Validity of the Microsoft Kinect for providing lateral trunk lean feedback during gait retraining

An Expert-Knowledge-Based Graph Convolutional Network for Skeleton- Based Physical Rehabilitation Exercises Assessment

Physical therapists play a crucial role in guiding patients through effective and safe rehabilitation processes according to medical guidelines. However, due to the therapist-patient imbalance, it is neither economical nor feasible for therapists to provide guidance to every patient during recovery sessions. Automated assessment of physical rehabilitation can help with this problem, but accurately quantifying patients' training movements and providing meaningful feedback poses a challenge. In this paper, an Expert-knowledge-based Graph Convolutional approach is proposed to automate the assessment of the quality of physical rehabilitation exercises. This approach utilizes experts' knowledge to improve the spatial feature extraction ability of the Graph Convolutional module and a Gated pooling module for feature aggregation. Additionally, a Transformer module is employed to capture long-range temporal dependencies in the movements. The attention scores and weight matrix obtained through this approach can serve as interpretability tools to help therapists understand the assessment model and assist patients in improving their exercises. The effectiveness of the proposed method is verified on the KIMORE dataset, achieving state-of-the-art performance compared to existing models. Experimental results also illustrate the interpretability of the method in both spatial and temporal dimensions.

Advances in active knee brace technology: A review of gait analysis, actuation, and control applications

This article discusses the significance of knee joint mechanics and the consequences of knee dysfunctions on an individual's quality of life. The utilization of active knee braces, which incorporate concepts of mechatronics systems, is investigated here as a potential treatment option. The complexity of the construction of the knee joint, which has six degrees of motion and is more prone to injury since it bears weight, is emphasized in this article. By wearing braces and using other support devices, one's knee can increase stability and mobility. In addition, the paper discusses various technologies that can be used to measure the knee adduction moment and supply spatial information on gait. Actuators for active knee braces must be compact, lightweight, and capable of producing a significant amount of torque; as a result, electric, hydraulic, and pneumatic actuators are the most common types. Creating control mechanisms, such as position control techniques and force/torque control approaches, is essential to knee exoskeleton research and development. These methods might make knee joint rehabilitation and assistive technology safer and more effective.

Fluctuations in Upper and Lower Body Movement during Walking in Normal Pressure Hydrocephalus and Parkinson's Disease Assessed by Motion Capture with a Smartphone Application, TDPT-GT

We aimed to capture the fluctuations in the dynamics of body positions and find the characteristics of them in patients with idiopathic normal pressure hydrocephalus (iNPH) and Parkinson's disease (PD). With the motion-capture application (TDPT-GT) generating 30 Hz coordinates at 27 points on the body, walking in a circle 1 m in diameter was recorded for 23 of iNPH, 23 of PD, and 92 controls. For 128 frames of calculated distances from the navel to the other points, after the Fourier transforms, the slopes (the representatives of fractality) were obtained from the graph plotting the power spectral density against the frequency in log-log coordinates. Differences in the average slopes were tested by one-way ANOVA and multiple comparisons between every two groups. A decrease in the absolute slope value indicates a departure from the 1/f noise characteristic observed in healthy variations. Significant differences in the patient groups and controls were found in all body positions, where patients always showed smaller absolute values. Our system could measure the whole body's movement and temporal variations during walking. The impaired fluctuations of body movement in the upper and lower body may contribute to gait and balance disorders in patients.

Artificial Intelligence Distinguishes Pathological Gait: The Analysis of Markerless Motion Capture Gait Data Acquired by an iOS Application (TDPT-GT)

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

Assessing Walking Stability Based on Whole-Body Movement Derived from a Depth-Sensing Camera

Stability during walking is considered a crucial aspect of assessing gait ability. The current study aimed to assess walking stability by applying principal component analysis (PCA) to decompose three-dimensional (3D) whole-body kinematic data of 104 healthy young adults (21.9 ± 3.5 years, 54 females) derived from a depth-sensing camera into a set of movement components/synergies called "principal movements" (PMs), forming together to achieve the task goal. The effect of sex as the focus area was tested on three PCA-based variables computed for each PM: the relative explained variance (rVAR) as a measure of the composition of movement structures; the largest Lyapunov exponent (LyE) as a measure of variability; and the number of zero-crossings (N) as a measure of the tightness of neuromuscular control. The results show that the sex effects appear in the specific PMs. Specifically, in PM₁, resembling the swing-phase movement, females have greater LyE (p = 0.013) and N (p = 0.017) values than males. Moreover, in PM₃, representing the mid-stance-phase movement, females have smaller rVAR (p = 0.020) but greater N (p = 0.008) values than males. These empirical findings suggest that the inherent sex differences in walking stability should be considered in assessing and training locomotion.

SANE (Easy Gait Analysis System): Towards an AI-Assisted Automatic Gait-Analysis

The gait cycle of humans may be influenced by a range of variables, including neurological, orthopedic, and pathological conditions. Thus, gait analysis has a broad variety of applications, including the diagnosis of neurological disorders, the study of disease development, the assessment of the efficacy of a treatment, postural correction, and the evaluation and enhancement of sport performances. While the introduction of new technologies has resulted in substantial advancements, these systems continue to struggle to achieve a right balance between cost, analytical accuracy, speed, and convenience. The target is to provide low-cost support to those with motor impairments in order to improve their quality of life. The article provides a novel automated approach for motion characterization that makes use of artificial intelligence to perform real-time analysis, complete automation, and non-invasive, markerless analysis. This automated procedure enables rapid diagnosis and prevents human mistakes. The gait metrics obtained by the two motion tracking systems were compared to show the effectiveness of the proposed methodology.

