detection approach | Structured environment | Cadence estimation of gait in its coronal plane in the daily clinical setting was successfully conducted in normal gait movies using ST-ACF | 2D movies recorded with a home video camera is helpful in identifying an effective gait and calculate its cadence in normal and pathological gaits |
| Vivar et al. 2019 [8] | PD | Patients with PD (20) | LMC | Tremor levels measured during hand extension and pushing the ball action | Classification of tremor level in PD according to the MDS-UPDRS standard | Laboratory | The proposed method classified the patient measurements following MDS-UPDRS in tremor levels 0, 1, and 2 with high accuracy | “It is possible to classify the different levels of tremor in patients with PD using only two statistical features, such as homogeneity and contrast” [8] |
| Caruso et al. 2020 [52] | ASD | Infants with high risk of ASD (50); infants with low risk of ASD (53) | Video recording | Quantity of motion, centroid of motion, presence of repetitive movements in the motion of limbs | Kinematic parameters related to upper and lower limb movements in infants with low risk and high risk of ASD | Bed | Early developmental trajectories of specific motor parameters were different in high-risk infants later diagnosed with neurodevelopmental diseases from those of infants developing typically | “Computer-based analysis of infants’ movements may support and integrate the analysis of motor patterns of infants at risk of neurodevelopmental diseases in research settings” [52] |
| Chambers et al. 2020 [66] | Neuromotor disease | Infants at risk of neuromotor impairment (19); healthy infants (85) | GoPro cameras, YouTube video | Absolute position and angle, variability of posture, velocity of movement, variability of movement, complexity, left-right symmetry of movement | Extent of kinematic features from infants at risk deviate from the group of healthy infants as reflected by Naïve Gaussian Bayesian Surprise metric | Childcare facility, hospital, natural environment | Infants who are at high risk for impairments deviate considerably from the healthy group | “Markerless tracking promises to improve accessibility to diagnostics, monitor naturalistic movements, and provide a quantitative understanding of infant neuromotor disorders” [66] |
| Fujii et al. 2020 [70] | Patients with gait disturbance | Patients with gait ataxia (6); control subjects (6) | Kinect 2, migrated to Azure Kinect | Gait parameters (e.g., walking speed and stride length) | Gait comparison between the patient group and the healthy subject group | Laboratory | Significant differences were observed between the patient group and the healthy subject group in terms of the mean value and variation of stride length | “A low-cost noninvasive motion capture device can be used for the objective clinical assessment of patients with stroke and PD who display manifestations of gait and motor deficits” [70] |
| Hu et al. 2020 [34] | PD | Patients with PD (45) | Video | Gait parameters, motion patterns | Automatic FoG detection by fine-grained human action recognition method | Structured environment | The experimental results demonstrate the superior performance of the proposed method over the state-of-the-art methods | “Anatomic joint graph representation provides clinicians an intuitive interpretation of the detection results by localizing key vertices in a FoG video” [34] |
| Krasowicz et al. 2020 [42] | CP | Patients with diagnosed ICP (8) | 4DBODY system | TMFPI developed based on movement sequences | TMFPI compared with the assessment made according to the GMFM-88 scale | Laboratory | The system provided results agreeable with the clinical indicator GMFM-88 and with clinical observations of a PT | “The conducted assessments indicated that the use of dynamic 3D surface measurements is a promising direction of research and can provide valuable information on patient movement patterns” [42] |
| Lin et al. 2020 [19] | PD | Patients with PD (121) | iPhone 6s Plus | Motor behaviors, including stability, completeness, and self-similarity | Quantification of motor behaviors in patients with PD and bradykinesia recognition by a periodic motion-based network consisting of an autoencoder and fully connected neural network | Laboratory | The proposed periodic motion model delivers the F-score of 0.7778 for bradykinesia recognition | Using single RGB video for bradykinesia recognition is easy and convenient for patients and doctors |
