Evaluation of the Microsoft Kinect as a clinical assessment tool of body sway

The Healthy Brain Initiative (HBI): A prospective cohort study protocol

BACKGROUND

The Health Brain Initiative (HBI), established by University of Miami's Comprehensive Center for Brain Health (CCBH), follows racially/ethnically diverse older adults without dementia living in South Florida. With dementia prevention and brain health promotion as an overarching goal, HBI will advance scientific knowledge by developing novel assessments and non-invasive biomarkers of Alzheimer's disease and related dementias (ADRD), examining additive effects of sociodemographic, lifestyle, neurological and biobehavioral measures, and employing innovative, methodologically advanced modeling methods to characterize ADRD risk and resilience factors and transition of brain aging.

METHODS

HBI is a longitudinal, observational cohort study that will follow 500 deeply-phenotyped participants annually to collect, analyze, and store clinical, cognitive, behavioral, functional, genetic, and neuroimaging data and biospecimens. Participants are ≥50 years old; have no, subjective, or mild cognitive impairment; have a study partner; and are eligible to undergo magnetic resonance imaging (MRI). Recruitment is community-based including advertisements, word-of-mouth, community events, and physician referrals. At baseline, following informed consent, participants complete detailed web-based surveys (e.g., demographics, health history, risk and resilience factors), followed by two half-day visits which include neurological exams, cognitive and functional assessments, an overnight sleep study, and biospecimen collection. Structural and functional MRI is completed by all participants and a subset also consent to amyloid PET imaging. Annual follow-up visits repeat the same data and biospecimen collection as baseline, except that MRIs are conducted every other year after baseline.

ETHICS AND EXPECTED IMPACT

HBI has been approved by the University of Miami Miller School of Medicine Institutional Review Board. Participants provide informed consent at baseline and are re-consented as needed with protocol changes. Data collected by HBI will lead to breakthroughs in developing new diagnostics and therapeutics, creating comprehensive diagnostic evaluations, and providing the evidence base for precision medicine approaches to dementia prevention with individualized treatment plans.

The Healthy Brain Initiative (HBI): A prospective cohort study protocol

Background

The Health Brain Initiative (HBI), established by University of Miami's Comprehensive Center for Brain Health (CCBH), follows racially/ethnically diverse older adults without dementia living in South Florida. With dementia prevention and brain health promotion as an overarching goal, HBI will advance scientific knowledge by developing novel assessments and non-invasive biomarkers of Alzheimer's disease and related dementias (ADRD), examining additive effects of sociodemographic, lifestyle, neurological and biobehavioral measures, and employing innovative, methodologically advanced modeling methods to characterize ADRD risk and resilience factors and transition of brain aging.

Methods

HBI is a longitudinal, observational cohort study that will follow 500 deeply-phenotyped participants annually to collect, analyze, and store clinical, cognitive, behavioral, functional, genetic, and neuroimaging data and biospecimens. Participants are ≥50 years old; have no, subjective, or mild cognitive impairment; have a study partner; and are eligible to undergo magnetic resonance imaging (MRI). Recruitment is community-based including advertisements, word-of-mouth, community events, and physician referrals. At baseline, following informed consent, participants complete detailed web-based surveys (e.g., demographics, health history, risk and resilience factors), followed by two half-day visits which include neurological exams, cognitive and functional assessments, an overnight sleep study, and biospecimen collection. Structural and functional MRI is completed by all participants and a subset also consent to amyloid PET imaging. Annual follow-up visits repeat the same data and biospecimen collection as baseline, except that MRIs are conducted every other year after baseline.

Ethics and expected impact

HBI has been approved by the University of Miami Miller School of Medicine Institutional Review Board. Participants provide informed consent at baseline and are re-consented as needed with protocol changes. Data collected by HBI will lead to breakthroughs in developing new diagnostics and therapeutics, create comprehensive diagnostic evaluations, and provide the evidence base for precision medicine approaches to dementia prevention with individualized treatment plans.

Feasibility of a kinect-based system in assessing physical function of the elderly for home-based care

BACKGROUND

With concerns about accurate diagnosis through telehealth, the Kinect sensor offers a reliable solution for movement analysis. However, there is a lack of practical research investigating the suitability of a Kinect-based system as a functional fitness assessment tool in homecare settings. Hence, the objective of this study was to evaluate the feasibility of using a Kinect-based system to assess physical function changes in the elderly.

METHODS

The study consisted of two phases. Phase one involved 35 young healthy adults, evaluating the reliability and validity of a Kinect-based fitness evaluation compared to traditional physical examination using the intraclass correlation coefficient (ICC). Phase two involved 665 elderly subjects, examining the correlation between the Kinect-based fitness evaluation and physical examination through Pearson's correlation coefficients. A Kinect sensor (Microsoft Xbox One Kinect V2) with customized software was employed to capture and compute the movement of joint centers. Both groups performed seven functional assessments simultaneously monitored by a physical therapist and the Kinect system. System usability and user satisfaction were assessed using the System Usability Scale (SUS) and Questionnaire for User Interface Satisfaction (QUIS), respectively.

RESULTS

Kinect-based system showed overall moderate to excellent within-day reliability (ICC = 0.633-1.0) and between-day reliability (ICC = 0.686-1.0). The overall agreement between the two devices was highly correlated (r ≧ 0.7) for all functional assessment tests in young healthy adults. The Kinect-based system also showed a high correlation with physical examination for the functional assessments (r = 0.858-0.988) except functional reach (r = 0.484) and walking speed(r = 0.493). The users' satisfaction with the system was excellent (SUS score = 84.4 ± 18.5; QUIS score = 6.5-6.7).

CONCLUSIONS

The reliability and validity of Kinect for assessing functional performance are generally favorable. Nonetheless, caution is advised when employing Kinect for tasks involving depth changes, such as functional reach and walking speed tests for their moderate validity. However, Kinect's fundamental motion detection capabilities demonstrate its potential for future applications in telerehabilitation in different healthcare settings.

Evaluation of Arm Swing Features and Asymmetry during Gait in Parkinson's Disease Using the Azure Kinect Sensor

Arm swinging is a typical feature of human walking: Continuous and rhythmic movement of the upper limbs is important to ensure postural stability and walking efficiency. However, several factors can interfere with arm swings, making walking more risky and unstable: These include aging, neurological diseases, hemiplegia, and other comorbidities that affect motor control and coordination. Objective assessment of arm swings during walking could play a role in preventing adverse consequences, allowing appropriate treatments and rehabilitation protocols to be activated for recovery and improvement. This paper presents a system for gait analysis based on Microsoft Azure Kinect DK sensor and its body-tracking algorithm: It allows noninvasive full-body tracking, thus enabling simultaneous analysis of different aspects of walking, including arm swing characteristics. Sixteen subjects with Parkinson's disease and 13 healthy controls were recruited with the aim of evaluating differences in arm swing features and correlating them with traditional gait parameters. Preliminary results show significant differences between the two groups and a strong correlation between the parameters. The study thus highlights the ability of the proposed system to quantify arm swing features, thus offering a simple tool to provide a more comprehensive gait assessment.

Enhanced assessment of human dynamic stability by eliminating the effect of body height: modeling and experiment study

Margin of stability (MOS) is one of the essential indices for evaluating dynamic stability. However, there are indications that MOS was affected by body height and its application in identifying factors on dynamic stability other than body height is restricted. An inverted pendulum model was used to simulate human walking and investigate the relevance between MOS and body height. Eventually, a height-independent index in dynamic stability assessment (named as Angled Margin of Stability, AMOS) was proposed. For testing, fifteen healthy young volunteers performed walking trials with normal arm swing, holding arms, and anti-normal arm swing. Kinematic parameters were recorded using a gait analysis system with a Microsoft Kinect V2.0 and instrumented walkway. Both simulation and test results show that MOS had a significant correlation with height during walking with normal arm swing, while AMOS had no such significant correlation. Walking with normal arm swing produced significantly larger AMOS than holding arms and anti-normal arm swing. However, no significant difference showed up in MOS between normal arm swing and holding arms. The results suggest that AMOS is not affected by body height and has the potential to identify the variations in dynamic stability caused by physiological factors other than body height.