Game-based Evaluation of Whole-body Movement Functions with CoM Stability and Motion Smoothness

In the field of rehabilitation, there is a great demand for an automatic and quantitative evaluation system. The balance ability is an essential factor for motor function evaluation related to posture control. Although balance ability is assessed using various indices in current clinical situations, most of previous studies developing an automatic evaluation system have used only a single particular index for balance evaluation. In this study, we developed a system that evaluates whole-body motor function using multiple indices based on the trajectory of the center of mass (CoM) and the motion smoothness. The system is inexpensive and little physical burden because the evaluation indices are calculated from the skeleton tracked by Kinect in a game environment. We attempt to capture the differences in individual motor functions which are difficult to be detected by qualitative visual observation.

Automated and accurate assessment for postural abnormalities in patients with Parkinson's disease based on Kinect and machine learning

Background

Automated and accurate assessment for postural abnormalities is necessary to monitor the clinical progress of Parkinson’s disease (PD). The combination of depth camera and machine learning makes this purpose possible.

Methods

Kinect was used to collect the postural images from 70 PD patients. The collected images were processed to extract three-dimensional body joints, which were then converted to two-dimensional body joints to obtain eight quantified coronal and sagittal features (F1-F8) of the trunk. The decision tree classifier was carried out over a data set established by the collected features and the corresponding doctors’ MDS-UPDRS-III 3.13 (the 13th item of the third part of Movement Disorder Society-Sponsored Revision of the Unified Parkinson’s Disease Rating Scale) scores. An objective function was implanted to further improve the human–machine consistency.

Results

The automated grading of postural abnormalities for PD patients was realized with only six selected features. The intraclass correlation coefficient (ICC) between the machine’s and doctors’ score was 0.940 (95%CI, 0.905–0.962), meaning the machine was highly consistent with the doctors’ judgement. Besides, the decision tree classifier performed outstandingly, reaching 90.0% of accuracy, 95.7% of specificity and 89.1% of sensitivity in rating postural severity.

Conclusions

We developed an intelligent evaluation system to provide accurate and automated assessment of trunk postural abnormalities in PD patients. This study demonstrates the practicability of our proposed method in the clinical scenario to help making the medical decision about PD.

Evaluating Lower Limb Kinematics Using Microsoft's Kinect: A Simple, Novel Method

Purpose: Our aim was to evaluate the Microsoft Kinect sensor (MKS) as a markerless system for motion capture and analysis of lower limb motion, compare it with a state-of-the-art marker-based system (MBS), and investigate its accuracy in simultaneously capturing several lower limb joint movements on several planes while participants walked freely. Method: Participants were asked to walk while gait data were simultaneously recorded by both the MKS and the MBS. Software for analysing the Kinect data stream was developed using Microsoft Visual Studio and Kinect for Windows software development kits. Visual three-dimensional (3D) C-Motion software was used to calculate 3D joint angles of the MBS. Deviation of the joint angles calculated by the two systems was calculated using root-mean-square error (RMSE) on the basis of a designated formula. Results: The calculated RMSE average was <5° between the two systems, a level of difference that has practically no clinical significance. Conclusions: Quantitative measurements of the joint angles of the knee and hip can be acquired using one MKS with some accuracy. The system can be advantageous for clinical use, at the pre- and post-treatment stages of rehabilitation, at significantly lower costs. Further evaluation of the MKS should be performed with larger study populations.

Concurrent validity of a custom computer vision algorithm for measuring lumbar spine motion from RGB-D camera depth data

Using RGB-D cameras as an alternative motion capture device can be advantageous for biomechanical spine motion assessments of movement quality and dysfunction due to their lower cost and complexity. In this study, we evaluated RGB-D camera performance relative to gold-standard optoelectronic motion capture equipment. Twelve healthy young adults (6M, 6F) were recruited to perform repetitive spine flexion-extension, while wearing infrared reflective marker clusters placed over their T₁₀-T₁₂ spinous processes and sacrum, and motion capture data were recorded simultaneously by both systems. Custom computer vision algorithms were developed to extract spine angles from depth data. Root mean square error (RMSE) was calculated for continuous Euler angles, and intraclass correlation coefficients (ICC_2,1) were calculated between minimum and maximum angles and range of motion in all movement planes. RMSE was low (RMSE ≤ 2.05°) and reliability was good to excellent (0.849 ≤ ICC_2,1 ≤ 0.979) across all movement planes. In conclusion, the proposed algorithm for tracking 3D lumbar spine motion during a sagittal movement task from one RGB-D camera is reliable in comparison to gold-standard motion tracking equipment. Future research will investigate accuracy and validity in a wider variety of movements, and will also investigate the development of novel methods to measure spine motion without using infrared reflective markers.

Placement Recommendations for Single Kinect-Based Motion Capture System in Unilateral Dynamic Motion Analysis