| Oña et al. 2020 [39] | PD | Patients with PD (20) | LMC | Manual dexterity in BBT | Evaluation the validity of VR-BBT to reliably measure the manual dexterity | Laboratory | VR-BBT significantly correlated with the conventional assessment of the BBT | “VR-BBT could be used as a reliable indicator for health improvements in patients with PD” [39] |
| Pang et al. 2020 [20] | PD | Patients with PD; healthy controls (22) | Logitech HD Pro C920 webcams | Hand motion in tap thumb to the finger, creating a fist, pronation and supination of hand and resting state | Measurement of parkinsonian symptomology using automated analysis of hand gestures | Structured environment | Behavior of patients with PD and control subjects can be distinguished by analyzing the detailed motion features of their hands/fingers | Automatic hand movement detection method may help clinicians to identify tremor and bradykinesia in PD |
| Sabo et al. 2020 [58] | Dementia | Older adults with dementia (14) | Kinect | Gait parameters including cadence, average and minimum margin of stability per step, average step width, coefficient of variation of step width and time, the symmetry index of the step times, number of steps in the walking bout | Correlation and regression of gait features with clinical scores UPDRS and SAS | Hospital | Gait features extracted from both 2D and 3D videos are correlated to UPDRS-gait and SAS-gait scores of parkinsonism severity in gait | “Vision-based systems have the potential to be used as tools for longitudinal monitoring of parkinsonism in residential settings” [58] |
| Schroeder et al. 2020 [43] | CP | High-risk infants (29) | Kinect v1 | Infants’ general movement | Correlation of expert GMA ratings of standard RGB videos with GMA ratings on SMIL motion videos of the same sequence | Clinical environment | GMA based on computer-generated virtual 3D infant body models closely corresponded to the established gold standard based on conventional RGB videos | SMIL motion video might capture the movement characteristics required for GMA of infants |
| Williams et al. 2020 [21] | PD | Patients with PD (20); control participants (15) | Smartphone | Bradykinesia assessed by finger tapping | ML models to predict no/slight bradykinesia or mild/moderate/severe bradykinesia, and presence or absence of Parkinson’s diagnosis | Clinical setting |
SVM with radial basis function kernels predicted presence of mild/moderate/ severe bradykinesia with good accuracy. NB model predicted the presence of PD with moderate accuracy |
The proposed approach supports the detection of bradykinesia without purchasing extra hardware devices |
| Williams al. 2020 [22] | PD | Patients with idiopathic PD (39); healthy controls (30) | Smartphone | Bradykinesia assessed by finger tapping | Correlation of machine learning models with clinical ratings of bradykinesia | Clinical setting | Computer measures correlated well with clinical ratings of bradykinesia | “The research provides a new tool to quantify bradykinesia. It could potentially be used to support diagnosis and monitoring of PD” [22] |
| Zefinetti et al. 2020 [62] | SCI patients using a wheelchair | Patients with SCI (60) | Kinect v2 | Kinematic data, including humeral elevation, horizontal abduction of humerus, humeral rotation, elbow flexion, trunk flexion/extension of wheelchair propulsion | Correlation between the movements and the patients’ assessment | Laboratory | The measurements computed by the proposed system showed a good reliability for analyzing the movements of SCI patients’ wheelchair propulsion | “The proposed markerless solutions are useful for an adequate evaluation of wheelchair propulsion” [62] |
| Abbas et al. 2021 [57] | Schizophrenia | Patients with Schizophrenia (18); healthy controls (9) | Smartphone | Head movement | Comparison of head movement measurements between patients and healthy controls, relationship of head movement to schizophrenia symptom severity | Home setting/ Natural environment | Rate of head movement in participants with schizophrenia and those without differed significantly; head movement was a significant predictor of schizophrenia diagnosis | “Remote, smartphone- based assessments were able to capture meaningful visual behavior for computer vision-based objective measurement of head movement” [57] |