Kinect-Based Assessment of Lower Limbs during Gait in Post-Stroke Hemiplegic Patients: A Narrative Review

The aim of this review was to present an overview of the state of the art in the use of the Microsoft Kinect camera to assess gait in post-stroke individuals through an analysis of the available literature. In recent years, several studies have explored the potentiality, accuracy, and effectiveness of this 3D optical sensor as an easy-to-use and non-invasive clinical measurement tool for the assessment of gait parameters in several pathologies. Focusing on stroke individuals, some of the available studies aimed to directly assess and characterize their gait patterns. In contrast, other studies focused on the validation of Kinect-based measurements with respect to a gold-standard reference (i.e., optoelectronic systems). However, the nonhomogeneous characteristics of the participants, of the measures, of the methodologies, and of the purposes of the studies make it difficult to adequately compare the results. This leads to uncertainties about the strengths and weaknesses of this technology in this pathological state. The final purpose of this narrative review was to describe and summarize the main features of the available works on gait in the post-stroke population, highlighting similarities and differences in the methodological approach and primary findings, thus facilitating comparisons of the studies as much as possible.

Comparison of Azure Kinect and optical retroreflective motion capture for kinematic and spatiotemporal evaluation of the sit-to-stand test

BACKGROUND

The sit-to-stand test (STS) is commonly used to evaluate functional capabilities within a variety of clinical populations. Traditionally STS is a timed test, limiting the depth of information which can be gained from its evaluation. The Azure Kinect has the potential to add in-depth analysis to STS. Despite these potential benefits, the recently released (2019) Azure Kinect has yet to be evaluated for its ability to accurately assess STS.

RESEARCH QUESTIONS

Purposes of this work were to compare data captured during STS using both a 12 camera Vicon motion capture system and the Azure Kinect; and to calculate kinematic and spatiotemporal variables related to the four phases of the STS cycle.

METHODS

Spatiotemporal and kinematic measures for STS were simultaneously collected by both devices for 15 participants. Cycle waveforms were compared for right and left hip and knee flexion/extension angular displacement, right and left hip and knee flexion/extension angular velocity, and knee-to-ankle separation ratio. Evaluated discrete outcome variables included: phase time points, maximum knee extension velocity from phases 3 to 4, medial-lateral pelvic sway range, and total time to completion. Waveform summary data were compared using R, R², and RMSE. Discrete variables were analyzed using Spearman's Rank correlation coefficient.

RESULTS

R and R² values between the two systems indicated high levels of correlation (all R values > 0.711, all R² values > 0.660). Although there was an overall high level of agreement between waveform shapes, high RMSE values indicated some minor tracking errors for Kinect within the STS cycle. Spearman's Rank correlation coefficient indicated high levels of correlation between the systems for discrete variables (all R values > 0.89), with the exception of medial-lateral pelvic sway range.

SIGNIFICANCE

The Azure Kinect provides valuable insight into STS movement strategies allowing for improved precision in clinical decision making across multiple clinical populations.

Comparison of Motion Analysis Systems in Tracking Upper Body Movement of Myoelectric Bypass Prosthesis Users

Current literature lacks a comparative analysis of different motion capture systems for tracking upper limb (UL) movement as individuals perform standard tasks. To better understand the performance of various motion capture systems in quantifying UL movement in the prosthesis user population, this study compares joint angles derived from three systems that vary in cost and motion capture mechanisms: a marker-based system (Vicon), an inertial measurement unit system (Xsens), and a markerless system (Kinect). Ten healthy participants (5F/5M; 29.6 ± 7.1 years) were trained with a TouchBionic i-Limb Ultra myoelectric terminal device mounted on a bypass prosthetic device. Participants were simultaneously recorded with all systems as they performed standardized tasks. Root mean square error and bias values for degrees of freedom in the right elbow, shoulder, neck, and torso were calculated. The IMU system yielded more accurate kinematics for shoulder, neck, and torso angles while the markerless system performed better for the elbow angles. By evaluating the ability of each system to capture kinematic changes of simulated upper limb prosthesis users during a variety of standardized tasks, this study provides insight into the advantages and limitations of using different motion capture technologies for upper limb functional assessment.

Classification of Older and Fall-Experienced Subjects by Postural Sway Data using Mass Spring Damper Model

The quiet standing test is used to detect diseases of the postural control system. The descriptive statistics of the center of pressure (COP) of older people during the test tend to be larger than those of healthy young people, but they cannot indicate structural problems in postural control. COP trajectories can be mathematically modeled with structural parameters such as viscosity, stiffness, and stochastic terms; however, the classification accuracy of older and fall-experienced people using such parameters has not been sufficiently verified. In this study, six structural parameters of a mass-spring-damper (MSD) model were estimated using two datasets, in which a total of 212 subjects performed quiet standing tests under four conditions. The estimated parameters were used for classification with a random forest algorithm to examine the differences in classification accuracy compared to seven conventional descriptive statistics methods. For the classification of older subjects, the classification accuracy of the MSD parameter method was the highest in foam condition, with positive likelihood ratios approximately 8.0. For the classification of fall-experienced subjects, the positive likelihood ratio of the MSD parameter method was 5.0, which is better than conventional descriptive statistics. Various MSD parameters revealed that aging and changing the floor surface and visual conditions cause oscillations in the COP behavior. While the MSD parameters were confirmed to help classify older subjects more accurately than the conventional descriptive statistics, there was room for further improvement in the classification accuracy of fall-experienced subjects.

Kinect-based rapid movement training to improve balance recovery for stroke fall prevention: a randomized controlled trial

Background

Falls are more prevalent in stroke survivors than age-matched healthy older adults because of their functional impairment. Rapid balance recovery reaction with adequate range-of-motion and fast response and movement time are crucial to minimize fall risk and prevent serious injurious falls when postural disturbances occur. A Kinect-based Rapid Movement Training (RMT) program was developed to provide real-time feedback to promote faster and larger arm reaching and leg stepping distances toward targets in 22 different directions.

Objective

To evaluate the effectiveness of the interactive RMT and Conventional Balance Training (CBT) on chronic stroke survivors’ overall balance and balance recovery reaction.

Methods

In this assessor-blinded randomized controlled trial, chronic stroke survivors were randomized to receive twenty training sessions (60-min each) of either RMT or CBT. Pre- and post-training assessments included clinical tests, as well as kinematic measurements and electromyography during simulated forward fall through a “lean-and-release” perturbation system.

Results

Thirty participants were recruited (RMT = 16, CBT = 14). RMT led to significant improvement in balance control (Berg Balance Scale: pre = 49.13, post = 52.75; P = .001), gait control (Timed-Up-and-Go Test: pre = 14.66 s, post = 12.62 s; P = .011), and motor functions (Fugl-Meyer Assessment of Motor Recovery: pre = 60.63, post = 65.19; P = .015), which matched the effectiveness of CBT. Both groups preferred to use their non-paretic leg to take the initial step to restore stability, and their stepping leg’s rectus femoris reacted significantly faster post-training (P = .036).

Conclusion

The RMT was as effective as conventional balance training to provide beneficial effects on chronic stroke survivors’ overall balance, motor function and improving balance recovery with faster muscle response.

Trial registration: The study was registered at Clinicaltrials.gov (https://clinicaltrials.gov/ct2/show/NCT03183635, NCT03183635) on 12 June 2017.