Low-cost, portable, and easy-to-use Kinect-based systems achieved great popularity in out-of-the-lab motion analysis. The placement of a Kinect sensor significantly influences the accuracy in measuring kinematic parameters for dynamics tasks. We conducted an experiment to investigate the impact of sensor placement on the accuracy of upper limb kinematics during a typical upper limb functional task, the drinking task. Using a 3D motion capture system as the golden standard, we tested twenty-one Kinect positions with three different distances and seven orientations. Upper limb joint angles, including shoulder flexion/extension, shoulder adduction/abduction, shoulder internal/external rotation, and elbow flexion/extension angles, are calculated via our developed Kinect kinematic model and the UWA kinematic model for both the Kinect-based system and the 3D motion capture system. We extracted the angles at the point of the target achieved (PTA). The mean-absolute-error (MEA) with the standard represents the Kinect-based system's performance. We conducted a two-way repeated measure ANOVA to explore the impacts of distance and orientation on the MEAs for all upper limb angles. There is a significant main effect for orientation. The main effects for distance and the interaction effects do not reach statistical significance. The post hoc test using LSD test for orientation shows that the effect of orientation is joint-dependent and plane-dependent. For a complex task (e.g., drinking), which involves body occlusions, placing a Kinect sensor right in front of a subject is not a good choice. We suggest that place a Kinect sensor at the contralateral side of a subject with the orientation around 30∘ to 45∘ for upper limb functional tasks. For all kinds of dynamic tasks, we put forward the following recommendations for the placement of a Kinect sensor. First, set an optimal sensor position for capture, making sure that all investigated joints are visible during the whole task. Second, sensor placement should avoid body occlusion at the maximum extension. Third, if an optimal location cannot be achieved in an out-of-the-lab environment, researchers could put the Kinect sensor at an optimal orientation by trading off the factor of distance. Last, for those need to assess functions of both limbs, the users can relocate the sensor and re-evaluate the functions of the other side once they finish evaluating functions of one side of a subject.

Advanced rehabilitation technology in orthopaedics-a narrative review

INTRODUCTION

As the demand for rehabilitation in orthopaedics increases, so too has the development in advanced rehabilitation technology. However, to date, there are no review papers outlining the broad scope of advanced rehabilitation technology used within the orthopaedic population. The aim of this study is to identify, describe and summarise the evidence for efficacy for all advanced rehabilitation technologies applicable to orthopaedic practice.

METHODS

The relevant literature describing the use of advanced rehabilitation technology in orthopaedics was identified from appropriate electronic databases (PubMed and EMBASE) and a narrative review undertaken.

RESULTS

Advanced rehabilitation technologies were classified into two groups: hospital-based and home-based rehabilitation. In the hospital-based technology group, we describe the use of continuous passive motion and robotic devices (after spinal cord injury) and their effect on improving clinical outcomes. We also report on the use of electromagnetic sensor technology for measuring kinematics of upper and lower limbs during rehabilitation. In the home-based technology group, we describe the use of inertial sensors, smartphones, software applications and commercial game hardware that are relatively inexpensive, user-friendly and widely available. We outline the evidence for videoconferencing for promoting knowledge and motivation for rehabilitation as well as the emerging role of virtual reality.

CONCLUSIONS

The use of advanced rehabilitation technology in orthopaedics is promising and evidence for its efficacy is generally supportive.

Muscle torques and joint accelerations provide more sensitive measures of poststroke movement deficits than joint angles

The whole repertoire of complex human motion is enabled by forces applied by our muscles and controlled by the nervous system. The impact of stroke on the complex multijoint motor control is difficult to quantify in a meaningful way that informs about the underlying deficit in the active motor control and intersegmental coordination. We tested whether poststroke deficit can be quantified with high sensitivity using motion capture and inverse modeling of a broad range of reaching movements. Our hypothesis is that muscle moments estimated based on active joint torques provide a more sensitive measure of poststroke motor deficits than joint angles. The motion of 22 participants was captured while performing reaching movements in a center-out task, presented in virtual reality. We used inverse dynamic analysis to derive active joint torques that were the result of muscle contractions, termed muscle torques, that caused the recorded multijoint motion. We then applied a novel analysis to separate the component of muscle torque related to gravity compensation from that related to intersegmental dynamics. Our results show that muscle torques characterize individual reaching movements with higher information content than joint angles do. Moreover, muscle torques enable distinguishing the individual motor deficits caused by aging or stroke from the typical differences in reaching between healthy individuals. Similar results were obtained using metrics derived from joint accelerations. This novel quantitative assessment method may be used in conjunction with home-based gaming motion capture technology for remote monitoring of motor deficits and inform the development of evidence-based robotic therapy interventions.NEW & NOTEWORTHY Functional deficits seen in task performance have biomechanical underpinnings, seen only through the analysis of forces. Our study has shown that estimating muscle moments can quantify with high-sensitivity poststroke deficits in intersegmental coordination. An assessment developed based on this method could help quantify less observable deficits in mildly affected stroke patients. It may also bridge the gap between evidence from studies of constrained or robotically manipulated movements and research with functional and unconstrained movements.

Walking speed measurement technology: A review

Purpose of review

This article presents an overview of the main technologies used to estimate gait parameters, focusing on walking speed (WS).

Recent findings

New wearable and environmental technologies to estimate WS have been developed in the last five years. Wearable technologies refer to sensors attached to parts of the patient's body that capture the kinematics during walking. Alternatively, environmental technologies capture walking patterns using external instrumentation. In this review, wearable and external technologies have been included.From the different works reviewed, external technologies face the challenge of implementation outside controlled facilities; an advantage that wearable technologies have, but have not been fully explored. Additionally, systems that can track WS changes in daily activities, especially at-home assessments, have not been developed.

Summary

Walking speed is a gait parameter that can provide insight into an individual's health status. Image-based, walkways, wearable, and floor-vibrations technologies are the most current used technologies for estimating WS. In this paper, research from the last five years that explore each technology's capabilities on WS estimation and an evaluation of their technical and clinical aspects is presented.