| Ardalan et al. 2021 [71] | Neurodevelopmental Disorders | Children with 16p11.2 mutation (15); TD children (12) | A single point-and-shoot camera | Gait synchrony, balance parameters | Comparison of gait synchrony and balance in children with 16p11.2 mutation and TD children | Natural environment | Children with 16p11.2 mutation had significantly less whole-body gait synchrony and poorer balance compared to TD children | Remote video analysis approach facilitates the research in motor analysis in children with developmental disorders |
| Cao et al. 2021 [35] | PD | Patients with PD (18); healthy controls (42) | RGB camera | Shuffling step | Detection of shuffling step and severity assessment | Hospital | 3D convolution on videos achieves an average shuffling step detection accuracy of 90.8% | Video-based detection method might facilitate more frequent assessment of FoG in a more cost-effective way |
| Hurley et al. 2021 [69] | Patients awaiting TKR who were attending POAC | Patients awaiting unilateral primary TKR (23) | BioStage™ | LLM, VVM | Comparison of LLM and VVM performed clinically, radiologically, and using MMA | Laboratory | Discrepancies existed in LLM and VVM when evaluated using clinical, radiological, and MMA modalities | The MMC system should not be the only method to assess the patients for TKR |
| Kojovic et al. 2021 [55] | ASD | Children with ASD (169); TD children (68) | 2D camera | Patterns of atypical postures and movements | Differentiation between children with ASD and TD using non-verbal aspects of social interaction by deep neural network | Clinical setting | The classification accuracy is 80.9% with the prediction probability positively correlated to the overall level of symptoms of autism in social affect and repetitive and restricted behaviors domain | Remote machine learning-based ASD screening might be possible in the future |
| Lee et al. 2021 [50] | Stroke | Patient with stroke (206) | Smartphone | Swing time asymmetry between paretic and non-paretic lower limbs while walking | Classification of dependence in ambulation by employing a deep model in 3D-CNN | Hospital | The trained 3D-CNN performed with 86.3% accuracy, 87.4% precision | “Monitoring ambulation using videos may facilitate the design of personalized rehabilitation strategies for stroke patients with ambulatory and balance deficits in the community” [50] |
| Li et al. 2021 [23] | PD | Patients with PD (157) | Video | Skeleton sequence from finger-tapping test | Classification of finger tapping performance according to MDS-UPDRS score | Hospital | Fine-grained classification net- work achieved an accuracy of 72.4% and an acceptable accuracy of 98.3% | Vision-based assessment method has potential for remote monitoring of PD patients in the future |
| Mehdizadeh et al. 2021 [59] | Dementia | Individuals admitted to a specialized dementia inpatient unit (54) | Kinect v2 | Gait variables, including gait stability, step length, step time variability, step length variability | Changes in quantitative gait measured over a period during a psychogeriatric admission | Laboratory | Results showed that there was deterioration of gait in this cohort of participants, with men exhibiting greater decline in gait stability | “Quantitative gait monitoring in hospital environments may provide opportunities to intervene to prevent adverse events, decelerate mobility decline, and monitor rehabilitation outcomes” [59] |
| Negin et al. 2021 [53] | ASD | Children with or without ASD (108) | YouTube video | Spinning, head banging, hand action, arm flapping | Recognition of ASD associated behaviors | Natural environment | HOF descriptor achieves the best results when used with MLP classifier | “An action-recognition-based system can be potentially used to assist clinicians to provide a reliable, accurate, and timely diagnosis of ASD disorder” [53] |