Supporting the Assessment of Hereditary Transthyretin Amyloidosis Patients Based On 3-D Gait Analysis and Machine Learning

Hereditary Transthyretin Amyloidosis (vATTR-V30M) is a rare and highly incapacitating sensorimotor neuropathy caused by an inherited mutation (Val30Met), which typically affects gait, among other symptoms. In this context, we investigated the possibility of using machine learning (ML) techniques to build a model(s) that can be used to support the detection of the Val30Met mutation (possibility of developing the disease), as well as symptom onset detection for the disease, given the gait characteristics of a person. These characteristics correspond to 24 gait parameters computed from 3-D body data, provided by a Kinect v2 camera, acquired from a person while walking towards the camera. To build the model(s), different ML algorithms were explored: k-nearest neighbors, decision tree, random forest, support vector machines (SVM), and multilayer perceptron. For a dataset corresponding to 66 subjects (25 healthy controls, 14 asymptomatic mutation carriers, and 27 patients) and several gait cycles per subject, we were able to obtain a model that distinguishes between controls and vATTR-V30M mutation carriers (with or without symptoms) with a mean accuracy of 92% (SVM). We also obtained a model that distinguishes between asymptomatic and symptomatic carriers with a mean accuracy of 98% (SVM). These results are very relevant, since this is the first study that proposes a ML approach to support vATTR-V30M patient assessment based on gait, being a promising foundation for the development of a computer-aided diagnosis tool to help clinicians in the identification and follow-up of this disease. Furthermore, the proposed method may also be used for other neuropathies.

Frailty Level Classification of the Community Elderly Using Microsoft Kinect-Based Skeleton Pose: A Machine Learning Approach

Frailty is one of the most important geriatric syndromes, which can be associated with increased risk for incident disability and hospitalization. Developing a real-time classification model of elderly frailty level could be beneficial for designing a clinical predictive assessment tool. Hence, the objective of this study was to predict the elderly frailty level utilizing the machine learning approach on skeleton data acquired from a Kinect sensor. Seven hundred and eighty-seven community elderly were recruited in this study. The Kinect data were acquired from the elderly performing different functional assessment exercises including: (1) 30-s arm curl; (2) 30-s chair sit-to-stand; (3) 2-min step; and (4) gait analysis tests. The proposed methodology was successfully validated by gender classification with accuracies up to 84 percent. Regarding frailty level evaluation and prediction, the results indicated that support vector classifier (SVC) and multi-layer perceptron (MLP) are the most successful estimators in prediction of the Fried’s frailty level with median accuracies up to 97.5 percent. The high level of accuracy achieved with the proposed methodology indicates that ML modeling can identify the risk of frailty in elderly individuals based on evaluating the real-time skeletal movements using the Kinect sensor.

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.

Simplified digital balance assessment in typically developing school children

BACKGROUND

Postural balance can be considered a conjoined parameter of gross motor performance. It is acquired in early childhood and honed until adolescence, but may also be influenced by various conditions. A simplified clinical assessment of balance and posture could be helpful in monitoring motor development or therapy particularly in pediatric patients. While analogue scales are considered unprecise and lab-based force-plate posturography lacks accessibility, we propose a novel kinematic balance assessment based on markerless 3D sensor technology.

RESEARCH QUESTION

Can balance and posture be assessed by tracking kinematic data using a single 3D motion tracking camera and are the results representative of normal motor development in a healthy pediatric cohort?

METHODS

A proprietary algorithm was developed and tested that uses skeletal data from the Microsoft Kinect™ V2 3D motion capture camera to calculate and track the center of mass in real time during a set of balance tasks. The algorithm tracks the distance of the COM traveled over time to calculate a balance score (COM speed). For this study, 432 school children aged 4-18 years performed 5 balance tasks and the resulting balance scores were analyzed and correlated with demographic data.

RESULTS

Preliminary experiments demonstrated that the system was able to reliably detect differences in COM speed during different balance tasks. The method showed moderate correlation with age and sex. Athletic activity positively correlated with balance skill in the age group < 8 years, but not in older children. Body mass appeared not to be correlated with balance ability.

SIGNIFICANCE

This study demonstrates that markerless 3D motion analysis can be used for the clinical assessment of coordination and balance and could potentially be used to monitor gross motor performance at the point-of-care.

The Validity and Reliability of the Microsoft Kinect for Measuring Trunk Compensation during Reaching

Compensatory movements at the trunk are commonly utilized during reaching by persons with motor impairments due to neurological injury such as stroke. Recent low-cost motion sensors may be able to measure trunk compensation, but their validity and reliability for this application are unknown. The purpose of this study was to compare the first (K1) and second (K2) generations of the Microsoft Kinect to a video motion capture system (VMC) for measuring trunk compensation during reaching. Healthy participants (n = 5) performed reaching movements designed to simulate trunk compensation in three different directions and on two different days while being measured by all three sensors simultaneously. Kinematic variables related to reaching range of motion (ROM), planar reach distance, trunk flexion and lateral flexion, shoulder flexion and lateral flexion, and elbow flexion were calculated. Validity and reliability were analyzed using repeated-measures ANOVA, paired t-tests, Pearson’s correlations, and Bland-Altman limits of agreement. Results show that the K2 was closer in magnitude to the VMC, more valid, and more reliable for measuring trunk flexion and lateral flexion during extended reaches than the K1. Both sensors were highly valid and reliable for reaching ROM, planar reach distance, and elbow flexion for all conditions. Results for shoulder flexion and abduction were mixed. The K2 was more valid and reliable for measuring trunk compensation during reaching and therefore might be prioritized for future development applications. Future analyses should include a more heterogeneous clinical population such as persons with chronic hemiparetic stroke.

Extraction and selection of gait recognition features using skeleton point detection and improved fuzzy decision

It is of great importance to effectively measure gait features and recognize the signature gait patterns for gait rehabilitation. In this work, we used a skeleton point detection to extract gait features and proposed an improved fuzzy decision to select the most significant features for classifying gait patterns. Thirteen gait recognition features were extracted from the obtained skeleton points data. Taking the extracted features as an input, our improved fuzzy similarity priority decision method has obtained important sequences of all features based on the relatively important scores. Then, the ranked features were delivered in different classifiers by a sequential forward selection strategy to select the optimal feature subset. There were significant differences between groups in each of the thirteen gait recognition features (p < 0.005), indicating that all extracted features are potential influence factors for classifying gait patterns. We also found that the highest classification accuracy of 100% for gait feature subsets included the stride frequency, maximum flexion angle of knee, and toe-out angle, on the all classifiers. The results suggest that the proposed approaches are very useful in searching for the optimal feature subset in present dataset.

Relative and Absolute Reliability of a Motor Assessment System Using KINECT® Camera

(1) Background: Virtual reality is currently useful in different clinical specialties as a diagnostic and therapeutic tool. In this study, we analyzed the relative and absolute reliability of the motor evaluation with the Kinect camera, a markerless motion system. (2) Methods: Observational study in healthy people, whose inclusion criteria were: healthy people, age 18 to 40 years old without pathologies or injuries in osteoarticular structures or ligamentous muscle and pharmacological treatment with influence on motor skills. Fifty-two subjects were evaluated. (3) Results: The results of the relative reliability were favorable in variables such as the amplitude of passage of the right leg (ICC (Intraclass Correlation Coefficient) = 0.95 ± 0.03), the step width of the left leg (ICC = 0.92 ± 0.04) or balance of the left leg (ICC = 0.90 ± 0.05). Moderate values were found for other variables. The absolute reliability, measured by the coefficient of variation, was favorable in most of the variables. (4) Conclusions: The results reflect a favorable intraclass correlation in the evaluation of the variation and asymmetry of movements of the upper limbs, the balance of both legs, the side step width and the evaluation of the sitting and standing positions. The reliability of the evaluation of the variation of movements and the asymmetry of the lower limbs must be further improved.