Objective Analysis of Movement in Subjects with ADHD. Multidisciplinary Control Tool for Students in the Classroom

The term Attention Deficit Hyperactivity Disorder (ADHD) has a long history of problems behind it. The origin of all these problems lies in the lack of agreement in the assessment procedures and evaluation instruments. The diagnosis is clinical and is determined by the observation and information provided by parents and teachers. So, this is highly subjective and leads to disparate results. Therefore, on the one hand the inaccuracy of the diagnosis of ADHD, which has been based on subjective criteria, together with the fact that hyperactivity is one of the main symptoms of this disorder, implies that several studies (with limitations) have been carried out to record objective measures of movement in subjects in at least the last ten years. In order to solve some of this derived problems and limitations of previous studies, a computer program has been developed to objectively record the amount of movement of subjects. The main objective of this study is threefold: first to register the amount of movement of both experimental group and control group, then to compare them with the movement registered by observers and finally to determine the validity of the software developed as a tool to support the diagnosis of ADHD. Results show that there are significant differences in the amount of objective movement between a clinical group of subjects with ADHD and a control group, obtaining a higher average of movement the experimental group. In addition, results also demonstrate that the developed software is a valid tool for the evaluation of movement that solves the limitations of previous studies. The proposed tool is developed from different aspects to give it a multidisciplinary character.

Gait Analysis in Parkinson's Disease: An Overview of the Most Accurate Markers for Diagnosis and Symptoms Monitoring

The aim of this review is to summarize that most relevant technologies used to evaluate gait features and the associated algorithms that have shown promise to aid diagnosis and symptom monitoring in Parkinson's disease (PD) patients. We searched PubMed for studies published between 1 January 2005, and 30 August 2019 on gait analysis in PD. We selected studies that have either used technologies to distinguish PD patients from healthy subjects or stratified PD patients according to motor status or disease stages. Only those studies that reported at least 80% sensitivity and specificity were included. Gait analysis algorithms used for diagnosis showed a balanced accuracy range of 83.5-100%, sensitivity of 83.3-100% and specificity of 82-100%. For motor status discrimination the gait analysis algorithms showed a balanced accuracy range of 90.8-100%, sensitivity of 92.5-100% and specificity of 88-100%. Despite a large number of studies on the topic of objective gait analysis in PD, only a limited number of studies reported algorithms that were accurate enough deemed to be useful for diagnosis and symptoms monitoring. In addition, none of the reported algorithms and technologies has been validated in large scale, independent studies.

Deep Cerebellar Transcranial Direct Current Stimulation of the Dentate Nucleus to Facilitate Standing Balance in Chronic Stroke Survivors-A Pilot Study

Objective: Cerebrovascular accidents are the second leading cause of death and the third leading cause of disability worldwide. We hypothesized that cerebellar transcranial direct current stimulation (ctDCS) of the dentate nuclei and the lower-limb representations in the cerebellum can improve functional reach during standing balance in chronic (>6 months’ post-stroke) stroke survivors. Materials and Methods: Magnetic resonance imaging (MRI) based subject-specific electric field was computed across a convenience sample of 10 male chronic (>6 months) stroke survivors and one healthy MRI template to find an optimal bipolar bilateral ctDCS montage to target dentate nuclei and lower-limb representations (lobules VII–IX). Then, in a repeated-measure crossover study on a subset of 5 stroke survivors, we compared 15 min of 2 mA ctDCS based on the effects on successful functional reach (%) during standing balance task. Three-way ANOVA investigated the factors of interest– brain regions, montages, stroke participants, and their interactions. Results: “One-size-fits-all” bipolar ctDCS montage for the clinical study was found to be PO9h–PO10h for dentate nuclei and Exx7–Exx8 for lobules VII–IX with the contralesional anode. PO9h–PO10h ctDCS performed significantly (alpha = 0.05) better in facilitating successful functional reach (%) when compared to Exx7–Exx8 ctDCS. Furthermore, a linear relationship between successful functional reach (%) and electric field strength was found where PO9h–PO10h montage resulted in a significantly (alpha = 0.05) higher electric field strength when compared to Exx7–Exx8 montage for the same 2 mA current. Conclusion: We presented a rational neuroimaging based approach to optimize deep ctDCS of the dentate nuclei and lower limb representations in the cerebellum for post-stroke balance rehabilitation. However, this promising pilot study was limited by “one-size-fits-all” bipolar ctDCS montage as well as a small sample size.

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.

Home self-training: Visual feedback for assisting physical activity for stroke survivors

BACKGROUND AND OBJECTIVE

With the increase in the number of stroke survivors, there is an urgent need for designing appropriate home-based rehabilitation tools to reduce health-care costs. The objective is to empower the rehabilitation of post-stroke patients at the comfort of their homes by supporting them while exercising without the physical presence of the therapist.

METHODS

A novel low-cost home-based training system is introduced. This system is designed as a composition of two linked applications: one for the therapist and another one for the patient. The therapist prescribes personalized exercises remotely, monitors the home-based training and re-adapts the exercises if required. On the other side, the patient loads the prescribed exercises, trains the prescribed exercise while being guided by color-based visual feedback and gets updates about the exercise performance. To achieve that, our system provides three main functionalities, namely: 1) Feedback proposals guiding a personalized exercise session, 2) Posture monitoring optimizing the effectiveness of the session, 3) Assessment of the quality of the motion.

RESULTS

The proposed system is evaluated on 10 healthy participants without any previous contact with the system. To analyze the impact of the feedback proposals, we carried out two different experimental sessions: without and with feedback proposals. The obtained results give preliminary assessments about the interest of using such feedback.

CONCLUSIONS

Obtained results on 10 healthy participants are promising. This encourages to test the system in a realistic clinical context for the rehabilitation of stroke survivors.

Expanding instrumented gait testing in the community setting: A portable, depth-sensing camera captures joint motion in older adults

BACKGROUND

Currently, it is not feasible to obtain laboratory-based measures of joint motion in large numbers of older adults. We assessed the utility of a portable depth-sensing camera for quantifying hip and knee joint motion of older adults during mobility testing in the community.