| Nguyen-Thai et al. 2021 [44] | CP | Videos of infants who were at 14–15 weeks post-term age (235) | Smartphone | FM | Predicted the risk of CP by FM | Natural environment | Pose sequences were strong signals that retained motion information of joints and limbs while ignoring irrelevant, distracting visual artifacts | A STAM model can be used to identify infants at risk of cerebral palsy via video-based infant movement assessment |
| Rupprechter et al. 2021 [36] | PD | Patients with PD (729) | Smartphone | Leg ratio difference, vertical angle of the body, horizontal angle of the ankles and wrists, horizontal distance between the heels, speed of the ankles, step frequency | Estimation of severity of gait impairment in Parkinson’s disease using a computer vision-based methodology | Hospital and offices | Step frequency point estimates from the Bayesian model were highly correlated with manually labelled step frequencies | “Automated systems for quantifying Parkinsonian gait have great potential to be used in combination with, or the absence of, trained assessors, during assessments in the clinic or at home” [36] |
| Stricker et al. 2021 [37] | PD | Patients with PD (24) | Standard camera | Step length | Reliability of step length measurements from 2D video in patients with stroke; comparison of the step lengths of patients with/without a recent history of falls | Structured environment | Step length measurements from the video demonstrated excellent intra- and inter-rater reliability; patients with PD who had experienced a fall within the previous year demonstrated shorter step lengths | “Quantification of step length from 2D video may offer a feasible method for clinical use” [37] |
| Wei et al. 2021 [68] | Wheelchair user | Full-time wheelchair users (91) | Kinect | Wheelchair transfer motions including joint angles and positions | ML algorithm for evaluation of the quality of independent wheelchair sitting pivot transfers | Structured environment | Accuracies of the ML classifier were over 71% | “The results show promise for the objective assessment of the transfer technique using a low cost camera and machine learning classifiers” [68] |
| Williams et al. 2021 [72] | Tremor | Patients with PD (9); patients with essential tremor (5); patient with functional tremor (1) | Smartphone | Hand tremor at rest and in posture | Measurement of hand tremor frequency | Clinical setting | There was less than 0.5 Hz difference between the computer vision and accelerometer frequency measurements in 97% of the videos | “The study suggests a potential new, contactless point-and-press measure of tremor frequency within standard clinical settings, research studies, or telemedicine” [72] |
| Wu et al. 2021 [40] | PD | Patients with PD (7) | LMC | Hand kinematic in finger tapping hand opening and closing, and hand pronation and supination | Quantification of the motor component of bradykinesia | Laboratory | Average velocity and average amplitude of pronation/supination isolate the bradykinetic feature | “The LMC achieved promising results in evaluating PD patients’ hand and finger bradykinesia” [40] |
| Ferrer-Mallol et al. 2022 [73] | DMD | Patients with DMD (8) | Smartphone | Time, pattern of movement trajectory, smoothness and symmetry of movement | Quantitative measurement of the motor performance of the patients in the functional tasks | Home | Computer vision analysis allowed characterization of movement in an objective manner | “Video technology offers the possibility to perform clinical assessments and capture how patients function at home, causing minimal disruption to their lives” [73] |
| Guo et al. 2022 [24] | PD | Patients with PD (48); healthy controls (11) | RGB camera | Finger movement in finger tapping test | Classification of PD from finger tapping video | Structured environment | Classification accuracy is of 81.2% on a newly established 3D PD hand dataset of 59 subjects | Novel computer-vision approach could be effective in capturing and evaluating the 3D hand movement in patients with PD |
| Lonini et al., 2022 [51] | Stroke | Patients with stroke (8) | Digital RGB video camera | Gait parameters including cadence, double support time, swing time, stance time, and walking speed | Comparison of gait parameters obtained from clinical system and video-based method for gait analysis | Laboratory | Absolute accuracy and precision for swing, stance, and double support time were within 0.04 ± 0.11 s | “Single camera videos and pose estimation models based on deep networks could be used to quantify clinically relevant gait metrics in individuals poststroke” [51] |