Reliability and validity of a novel Kinect-based software program for measuring a single leg squat

Background

The Single leg squat (SLS) is a movement screening test widely used in clinical settings. The SLS is highly subjective in its nature. Objective measures, such as 3D-motion analyses, are seldom used in daily clinical work. An interactive, Kinect-based 3D-movement analysis system, the Qinematic™, is proposed to be easily used in clinical settings to assess the SLS. The aim of this study was to establish the test-retest reliability and construct validity of Qinematic™ for assessing the SLS. A further aim was to identify angles of medial knee displacement, to summarise the discriminative ability of the SLS measured by Qinematic™.

Methods

We performed a test-retest reliability study (n = 37) of the SLS using Qinematic™ and a construct validity study, in which Qinematic™ data were compared with visual assessment of video-recorded SLS.

Results

Three variables (left knee down, right knee up and down) reached “substantial reliability” (ICC = 0.64–0.69). One variable, “left knee up”, showed a significant difference between the two test occasions (T1–6.34°, T2 0.66°, p = 0.013, ICC = 0.50), and “poor absolute reliability” was seen for all variables (SEM = 9.04–10.66, SDC = 25.06–29.55). A moderate agreement between the visual assessment and Qinematic™ data for various knee angles was shown (Kappa = 0.45–0.58). The best discriminative ability of the SLS was found at a knee angle of 6° (AUC = 0.82, sensitivity = 0.86, specificity = 0.78, PPV = 0.58, NPV = 0.94).

Conclusions

Qinematic™ shows a poor absolute reliability, and a substantial relative reliability, in measuring a SLS at the way down. This indicates that Qinematic™ should not be recommended for the use on an individual level, but it can possibly be used on a group level. The merged results of the construct validity study indicate that Qinematic™ at 6° of medial displacement can identify subjects with a knee over foot position. In summary, the use of the Qinematic™ net trajectory angle, which estimates the “line of best fit” cannot be recommended to assess a knee medial to foot position and should be reconsidered.

Study of Postural Stability Features by Using Kinect Depth Sensors to Assess Body Joint Coordination Patterns

A stable posture requires the coordination of multiple joints of the body. This coordination of the multiple joints of the human body to maintain a stable posture is a subject of research. The number of degrees of freedom (DOFs) of the human motor system is considerably larger than the DOFs required for posture balance. The manner of managing this redundancy by the central nervous system remains unclear. To understand this phenomenon, in this study, three local inter-joint coordination pattern (IJCP) features were introduced to characterize the strength, changing velocity, and complexity of the inter-joint couplings by computing the correlation coefficients between joint velocity signal pairs. In addition, for quantifying the complexity of IJCPs from a global perspective, another set of IJCP features was introduced by performing principal component analysis on all joint velocity signals. A Microsoft Kinect depth sensor was used to acquire the motion of 15 joints of the body. The efficacy of the proposed features was tested using the captured motions of two age groups (18-24 and 65-73 years) when standing still. With regard to the redundant DOFs of the joints of the body, the experimental results suggested that an inter-joint coordination strategy intermediate to that of the two extreme coordination modes of total joint dependence and independence is used by the body. In addition, comparative statistical results of the proposed features proved that aging increases the coupling strength, decreases the changing velocity, and reduces the complexity of the IJCPs. These results also suggested that with aging, the balance strategy tends to be more joint dependent. Because of the simplicity of the proposed features and the affordability of the easy-to-use Kinect depth sensor, such an assembly can be used to collect large amounts of data to explore the potential of the proposed features in assessing the performance of the human balance control system.

Monocular 3D Sway Tracking for Assessing Postural Instability in Cerebral Hypoperfusion During Quiet Standing

Postural instability is prevalent in aging and neurodegenerative disease, decreasing quality of life and independence. Quantitatively monitoring balance control is important for assessing treatment efficacy and rehabilitation progress. However, existing technologies for assessing postural sway are complex and expensive, limiting their widespread utility. Here, we propose a monocular imaging system capable of assessing sub-millimeter 3D sway dynamics during quiet standing. Two anatomical targets with known feature geometries were placed on the lumbar and shoulder. Upper and lower trunk 3D kinematic motion were automatically assessed from a set of 2D frames through geometric feature tracking and an inverse motion model. Sway was tracked in 3D and compared between control and hypoperfusion conditions in 14 healthy young adults. The proposed system demonstrated high agreement with a commercial motion capture system (error [Formula: see text], [-0.52, 0.52]). Between-condition differences in sway dynamics were observed in anterior-posterior sway during early and mid stance, and medial-lateral sway during mid stance commensurate with decreased cerebral perfusion, followed by recovered sway dynamics during late stance with cerebral perfusion recovery. This inexpensive single-camera system enables quantitative 3D sway monitoring for assessing neuromuscular balance control in weakly constrained environments.

A new process to measure postural sway using a Kinect depth camera during a Sensory Organisation Test

Posturography provides quantitative, objective measurements of human balance and postural control for research and clinical use. However, it usually requires access to specialist equipment to measure ground reaction forces, which are not widely available in practice, due to their size or cost. In this study, we propose an alternative approach to posturography. It uses the skeletal output of an inexpensive Kinect depth camera to localise the Centre of Mass (CoM) of an upright individual. We demonstrate a pipeline which is able to measure postural sway directly from CoM trajectories, obtained from tracking the relative position of three key joints. In addition, we present the results of a pilot study that compares this method of measuring postural sway to the output of a NeuroCom SMART Balance Master. 15 healthy individuals (age: 42.3 ± 20.4 yrs, height: 172 ± 11 cm, weight: 75.1 ± 14.2 kg, male = 11), completed 25 Sensory Organisation Test (SOT) on a NeuroCom SMART Balance Master. Simultaneously, the sessions were recorded using custom software developed for this study (CoM path recorder). Postural sway was calculated from the output of both methods and the level of agreement determined, using Bland-Altman plots. Good agreement was found for eyes open tasks with a firm support, the agreement decreased as the SOT tasks became more challenging. The reasons for this discrepancy may lie in the different approaches that each method takes to calculate CoM. This discrepancy warrants further study with a larger cohort, including fall-prone individuals, cross-referenced with a marker-based system. However, this pilot study lays the foundation for the development of a portable device, which could be used to assess postural control, more cost-effectively than existing equipment.

Random forest-based classsification and analysis of hemiplegia gait using low-cost depth cameras

Hemiplegia is a form of paralysis that typically has the symptom of dysbasia. In current clinical rehabilitations, to measure the level of hemiplegia gaits, clinicians often conduct subject evaluations through observations, which is unreliable and inaccurate. The Microsoft Kinect sensor (MS Kinect) is a widely used, low-cost depth sensor that can be used to detect human behaviors in real time. The purpose of this study is to investigate the usage of the Kinect data for the classification and analysis of hemiplegia gait. We first acquire the gait data by using a MS Kinect and extract a set of gait features including the stride length, gait speed, left/right moving distances, and up/down moving distances. With the gait data of 60 subjects including 20 hemiplegia patients and 40 healthy subjects, we employ a random forest-based classification approach to analyze the importances of different gait features for hemiplegia classification. Thanks to the over-fitting avoidance nature of the random forest approach, we do not need to have a careful control over the percentage of patients in the training data. In our experiments, our approach obtained the averaged classification accuracy of 90.65% among all the combinations of the gait features, which substantially outperformed state-of-the-art methods. The best classification accuracy of our approach is 95.45%, which is superior than all existing methods. Additionally, our approach also correctly reveals the importance of different gait features for hemiplegia classification. Our random forest-based approach outperforms support vector machine-based method and the Bayesian-based method, and can effectively extract gait features of subjects with hemiplegia for the classification and analysis of hemiplegia. Graphical Abstract Random Forest based Classsification and Analysis of Hemiplegia Gait using Low-cost Depth Cameras. Left: Motion capture with MS Kinect; Top-right: Random Forest Classsification based on the extracted gait features; Bottom-right: Sensitivity and specificity evaluation of the proposed classification approach.