METHODS

Participants were 52 older adults enrolled in the Rush Memory and Aging Project, a community-based cohort study of aging. In a subset, we compared dynamic hip and knee flexion/extension obtained via the depth-sensing camera with that obtained concurrently using a laboratory-based optoelectronic motion capture system. Then we recorded participants' annual instrumented gait assessment in the community setting with the depth-sensing camera and examined the inter-relationships of hip and knee range of motion (ROM) with mobility metrics derived from a wearable sensor and other mobility-related health measures.

RESULTS

In the community, we successfully acquired joint motion from 49/52 participants using the depth-sensing camera. Hip and knee ROMs were related to diverse sensor-derived metrics of mobility performance (hip: Pearson's r = 0.31 to 0.58; knee: Pearson's r = 0.29 to 0.51), as well as daily physical activity, conventional motor measures, self-report hip and knee pain and dysfunction, mobility disability, and falls.

CONCLUSIONS

The depth-sensing camera's high rate of successful data acquisition and correlations of its hip and knee ROMs with other mobility measures suggest that this device can provide a cost-efficient means of quantifying joint motion in large numbers of community-dwelling older adults who span the health spectrum.

Reliability and validity of the Kinect V2 for the assessment of lower extremity rehabilitation exercises

BACKGROUND

Besides its initial use as a video gaming system the Kinect might also be suitable to capture human movements in the clinical context. However, the system's reliability and validity to capture rehabilitation exercises is unclear.

RESEARCH QUESTION

The purpose of this study was to evaluate the test-retest reliability of lower extremity kinematics during squat, hip abduction and lunge exercises captured by the Kinect and to evaluate the agreement to a reference 3D camera-based motion system.

METHODS

Twenty-one healthy individuals performed five repetitions of each lower limb exercise on two different days. Movements were simultaneously assessed by the Kinect and the reference 3D motion system. Joint angles and positions of the lower limb were calculated for sagittal and frontal plane. For the inter-session reliability and the agreement between the two systems standard error of measurement (SEM), bias with limits of agreement (LoA) and Pearson Correlation Coefficient (r) were calculated.

RESULTS

Parameters indicated varying reliability for the assessed joint angles and positions and decreasing reliability with increasing task complexity. Across all exercises, measurement deviations were shown especially for small movement amplitudes. Variability was acceptable for joint angles and positions during the squat, partially acceptable during the hip abduction and predominately inacceptable during the lunge. The agreement between systems was characterized by systematic errors. Overestimations by the Kinect were apparent for hip flexion during the squat and hip abduction/adduction during the hip abduction exercise as well as for the knee positions during the lunge. Knee and hip flexion during hip abduction and lunge were underestimated by the Kinect.

SIGNIFICANCE

The Kinect system can reliably assess lower limb joint angles and positions during simple exercises. The validity of the system is however restricted. An application in the field of early orthopedic rehabilitation without further development of post-processing techniques seems so far limited.

Validity and Reliability of Upper Limb Functional Assessment Using the Microsoft Kinect V2 Sensor

Objective

To quantify the concurrent accuracy and the test-retest reliability of a Kinect V2-based upper limb functional assessment system.

Approach

Ten healthy males performed a series of upper limb movements, which were measured concurrently with Kinect V2 and the Vicon motion capture system (gold standard). Each participant attended two testing sessions, seven days apart. Four tasks were performed including hand to contralateral shoulder, hand to mouth, combing hair, and hand to back pocket. Upper limb kinematics were calculated using our developed kinematic model and the UWA model for Kinect V2 and Vicon. The interdevice coefficient of multiple correlation (CMC) and the root mean squared error (RMSE) were used to evaluate the validity of the kinematic waveforms. Mean absolute bias and Pearson's r correlation were used to evaluate the validity of the angles at the points of target achieved (PTA) and the range of motion (ROM). The intersession CMC and RMSE and the intraclass correlation coefficient (ICC) were used to assess the test-retest reliability of Kinect V2.

Main Results

Both validity and reliability are found to be task-dependent and plane-dependent. Kinect V2 had good accuracy in measuring shoulder and elbow flexion/extension angular waveforms (CMC > 0.87), moderate accuracy of measuring shoulder adduction/abduction angular waveforms (CMC = 0.69-0.82), and poor accuracy of measuring shoulder internal/external angles (CMC < 0.6). We also found high test-retest reliability of Kinect V2 in most of the upper limb angular waveforms (CMC = 0.75-0.99), angles at the PTA (ICC = 0.65-0.91), and the ROM (ICC = 0.68-0.96).

Significance

Kinect V2 has great potential as a low-cost, easy implemented device for assessing upper limb angular waveforms when performing functional tasks. The system is suitable for assessing relative within-person change in upper limb motions over time, such as disease progression or improvement due to intervention.

Three-dimensional cameras and skeleton pose tracking for physical function assessment: A review of uses, validity, current developments and Kinect alternatives

BACKGROUND

Three-dimensional camera systems that integrate depth assessment with traditional two-dimensional images, such as the Microsoft Kinect, Intel Realsense, StereoLabs Zed and Orbecc, hold great promise as physical function assessment tools. When combined with point cloud and skeleton pose tracking software they can be used to assess many different aspects of physical function and anatomy. These assessments have received great interest over the past decade, and will likely receive further study as the integration of depth sensing and augmented reality smartphone cameras occurs more in everyday life.

RESEARCH QUESTION

The aim of this review is to discuss how these devices work, what options are available, the best methods for performing assessments and how they can be used in the future.

METHODS

Firstly, a review of the Microsoft Kinect devices and associated artificial intelligence, automated skeleton tracking algorithms is provided. This includes a narrative critique of the validity and clinical utility of these devices for assessing different aspects of physical function including spatiotemporal, kinematic and inverse dynamics data derived from gait and balance trials, and anatomical assessments performed using the depth sensor information. Methods for improving the accuracy of data are examined, including multiple-camera systems and sensor fusion with inertial monitoring units, model fitting, and marker tracking. Secondly, alternative hardware, including other structured light and time of flight methods, stereoscopic cameras and augmented reality leveraging smartphone and tablet cameras to perform measurements in three-dimensional space are summarised. Software options related to depth sensing cameras are then discussed, focussing on recent advances such as OpenPose and web-based methods such as PoseNet.