| Morinan et al. 2022 [38] | PD | Videos from patients with PD (447) | Smartphone | Body kinematics including movement, velocity variation and smoothness | Estimation of ‘arising from chair’ task score in MDS-UPDRS | Clinical setting | Compute-vision based method can accurately quantify PD patients’ ability to perform the arising from chair action | Computer-vision based approach might be used for quality control and reduction of human error by identifying unusual clinician ratings |
| Vu et al. 2022 [74] | CD | Patients with CD (93) | Video recording | Peak power, frequency, and directional dominance of head movement | Quantification of oscillatory and directional aspects of HT | Structured environment | Computer-vision based method of quantification of HT exhibits convergent validity with clinical severity ratings | “Objective methods for quantifying HT can provide a reliable outcome measure for clinical trials” [74] |
| Morinan et al. 2023 [27] | PD | Patients with PD (628) | Consumer-grade hand- held devices | Movements during the bradykinesia examinations including finger tapping, hand movement, pronation-supination, toe tapping, leg agility | Quantification of bradykinesia according to clinician ratings | Clinical setting and laboratory | Classification model estimate of composite bradykinesia had high agreement with the clinician ratings | Computer vision technology with smartphone/ tablet devices can be adopted in the current clinical workflows |
| Song et al. 2023 [54] | ASD | Children with ASD (29); TD child (1) | RGB camera | Head and body movement during response to name behavior | Prediction of ASD by response to name behavior | Structured environment | The prediction method is highly consistent with clinical diagnosis | Automatic detection method can help to carry out remote autism screening in the early developmental stage of children |
3D-CNN: 3D Convolutional Neural Network; AC: Adhesive Capsulitis; ALS: Amyotrophic Lateral Sclerosis; ALSFRS-R: Revised Amyotrophic Lateral Sclerosis Functional Rating Scale; ASD: Autism Spectrum Disorder; BME: Body Motion Evaluation; CCD: Commercial Digital Charge-coupled Device; CD: cervical dystonia; CP: Cerebral Palsy; CV: Computer Vision; DBS: Deep Brain Stimulation; DMD: Duchenne muscular dystrophy; FM: Fidgety Movement; FMA: Fugl-Meyer Assessment; FoG: Freezing of Gait; FoG: Freezing of gait; SAS: Simpson- Angus Scale; FVC: Forced Vital Capacity; FXS: Fragile X Syndrome; GMA: General Movement Assessment; GMFM-88: Gross Motor Function Measure-88; HOF: Histogram of Optical Flow; HT: Head Tremor; ICC: Intra-Class Correlation Coefficient; ICP: Infantile Cerebral Palsy; KPCA: Kernel-based Principal Component Analysis; LDA: Linear Discriminant Analysis; LLM: Leg Length Measurement; LMC: Leap Motion Controller; LOSOCV: Leave-One-Subject-Out Cross-Validation; LR: Logistic Regression; MDS-UPDRS: Movement Disorder Society-Sponsored Revision of the Unified Parkinson’s Disease Rating Scale; ML: Machine Learning; MLP: Multi-layer Perceptron; MMA: Markerless Motion Analysis; MMC: Markerless Motion Capture; NB: Naïve Bayes; NN: Neural Network; OPCL: Hand Opening/Closing; PANU: Proximal Arm Non-Use; PCA: Principal Component Analysis; PD: Parkinson’s Disease; PFP: Patellofemoral pain; POAC: Pre-Operative Assessment Clinic; POST: Postural Tremor; PSUP: Forearm Pronation-Supination; PT: Physiotherapist; RGB: Red Green Blue; ROM: Range of Motion; RSA: Relative Surface Area; SCI: Spinal Cord Injured; SDK: Software Development Kit; SMIL: Skinned Multi-Infant Linear Body Model; SST: Simple Shoulder Test; ST-ACF: short-time autocorrelation function; STAM: Spatio-Temporal Attention-Based Model; SVM: Support Vector Machine; TD: Typically Developing; THFF: Thumb Forefinger Tapping; TKR: Total Knee Arthroplasty; TMFPI: Trunk Mobility in the Frontal Plane Index; UDysPS: Unified Dyskinesia Rating Scale; UPDRS: Unified Parkinson’s Disease Rating Scale; UPDRS-FT: Unified Parkinson’s Disease Rating Scale-Finger Tapping; USCP: Unilateral Spastic Cerebral Palsy; VR: Virtual Reality; VVM: Varus/Valgus Knee Measurements