Reliability of measurement of active trunk movement in wheelchair basketball players

The study aim was to assess the reliability to active trunk movements measurement in four sitting positions in wheelchair basketball players and to check their trunk movements in these positions. Eighteen volunteer wheelchair basketball athletes, with a minimum of five years’ training experience, were asked to perform the maximum range of active trunk movement in three planes in four sitting positions (in a sports wheelchair with straps, without straps, on a table with feet on the floor, on a table without foot support). The range of movement was measured by the Kinect for Windows V2 sensor twice (with one-week interval). To assess the reliability, different statistical methods were used for each movement: significance of differences between the results (p-value), interclass correlation coefficient (ICC) and minimal detectable change (MDC). The limits of agreement analysis (LOA) were calculated. Differences between trunk movements in four positions were checked by the MANOVA (Wilk’s Lambda and ETA² were calculated if data were normally distributed). The significance level was set at α < .05. Friedman ANOVA and non-parametric Wilcoxon test with the Bonferroni correction were applied when data were not normally distributed. The significance level after Bonferroni correction was set at α < .013 (α = p/k, where p = .05, k–number of positions = 4). The measurement of active trunk movement in each plane was reliable (p > .05, no differences between the results, “very good”ICC, between .96-.99). In the position with straps, the trunk movement was significantly bigger than in other positions (p < .05), except for the position without straps (p > .05). The Kinect for Windows V2 sensor measured active trunk movement in a reliable manner and it can be recommended as a reliable tool for measuring trunk function. Utilizing straps by wheelchair basketball players increases their trunk movement.

Contactless recording of sleep apnea and periodic leg movements by nocturnal 3-D-video and subsequent visual perceptive computing

Contactless measurements during the night by a 3-D-camera are less time-consuming in comparison to polysomnography because they do not require sophisticated wiring. However, it is not clear what might be the diagnostic benefit and accuracy of this technology. We investigated 59 persons simultaneously by polysomnography and 3-D-camera and visual perceptive computing (19 patients with restless legs syndrome (RLS), 21 patients with obstructive sleep apnea (OSA), and 19 healthy volunteers). There was a significant correlation between the apnea hypopnea index (AHI) measured by polysomnography and respiratory events measured with the 3-D-camera in OSA patients (r = 0.823; p < 0.001). The receiver operating characteristic curve yielded a sensitivity of 90% for OSA with a specificity of 71.4%. In RLS patients 72.8% of leg movements confirmed by polysomnography could be detected by 3-D-video and a significant moderate correlation was found between PLM measured by polysomnography and by the 3-D-camera (RLS: r = 0.654; p = 0.004). In total, 95.4% of the sleep epochs were correctly classified by the machine learning approach, but only 32.5% of awake epochs. Further studies should investigate, if this technique might be an alternative to home sleep testing in persons with an increased pre-test probability for OSA.

Validation of a Single RGB-D Camera for Gait Assessment of Polyneuropathy Patients

Motion analysis systems based on a single markerless RGB-D camera are more suitable for clinical practice than multi-camera marker-based reference systems. Nevertheless, the validity of RGB-D cameras for motor function assessment in some diseases affecting gait, such as Transthyretin Familial Amyloid Polyneuropathy (TTR-FAP), is yet to be investigated. In this study, the agreement between the Kinect v2 and a reference system for obtaining spatiotemporal and kinematic gait parameters was evaluated in the context of TTR-FAP. 3-D body joint data provided by both systems were acquired from ten TTR-FAP symptomatic patients, while performing ten gait trials. For each gait cycle, we computed several spatiotemporal and kinematic gait parameters. We then determined, for each parameter, the Bland Altman’s bias and 95% limits of agreement, as well as the Pearson’s and concordance correlation coefficients, between systems. The obtained results show that an affordable, portable and non-invasive system based on an RGB-D camera can accurately obtain most of the studied gait parameters (excellent or good agreement for eleven spatiotemporal and one kinematic). This system can bring more objectivity to motor function assessment of polyneuropathy patients, potentially contributing to an improvement of TTR-FAP treatment and understanding, with great benefits to the patients’ quality of life.

Current Low-Cost Video-Based Motion Analysis Options for Clinical Rehabilitation: A Systematic Review

BACKGROUND

Physical therapists, as clinical human movement experts, must qualitatively evaluate patients' functional and biomechanical impairments. There are now low-cost 2- and 3-dimensional video measurement systems that can be used to increase the precision and reliability of these qualitative clinical assessments.

PURPOSE

The purpose of this study was to systematically review current low-cost video-based methods for motion analysis compared with gold-standard 3-dimensional biomechanical methods.

DATA SOURCES

Electronic searches were conducted until January 2018 within the following databases: MEDLINE via PubMed, Cumulative Index to Nursing and Allied Health Literature (CINAHL), Cochrane, Scopus, and the Institute of Electrical and Electronics Engineers.

STUDY SELECTION

Studies designed to evaluate criterion-referenced validity and/or reliability of video-based motion analysis technologies within the last 20 years were included. English-language articles dealing with human rehabilitation were considered.

DATA EXTRACTION

Data extraction was independently completed by 3 reviewers, and methodological quality was assessed using the 2018 Consensus-Based Standards for the Selection of Health Measurement Instruments checklist. Articles were organized for analysis on the basis of type of motion analyzed and category of each low-cost technology used.

DATA SYNTHESIS

With 20 articles meeting selection criteria, 10 low-cost motion analysis platforms were presented, each examining different functional movement-dependent variables. Overall article quality was "low" or "very low" on the basis of Consensus-Based Standards for the Selection of Health Measurement Instruments scoring. Correlations between low-cost and 3-dimensional gold standard systems ranged widely from "poor" agreement (r = 0.025) to "strong" agreement (r = 0.992). Spatiotemporal gait parameters consistently outperformed planar joint angle data. Reliability was better measured than concurrent validity. A summary table was developed to assist clinicians in choosing which motions could potentially be measured accurately by each low-cost platform on the basis of current findings.

LIMITATIONS

Databases available to researchers were more clinical/medical in nature, and this review was written from that clinically based perspective. Lack of standardized protocols and methodology within included studies was common, making generalizability difficult.

CONCLUSIONS

Research attempting to validate newer low-cost movement analysis systems is limited in quality. Measurement of only certain variables should be considered when these tools are used. Further research is warranted, because these devices still have potential clinical utility for supplementing qualitative movement assessment with objective outcome measures.

A Framework for Interpretable Full-Body Kinematic Description Using Geometric and Functional Analysis

Rapid advances in cost-effective and non-invasive depth sensors, and the development of reliable and real-time 3D skeletal data estimation algorithms, have opened up a new application area in computer vision - statistical analysis of human kinematic data for fast, automated assessment of body movements. These assessments can play important roles in sports, medical diagnosis, physical therapy, elderly monitoring and related applications. This paper develops a comprehensive geometric framework for quantification and statistical evaluation of kinematic features. The key idea is to avoid analysis of individual joints, as is the current paradigm, and represent movements as temporal evolutions, or trajectories, on shape space of full body skeletons. This allows metrics with appropriate invariance properties to be imposed on these trajectories and leads to definitions of higher-level features, such as spatial symmetry (sS), temporal symmetry (tS), action's velocity (Vl) and body's balance (Bl), during performance of an action. These features exploit skeletal symmetries in space and time, and capture motion cadence to naturally quantify motions of individual subjects. The study of these features as functional data allows us to formulate certain hypothesis tests in feature space. This, in turn, leads to validation of existing assumptions and discoveries of new relationships between kinematics and demographic factors, such as age, gender, and athletic training. We use the clinically validated K3Da kinect dataset to illustrate these ideas, and hope these tools will lead to discovery of new relationships between full-body kinematic features and demographic, health, and wellness factors that are clinically relevant.