RESULTS AND SIGNIFICANCE

The clinical and non-laboratory utility of these devices holds great promise for physical function assessment, and recent developments could strengthen their ability to provide important and impactful health-related data.

Validity of a novel method to measure vertical oscillation during running using a depth camera

Recent advancements in low-cost depth cameras may provide a clinically accessible alternative to conventional three-dimensional (3D) multi-camera motion capture systems for gait analysis. However, there remains a lack of information on the validity of clinically relevant running gait parameters such as vertical oscillation (VO). The purpose of this study was to assess the validity of measures of VO during running gait using raw depth data, in comparison to a 3D multi-camera motion capture system. Sixteen healthy adults ran on a treadmill at a standard speed of 2.7 m/s. The VO of their running gait was simultaneously collected from raw depth data (Microsoft Kinect v2) and 3D marker data (Vicon multi-camera motion capture system). The agreement between the VO measures obtained from the two systems was assessed using a Bland-Altman plot with 95% limits of agreement (LOA), a Pearson's correlation coefficient (r), and a Lin's concordance correlation coefficient (r_c). The depth data from the Kinect v2 demonstrated excellent results across all measures of validity (r = 0.97; r_c = 0.97; 95% LOA = -8.0 mm - 8.7 mm), with an average absolute error and percent error of 3.7 (2.1) mm and 4.0 (2.0)%, respectively. The findings of this study have demonstrated the ability of a low cost depth camera and a novel tracking method to accurately measure VO in running gait.

Assessment of Sit-to-Stand Movements Using a Single Kinect Sensor

The aim of this article is to show that the absolute measurement difference between the Kinect-based system and the marker-based system for the sit-to-stand exercise is less important provided that the difference is caused by systematic errors in the Kinect measurement. Three healthy individuals participated in this study. Marker trajectories and Kinect skeletal joint data were collected while each subject performed the sit-to-stand exercise. Three key parameters, the initial, minimum, and maximum hip angles obtained from the two systems are compared. This preliminary study shows that the Kinect-based system has good agreement with the marker-based system, and it has comparable between-trial reliability and variability to those of the marker-based system. This study shows that Kinect can be used to reliably assess the quality of the sit-to-stand exercise provided that the systematic measurement errors are compensated. This study establishes the foundation to implement a Kinect-based system to automatically monitor, assess, and provide realtime feedback to a patient who carries out the sit-to-stand exercise with or without the supervision of a clinician.

Using Kinect to classify Parkinson's disease stages related to severity of gait impairment

BACKGROUND

Parkinson's Disease (PD) is a chronic neurodegenerative disease associated with motor problems such as gait impairment. Different systems based on 3D cameras, accelerometers or gyroscopes have been used in related works in order to study gait disturbances in PD. Kinect Ⓡ has also been used to build these kinds of systems, but contradictory results have been reported: some works conclude that Kinect does not provide an accurate method of measuring gait kinematics variables, but others, on the contrary, report good accuracy results.

METHODS

In this work, we have built a Kinect-based system that can distinguish between different PD stages, and have performed a clinical study with 30 patients suffering from PD belonging to three groups: early PD patients without axial impairment, more evolved PD patients with higher gait impairment but without Freezing of Gait (FoG), and patients with advanced PD and FoG. Those patients were recorded by two Kinect devices when they were walking in a hospital corridor. The datasets obtained from the Kinect were preprocessed, 115 features identified, some methods were applied to select the relevant features (correlation based feature selection, information gain, and consistency subset evaluation), and different classification methods (decision trees, Bayesian networks, neural networks and K-nearest neighbours classifiers) were evaluated with the goal of finding the most accurate method for PD stage classification.

RESULTS

The classifier that provided the best results is a particular case of a Bayesian Network classifier (similar to a Naïve Bayesian classifier) built from a set of 7 relevant features selected by the correlation-based on feature selection method. The accuracy obtained for that classifier using 10-fold cross validation is 93.40%. The relevant features are related to left shin angles, left humerus angles, frontal and lateral bents, left forearm angles and the number of steps during spin.

CONCLUSIONS

In this paper, it is shown that using Kinect is adequate to build a inexpensive and comfortable system that classifies PD into three different stages related to FoG. Compared to the results of previous works, the obtained accuracy (93.40%) can be considered high. The relevant features for the classifier are: a) movement and position of the left arm, b) trunk position for slightly displaced walking sequences, and c) left shin angle, for straight walking sequences. However, we have obtained a better accuracy (96.23%) for a classifier that only uses features extracted from slightly displaced walking steps and spin walking steps. Finally, the obtained set of relevant features may lead to new rehabilitation therapies for PD patients with gait problems.

Assessment of a markerless motion analysis system for manual wheelchair application

BACKGROUND

Wheelchair biomechanics research advances accessibility and clinical care for manual wheelchair users. Standardized outcome assessments are vital tools for tracking progress, but there is a strong need for more quantitative methods. A system offering kinematic, quantitative detection, with the ease of use of a standardized outcome assessment, would be optimal for repeated, longitudinal assessment of manual wheelchair users' therapeutic progress, but has yet to be offered.

RESULTS

This work evaluates a markerless motion analysis system for manual wheelchair mobility in clinical, community, and home settings. This system includes Microsoft® Kinect® 2.0 sensors, OpenSim musculoskeletal modeling, and an automated detection, processing, and training interface. The system is designed to be cost-effective, easily used by caregivers, and capable of detecting key kinematic metrics involved in manual wheelchair propulsion. The primary technical advancements in this research are the software components necessary to detect and process the upper extremity kinematics during manual wheelchair propulsion, along with integration of the components into a complete system. The study defines and evaluates an adaptable systems methodology for processing kinematic data using motion capture technology and open-source musculoskeletal models to assess wheelchair propulsion pattern and biomechanics, and characterizes its accuracy, sensitivity and repeatability. Inter-trial repeatability of spatiotemporal parameters, joint range of motion, and musculotendon excursion were all found to be significantly correlated (p < 0.05).

CONCLUSIONS

The system is recommended for use in clinical settings for frequent wheelchair propulsion assessment, provided the limitations in precision are considered. The motion capture-model software bridge methodology could be applied in the future to any motion-capture system or specific application, broadening access to detailed kinematics while reducing assessment time and cost.

Investigating the impact of a motion capture system on Microsoft Kinect v2 recordings: A caution for using the technologies together

Microsoft Kinect sensors are considered to be low-cost popular RGB-D sensors and are widely employed in various applications. Consequently, several studies have been conducted to evaluate the reliability and validity of Microsoft Kinect sensors, and noise models have been proposed for the sensors. Several studies utilized motion capture systems as a golden standard to assess the Microsoft Kinect sensors, and none of them reported interference between Kinect sensors and motion capture systems. This study aimed to investigate possible interference between a golden standard (i.e., Qualisys) and Microsoft Kinect v2. The depth recordings of Microsoft Kinect sensors were processed to estimate the intensity of interference. A flat non-reflective surface was utilized, and smoothness of the surface was measured using Microsoft Kinect v2 in absence and presence of an active motion capture system. The recording was repeated in five different distances. The results indicated that Microsoft Kinect v2 is distorted by the motion capture system and the distortion is increasing by increasing distance between Kinect and region of interest. Regarding the results, it can be concluded that the golden standard motion capture system is robust against interference from the Microsoft Kinect sensors.

Validation of wearable visual feedback for retraining foot progression angle using inertial sensors and an augmented reality headset

Background

Gait retraining interventions using real-time biofeedback have been proposed to alter the loading across the knee joint in patients with knee osteoarthritis. Despite the demonstrated benefits of these conservative treatments, their clinical adoption is currently obstructed by the high complexity, spatial demands, and cost of optical motion capture systems. In this study we propose and evaluate a wearable visual feedback system for gait retraining of the foot progression angle (FPA).

Methods

The primary components of the system are inertial measurement units, which track the human movement without spatial limitations, and an augmented reality headset used to project the visual feedback in the visual field. The adapted gait protocol contained five different target angles ranging from 15 degrees toe-out to 5 degrees toe-in. Eleven healthy participants walked on an instrumented treadmill, and the protocol was performed using both an established laboratory visual feedback driven by optical motion capture, and the proposed wearable system.

Results and conclusions

The wearable system tracked FPA with an accuracy of 2.4 degrees RMS and ICC=0.94 across all target angles and subjects, when compared to an optical motion capture reference. In addition, the effectiveness of the biofeedback, reflected by the number of steps with FPA value ±2 degrees from the target, was found to be around 50% in both wearable and laboratory approaches. These findings demonstrate that retraining of the FPA using wearable inertial sensing and visual feedback is feasible with effectiveness matching closely an established laboratory method. The proposed wearable setup may reduce the complexity of gait retraining applications and facilitate their transfer to routine clinical practice.

3D Analysis of Upper Limbs Motion during Rehabilitation Exercises Using the KinectTM Sensor: Development, Laboratory Validation and Clinical Application

Optoelectronic devices are the gold standard for 3D evaluation in clinics, but due to the complexity of this kind of hardware and the lack of access for patients, affordable, transportable, and easy-to-use systems must be developed to be largely used in daily clinics. The Kinect^TM sensor has various advantages compared to optoelectronic devices, such as its price and transportability. However, it also has some limitations: (in)accuracy of the skeleton detection and tracking as well as the limited amount of available points, which makes 3D evaluation impossible. To overcome these limitations, a novel method has been developed to perform 3D evaluation of the upper limbs. This system is coupled to rehabilitation exercises, allowing functional evaluation while performing physical rehabilitation. To validate this new approach, a two-step method was used. The first step was a laboratory validation where the results obtained with the Kinect^TM were compared with the results obtained with an optoelectronic device; 40 healthy young adults participated in this first part. The second step was to determine the clinical relevance of this kind of measurement. Results of the healthy subjects were compared with a group of 22 elderly adults and a group of 10 chronic stroke patients to determine if different patterns could be observed. The new methodology and the different steps of the validations are presented in this paper.

Validity of motion analysis using the Kinect system to evaluate single leg stance in patients with hip disorders

BACKGROUND

Abnormal movements during single leg stance in patients with hip disorders could be detected by the Kinect system as well as the three-dimensional motion analysis system. However, validity of the Kinect system to evaluate single leg stance in patients with hip disorders remains unknown.

RESEARCH QUESTION

To investigate the concurrent validity of the Kinect system, relative to the VICON three-dimensional system which is considered as the gold standard for motion analysis, to measure trunk and pelvis alignment during single leg stance. To investigate the discriminant validity of the Kinect system between with and without hip disorders.

METHODS

For evaluation of the concurrent validity of the Kinect system, the intra-class correlation coefficient (ICC _(3,1)) was calculated for the angle of inclination of the pelvis and trunk in 5 healthy individuals. For evaluation of the discriminant validity of the Kinect system, the angle of inclination of the pelvis and trunk during single leg stance were measured in 27 individuals with hip disorders and 100 healthy individuals. Differences in the maximum angle of inclination of the pelvis and trunk were evaluated between hip disorders and healthy individuals using the Mann-Whitney U test.