Accuracy of classifying the movement strategy in the functional reach test using a markerless motion capture system

The purpose of this study was to examine the accuracy of classifying the movement strategy in the functional reach test (FRT) using a markerless motion capture system (MLS) on the basis of values acquired with a marker-based motion capture system (MBS). Sixty young, injury-free individuals participated in this study. The task action involved reaching forward in the standing position. Using the Microsoft Kinect v2 as an MLS and Vicon as a MBS, the coordinates of the hip joints, knee joints and ankle joints were measured. The hip and ankle joint angles during the task were calculated from the coordinate data. These angles between MLS and MBS were compared using a paired t-test. The accuracy of movement strategy defined using MLS was examined based on the MBS. A t-test showed a significant difference in both the hip and ankle joint angles between systems (p < .01). However, in case of using data of left ankle joint, indices of the classification accuracy of MLS were 0.825 for sensitivity, 1.000 for specificity, infinity for positive likelihood ratio and 0.175 for negative likelihood ratio. The results for the right joint angle were similar to those of the left joint angle. Although the absolute measures in the hip and joint angles obtained using MLS differ from MBS, the MLS may be useful for accurately classifying the movement strategy adopted in the FRT.

Validity and Reliability of the Kinect for Assessment of Standardized Transitional Movements and Balance: Systematic Review and Translation into Practice

The validity and reliability of using the Kinect camera to measure standardized assessment of transitional movement, stepping, and balance was systematically reviewed and critically appraised for quality of the methods and results. The study made recommendations of specific tests for practice based on inclusion of both validity and reliability testing as well as quality of results. Authors' willingness to share their software was reported. Translation into practice is limited by lack of redundancy among studies and access to the software to implement the tests.

Assessment of frailty: a survey of quantitative and clinical methods

BACKGROUND

Frailty assessment is a critical approach in assessing the health status of older people. The clinical tools deployed by geriatricians to assess frailty can be grouped into two categories; using a questionnaire-based method or analyzing the physical performance of the subject. In performance analysis, the time taken by a subject to complete a physical task such as walking over a specific distance, typically three meters, is measured. The questionnaire-based method is subjective, and the time-based performance analysis does not necessarily identify the kinematic characteristics of motion and their root causes. However, kinematic characteristics are crucial in measuring the degree of frailty.

RESULTS

The studies reviewed in this paper indicate that the quantitative analysis of activity of daily living, balance and gait are significant methods for assessing frailty in older people. Kinematic parameters (such as gait speed) and sensor-derived parameters are also strong markers of frailty. Seventeen gait parameters are found to be sensitive for discriminating various frailty levels. Gait velocity is the most significant parameter. Short term monitoring of daily activities is a more significant method for frailty assessment than is long term monitoring and can be implemented easily using clinical tests such as sit to stand or stand to sit. The risk of fall can be considered an outcome of frailty.

CONCLUSION

Frailty is a multi-dimensional phenomenon that is defined by various domains; physical, social, psychological and environmental. The physical domain has proven to be essential in the objective determination of the degree of frailty in older people. The deployment of inertial sensor in clinical tests is an effective method for the objective assessment of frailty.

Feasibility of Home-Based Automated Assessment of Postural Instability and Lower Limb Impairments in Parkinson's Disease

A self-managed, home-based system for the automated assessment of a selected set of Parkinson’s disease motor symptoms is presented. The system makes use of an optical RGB-Depth device both to implement its gesture-based human computer interface and for the characterization and the evaluation of posture and motor tasks, which are specified according to the Unified Parkinson’s Disease Rating Scale (UPDRS). Posture, lower limb movements and postural instability are characterized by kinematic parameters of the patient movement. During an experimental campaign, the performances of patients affected by Parkinson’s disease were simultaneously scored by neurologists and analyzed by the system. The sets of parameters which best correlated with the UPDRS scores of subjects’ performances were then used to train supervised classifiers for the automated assessment of new instances of the tasks. Results on the system usability and the assessment accuracy, as compared to clinical evaluations, indicate that the system is feasible for an objective and automated assessment of Parkinson’s disease at home, and it could be the basis for the development of neuromonitoring and neurorehabilitation applications in a telemedicine framework.

Measurement of the total body center of gravity during sit-to-stand motion using a markerless motion capture system

PURPOSE

In order to evaluate the movement strategy in sit-to-stand (STS) motion, it is necessary to measure the center of gravity (COG). However, there is no established method for enabling this using convenience device in the clinical setting. The purpose of this study was to validate the measurement of the COG during the STS motion using an inexpensive, portable and markerless motion capture system (MLS).

METHOD

Eighteen healthy adults participated in our study. The coordinates of the joint centers during the STS motion were collected using the Microsoft Kinect system (as a MLS) and the Vicon system (as a marker-based motion capture system [MBS]). The center of mass of each segment-which was calculated based on the segmental mass and length-were synthesized to calculate the COG. The displacement, velocity, acceleration of the COG during the STS motion were calculated from the data obtained using each system and compared between systems.

RESULTS

The two systems showed significant difference in their measurements of displacement in both the vertical and anteroposterior directions. However, in the anteroposterior direction, there was no significant difference in the measurements of either velocity or acceleration.

CONCLUSION

Our results suggested the validity of the COG in the anteroposterior direction during the STS motion measured using the MLS. The method developed in the present study enables the evaluation of a patient's movement strategy.

Preliminary evaluation of a self-guided fall risk assessment tool for older adults

Falls are a major health problem for older adults with significant physical and psychological consequences. The first step of successful fall prevention is to identify those at risk of falling. Recent technology advancement offers the possibility of objective, lowcost and self-guided fall risk assessment. The present work evaluated the preliminary validity and usability of a Kinect camera-based selfinitiated fall risk assessment system in a hospital setting. A convenience sample of 29 female participants (77.5 ± 7.9 years old) enrolled in this study. This low-cost self-guided system included a Kinect depth-sensing camera, a PC-based computer, and custom-built software. An onscreen Fall Risk Assessment Avatar (FRAAn) utilizing visual and verbal instructions led participants through a fall risk assessment consisting of self-report measures and clinically validated balance and mobility tests. Participants also completed clinical fall risk evaluation (Timed-Up and Go, and Berg Balance Scale) led by a researcher. User experience was evaluated by the System Usability Scale (SUS). Results indicate that FRAAn-based outcome measures (postural sway metrics, and sit-to-stand speed) were highly correlated with clinical fall risk measures, and were able to differentiate individuals with increased fall risk. Additionally, 83% participants reported high usability (SUS > 80), indicating the system is well received among older users. Overall, our results indicate that the FRAAn system has promise for providing a self-guided fall risk assessment, and is well received by older users. This affordable, portable and self-guided system has potential to facilitate objective fall risk assessment in older adults in various settings.

Clinical assessment of depth sensor based pose estimation algorithms for technology supervised rehabilitation applications

Encouraging rehabilitation by the use of technology in the home can be a cost-effective strategy, particularly if consumer-level equipment can be used. We present a clinical qualitative and quantitative analysis of the pose estimation algorithms of a typical consumer unit (Xbox One Kinect), to assess its suitability for technology supervised rehabilitation and guide development of future pose estimation algorithms for rehabilitation applications. We focused the analysis on upper-body stroke rehabilitation as a challenging use case. We found that the algorithms require improved joint tracking, especially for the shoulder, elbow and wrist joints, and exploiting temporal information for tracking when there is full or partial occlusion in the depth data.

Eigen Posture Based Fall Risk Assessment System Using Kinect

Postural Instability (PI) is a major reason for fall in geriatric population as well as for people with diseases or disorders like Parkinson's, stroke etc. Conventional stability indicators like Berg Balance Scale (BBS) require clinical settings with skilled personnel's interventions to detect PI and finally classify the person into low, mid or high fall risk categories. Moreover these tests demand a number of functional tasks to be performed by the patient for proper assessment. In this paper a machine learning based approach is developed to determine fall risk with minimal human intervention using only Single Limb Stance exercise. The analysis is done based on the spatiotemporal dynamics of skeleton joint positions obtained from Kinect sensor. A novel posture modeling method has been applied for feature extraction along with some traditional time domain and metadata features to successfully predict the fall risk category. The proposed unobstrusive, affordable system is tested over 224 subjects and is able to achieve 75% mean accuracy on the geriatric and patient population.

Analysis of muscle activation in lower extremity for static balance

Balance plays an important role for human bipedal locomotion. Degeneration of balance control is prominent in stroke patients, elderly adults and even for majority of obese people. Design of personalized balance training program, in order to strengthen muscles, requires the analysis of muscle activation during an activity. In this paper we have proposed an affordable and portable approach to analyze the relationship between the static balance strategy and activation of various lower extremity muscles. To do that we have considered Microsoft Kinect XBox 360 as a motion sensing device and Wii balance board for measuring external force information. For analyzing the muscle activation pattern related to static balance, participants are asked to do the single limb stance (SLS) exercise on the balance board and in front of the Kinect. Static optimization to minimize the overall muscle activation pattern is carried out using OpenSim, which is an open-source musculoskeletal simulation software. The study is done on ten normal and ten obese people, grouped according to body mass index (BMI). Results suggest that the lower extremity muscles like biceps femoris, psoas major, sartorius, iliacus play the major role for both maintaining the balance using one limb as well as maintaining the flexion of the other limb during SLS. Further investigations reveal that the higher muscle activations of the flexed leg for normal group demonstrate higher strength. Moreover, the lower muscle activation of the standing leg for normal group demonstrate more headroom for the biceps femoris-short-head and psoas major to withstand the load and hence have better static balance control.

Validity of the total body centre of gravity during gait using a markerless motion capture system

PURPOSE

The purpose of this study was to examine the validity of total body centre of gravity (COG) measurement during gait with markerless motion capture system (MLS) on the basis of values acquired with a marker-based motion capture system (MBS).

MATERIALS AND METHODS

Thirty young healthy subjects walked on a flat surface as coordinate data from their bodies were acquired using the Kinect v2 (as a MLS) and Vicon systems (as a MBS). COG was calculated using coordinate data of the total body. Comparisons of COG ensemble curves in the mediolateral and vertical directions were performed between MLS and MBS throughout the gait cycle. The relative consistency between these systems was assessed using Pearson correlation coefficients.

RESULTS

The COG trajectory made by using MLS data followed the trend of the COG trajectory with MBS in the mediolateral direction. In the vertical direction, however, the COG trajectories did not match between two systems. High correlation coefficients (r  > 0.79) were observed from 30% to 80% of the gait cycle. The greatest difference of COG between MLS and MBS in the mediolateral direction was 1.1 mm. Differences in the vertical direction appeared to be proportional to the distance between the participant and the Kinect v2 sensor.

CONCLUSION

In the mediolateral direction, COG calculated with MLS data during gait was validated with COG calculated on the basis of a MBS. Further correction of systematic error is necessary to improve the validity of COG calculations in the vertical direction.

Reliability and validity of the Microsoft Kinect for assessment of manual wheelchair propulsion

Concurrent validity and test-retest reliability of the Microsoft Kinect in quantification of manual wheelchair propulsion were examined. Data were collected from five manual wheelchair users on a roller system. Three Kinect sensors were used to assess test-retest reliability with a still pose. Three systems were used to assess concurrent validity of the Kinect to measure propulsion kinematics (joint angles, push loop characteristics): Kinect, Motion Analysis, and Dartfish ProSuite (Dartfish joint angles were limited to shoulder and elbow flexion). Intraclass correlation coefficients revealed good reliability (0.87-0.99) between five of the six joint angles (neck flexion, shoulder flexion, shoulder abduction, elbow flexion, wrist flexion). ICCs suggested good concurrent validity for elbow flexion between the Kinect and Dartfish and between the Kinect and Motion Analysis. Good concurrent validity was revealed for maximum height, hand-axle relationship, and maximum area (0.92-0.95) between the Kinect and Dartfish and maximum height and hand-axle relationship (0.89-0.96) between the Kinect and Motion Analysis. Analysis of variance revealed significant differences (p < 0.05) in maximum length between Dartfish (mean 58.76 cm) and the Kinect (40.16 cm). Results pose promising research and clinical implications for propulsion assessment and overuse injury prevention with the application of current findings to future technology.

Using visual stimuli to enhance gait control

Gait control challenges commonly coincide with vestibular dysfunction and there is a long history in using balance and gait activities to enhance functional mobility in this population. While much has been learned using traditional rehabilitation exercises, there is a new line of research emerging that is using visual stimuli in a very specific way to enhance gait control. For example, avatars can be created in an individualized manner to incorporate specific gait characteristics. The avatar could then be used as a visual stimulus to which the patient can synchronize their own gait cycle. This line of research builds upon the rich history of sensorimotor control research in which augmented sensory information (visual, haptic, or auditory) is used to probe, and even enhance, human motor control. This review paper focuses on gait control challenges in patients with vestibular dysfunction, provides a brief historical perspective on how various visual displays have been used to probe sensorimotor and gait control, and offers some recommendations for future research.

Reliability and validity of a novel Kinect-based software program for measuring posture, balance and side-bending

Background

Clinical examinations are subjective and often show a low validity and reliability. Objective and highly reliable quantitative assessments are available in laboratory settings using 3D motion analysis, but these systems are too expensive to use for simple clinical examinations. Qinematic™ is an interactive movement analyses system based on the Kinect camera and is an easy-to-use clinical measurement system for assessing posture, balance and side-bending. The aim of the study was to test the test-retest the reliability and construct validity of Qinematic™ in a healthy population, and to calculate the minimal clinical differences for the variables of interest. A further aim was to identify the discriminative validity of Qinematic™ in people with low-back pain (LBP).

Methods

We performed a test-retest reliability study (n = 37) with around 1 week between the occasions, a construct validity study (n = 30) in which Qinematic™ was tested against a 3D motion capture system, and a discriminative validity study, in which a group of people with LBP (n = 20) was compared to healthy controls (n = 17). We tested a large range of psychometric properties of 18 variables in three sections: posture (head and pelvic position, weight distribution), balance (sway area and velocity in single- and double-leg stance), and side-bending.

Results

The majority of the variables in the posture and balance sections, showed poor/fair reliability (ICC < 0.4) and poor/fair validity (Spearman <0.4), with significant differences between occasions, between Qinematic™ and the 3D–motion capture system. In the clinical study, Qinematic™ did not differ between people with LPB and healthy for these variables. For one variable, side-bending to the left, there was excellent reliability (ICC =0.898), excellent validity (r = 0.943), and Qinematic™ could differentiate between LPB and healthy individuals (p = 0.012).

Conclusion

This paper shows that a novel software program (Qinematic™) based on the Kinect camera for measuring balance, posture and side-bending has poor psychometric properties, indicating that the variables on balance and posture should not be used for monitoring individual changes over time or in research. Future research on the dynamic tasks of Qinematic™ is warranted.

Posturography stability score generation for stroke patient using Kinect: Fuzzy based approach

Aim of this paper is to formulate a posturography stability score for stroke patients using fuzzy logic. Postural instability is one of the prominent symptoms of stroke, dementia, parkinsons disease, myopathy, etc. and is the major precursor of fall. Conventional scoring techniques used to assess postural stability require manual intervention and are dependent on live interaction with physiotherapist. We propose a novel scoring technique to calculate static stability of a person using posturography features acquired by Kinect sensor, which do not require any manual intervention or expert guidance, is cost effective and hence are ideal for tele rehabilitation purpose. Stability analysis is done during Single Limb Stance (SLS) exercise. Kinect sensor is used to calculate three features, naming SLS duration, vibration index, calculated from mean vibration of twenty joints and sway area of Centre of Mass (CoM). Based on the variation of these features, a fuzzy rule base is generated which calculates a static stability score. One way analysis of variance (Anova) between a group of stroke population and healthy individuals under study validates the reliability of the proposed scorer. Generated fuzzy score are comparable with standard stability scorer like Berg Balance scale and fall risk assessment tool like Johns Hopkins scale. Stability score, besides providing an index of overall stability can also be used as a fall predictability index.

The Automated Assessment of Postural Stability: Balance Detection Algorithm

Impaired balance is a common indicator of mild traumatic brain injury, concussion and musculoskeletal injury. Given the clinical relevance of such injuries, especially in military settings, it is paramount to develop more accurate and reliable on-field evaluation tools. This work presents the design and implementation of the automated assessment of postural stability (AAPS) system, for on-field evaluations following concussion. The AAPS is a computer system, based on inexpensive off-the-shelf components and custom software, that aims to automatically and reliably evaluate balance deficits, by replicating a known on-field clinical test, namely, the Balance Error Scoring System (BESS). The AAPS main innovation is its balance error detection algorithm that has been designed to acquire data from a Microsoft Kinect^® sensor and convert them into clinically-relevant BESS scores, using the same detection criteria defined by the original BESS test. In order to assess the AAPS balance evaluation capability, a total of 15 healthy subjects (7 male, 8 female) were required to perform the BESS test, while simultaneously being tracked by a Kinect 2.0 sensor and a professional-grade motion capture system (Qualisys AB, Gothenburg, Sweden). High definition videos with BESS trials were scored off-line by three experienced observers for reference scores. AAPS performance was assessed by comparing the AAPS automated scores to those derived by three experienced observers. Our results show that the AAPS error detection algorithm presented here can accurately and precisely detect balance deficits with performance levels that are comparable to those of experienced medical personnel. Specifically, agreement levels between the AAPS algorithm and the human average BESS scores ranging between 87.9% (single-leg on foam) and 99.8% (double-leg on firm ground) were detected. Moreover, statistically significant differences in balance scores were not detected by an ANOVA test with alpha equal to 0.05. Despite some level of disagreement between human and AAPS-generated scores, the use of an automated system yields important advantages over currently available human-based alternatives. These results underscore the value of using the AAPS, that can be quickly deployed in the field and/or in outdoor settings with minimal set-up time. Finally, the AAPS can record multiple error types and their time course with extremely high temporal resolution. These features are not achievable by humans, who cannot keep track of multiple balance errors with such a high resolution. Together, these results suggest that computerized BESS calculation may provide more accurate and consistent measures of balance than those derived from human experts.

Using data from the Microsoft Kinect 2 to determine postural stability in healthy subjects: A feasibility trial

The objective of this study was to determine whether kinematic data collected by the Microsoft Kinect 2 (MK2) could be used to quantify postural stability in healthy subjects. Twelve subjects were recruited for the project, and were instructed to perform a sequence of simple postural stability tasks. The movement sequence was performed as subjects were seated on top of a force platform, and the MK2 was positioned in front of them. This sequence of tasks was performed by each subject under three different postural conditions: "both feet on the ground" (1), "One foot off the ground" (2), and "both feet off the ground" (3). We compared force platform and MK2 data to quantify the degree to which the MK2 was returning reliable data across subjects. We then applied a novel machine-learning paradigm to the MK2 data in order to determine the extent to which data from the MK2 could be used to reliably classify different postural conditions. Our initial comparison of force plate and MK2 data showed a strong agreement between the two devices, with strong Pearson correlations between the trunk centroids "Spine_Mid" (0.85 ± 0.06), "Neck" (0.86 ± 0.07) and "Head" (0.87 ± 0.07), and the center of pressure centroid inferred by the force platform. Mean accuracy for the machine learning classifier from MK2 was 97.0%, with a specific classification accuracy breakdown of 90.9%, 100%, and 100% for conditions 1 through 3, respectively. Mean accuracy for the machine learning classifier derived from the force platform data was lower at 84.4%. We conclude that data from the MK2 has sufficient information content to allow us to classify sequences of tasks being performed under different levels of postural stability. Future studies will focus on validating this protocol on large populations of individuals with actual balance impairments in order to create a toolkit that is clinically validated and available to the medical community.

Evaluation of Kinect 3D Sensor for Healthcare Imaging

Microsoft Kinect is a three-dimensional (3D) sensor originally designed for gaming that has received growing interest as a cost-effective and safe device for healthcare imaging. Recent applications of Kinect in health monitoring, screening, rehabilitation, assistance systems, and intervention support are reviewed here. The suitability of available technologies for healthcare imaging applications is assessed. The performance of Kinect I, based on structured light technology, is compared with that of the more recent Kinect II, which uses time-of-flight measurement, under conditions relevant to healthcare applications. The accuracy, precision, and resolution of 3D images generated with Kinect I and Kinect II are evaluated using flat cardboard models representing different skin colors (pale, medium, and dark) at distances ranging from 0.5 to 1.2 m and measurement angles of up to 75°. Both sensors demonstrated high accuracy (majority of measurements <2 mm) and precision (mean point to plane error <2 mm) at an average resolution of at least 390 points per cm². Kinect I is capable of imaging at shorter measurement distances, but Kinect II enables structures angled at over 60° to be evaluated. Kinect II showed significantly higher precision and Kinect I showed significantly higher resolution (both p < 0.001). The choice of object color can influence measurement range and precision. Although Kinect is not a medical imaging device, both sensor generations show performance adequate for a range of healthcare imaging applications. Kinect I is more appropriate for short-range imaging and Kinect II is more appropriate for imaging highly curved surfaces such as the face or breast.

Accuracy and Reliability of the Kinect Version 2 for Clinical Measurement of Motor Function

Background

The introduction of low cost optical 3D motion tracking sensors provides new options for effective quantification of motor dysfunction.

Objective

The present study aimed to evaluate the Kinect V2 sensor against a gold standard motion capture system with respect to accuracy of tracked landmark movements and accuracy and repeatability of derived clinical parameters.

Methods

Nineteen healthy subjects were concurrently recorded with a Kinect V2 sensor and an optical motion tracking system (Vicon). Six different movement tasks were recorded with 3D full-body kinematics from both systems. Tasks included walking in different conditions, balance and adaptive postural control. After temporal and spatial alignment, agreement of movements signals was described by Pearson’s correlation coefficient and signal to noise ratios per dimension. From these movement signals, 45 clinical parameters were calculated, including ranges of motions, torso sway, movement velocities and cadence. Accuracy of parameters was described as absolute agreement, consistency agreement and limits of agreement. Intra-session reliability of 3 to 5 measurement repetitions was described as repeatability coefficient and standard error of measurement for each system.

Results

Accuracy of Kinect V2 landmark movements was moderate to excellent and depended on movement dimension, landmark location and performed task. Signal to noise ratio provided information about Kinect V2 landmark stability and indicated larger noise behaviour in feet and ankles. Most of the derived clinical parameters showed good to excellent absolute agreement (30 parameters showed ICC(3,1) > 0.7) and consistency (38 parameters showed r > 0.7) between both systems.

Conclusion

Given that this system is low-cost, portable and does not require any sensors to be attached to the body, it could provide numerous advantages when compared to established marker- or wearable sensor based system. The Kinect V2 has the potential to be used as a reliable and valid clinical measurement tool.