RESULTS

ICC values were between 0.83-0.93 for the pelvic and 0.63-0.81 for the trunk angle, respectively. The maximum trunk inclination angle calculated using the Kinect system was significantly higher in patients with hip disorders than healthy individuals, with no significant between-group difference in the angle of inclination of the pelvis.

SIGNIFICANCE

The Kinect system was adequate to detect certain abnormal movements during single leg stance among patients with hip disorders. Therefore, the Kinect system could provide a convenient motion analysis tool for the assessment of patients with hip disorders.

Application of Commercial Games for Home-Based Rehabilitation for People with Hemiparesis: Challenges and Lessons Learned

OBJECTIVE

To identify the factors that influence the use of an at-home virtual rehabilitation gaming system from the perspective of therapists, engineers, and adults and adolescents with hemiparesis secondary to stroke, brain injury, and cerebral palsy.

MATERIALS AND METHODS

This study reports on qualitative findings from a study, involving seven adults (two female; mean age: 65 ± 8 years) and three adolescents (one female; mean age: 15 ± 2 years) with hemiparesis, evaluating the feasibility and clinical effectiveness of a home-based custom-designed virtual rehabilitation system over 2 months. Thematic analysis was used to analyze qualitative data from therapists' weekly telephone interview notes, research team documentation regarding issues raised during technical support interactions, and the transcript of a poststudy debriefing session involving research team members and collaborators.

RESULTS

Qualitative themes that emerged suggested that system use was associated with three key factors as follows: (1) the technology itself (e.g., characteristics of the games and their clinical implications, system accessibility, and hardware and software design); (2) communication processes (e.g., preferences and effectiveness of methods used during the study); and (3) knowledge and training of participants and therapists on the technology's use (e.g., familiarity with Facebook, time required to gain competence with the system, and need for clinical observations during remote therapy). Strategies to address these factors are proposed.

CONCLUSION

Lessons learned from this study can inform future clinical and implementation research using commercial videogames and social media platforms. The capacity to track compensatory movements, clinical considerations in game selection, the provision of kinematic and treatment progress reports to participants, and effective communication and training for therapists and participants may enhance research success, system usability, and adoption.

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.

Technologies for Advanced Gait and Balance Assessments in People with Multiple Sclerosis

Subtle gait and balance dysfunction is a precursor to loss of mobility in multiple sclerosis (MS). Biomechanical assessments using advanced gait and balance analysis technologies can identify these subtle changes and could be used to predict mobility loss early in the disease. This update critically evaluates advanced gait and balance analysis technologies and their applicability to identifying early lower limb dysfunction in people with MS. Non-wearable (motion capture systems, force platforms, and sensor-embedded walkways) and wearable (pressure and inertial sensors) biomechanical analysis systems have been developed to provide quantitative gait and balance assessments. Non-wearable systems are highly accurate, reliable and provide detailed outcomes, but require cumbersome and expensive equipment. Wearable systems provide less detail but can be used in community settings and can provide real-time feedback to patients and clinicians. Biomechanical analysis using advanced gait and balance analysis technologies can identify changes in gait and balance in early MS and consequently have the potential to significantly improve monitoring of mobility changes in MS.

Trunk lean gait decreases multi-segmental coordination in the vertical direction

[Purpose] The strategy of trunk lean gait to reduce external knee adduction moment (KAM) may affect multi-segmental synergy control of center of mass (COM) displacement. Uncontrolled manifold (UCM) analysis is an evaluation index to understand motor variability. The purpose of this study was to investigate how motor variability is affected by using UCM analysis on adjustment of the trunk lean angle. [Subjects and Methods] Fifteen healthy young adults walked at their preferred speed under two conditions: normal and trunk lean gait. UCM analysis was performed with respect to the COM displacement during the stance phase. The KAM data were analyzed at the points of the first KAM peak during the stance phase. [Results] The KAM during trunk lean gait was smaller than during normal gait. Despite a greater segmental configuration variance with respect to mediolateral COM displacement during trunk lean gait, the synergy index was not significantly different between the two conditions. The synergy index with respect to vertical COM displacement during trunk lean gait was smaller than that during normal gait. [Conclusion] These results suggest that trunk lean gait is effective in reducing KAM; however, it may decrease multi-segmental movement coordination of COM control in the vertical direction.

Can shoulder range of movement be measured accurately using the Microsoft Kinect sensor plus Medical Interactive Recovery Assistant (MIRA) software?

BACKGROUND

This study compared the accuracy of measuring shoulder range of movement (ROM) with a simple laptop-sensor combination vs. trained observers (shoulder physiotherapists and shoulder surgeons) using motion capture (MoCap) laboratory equipment as the gold standard.

METHODS

The Microsoft Kinect sensor (Microsoft Corp., Redmond, WA, USA) tracks 3-dimensional human motion. Ordinarily used with an Xbox (Microsoft Corp.) video game console, Medical Interactive Recovery Assistant (MIRA) software (MIRA Rehab Ltd., London, UK) allows this small sensor to measure shoulder movement with a standard computer. Shoulder movements of 49 healthy volunteers were simultaneously measured by trained observers, MoCap, and the MIRA device. Internal rotation was assessed with the shoulder abducted 90° and external rotation with the shoulder adducted. Visual estimation and MIRA measurements were compared with gold standard MoCap measurements for agreement using Bland-Altman methods.

RESULTS

There were 1670 measurements analyzed. The MIRA evaluations of all 4 cardinal shoulder movements were significantly more precise, with narrower limits of agreement, than the measurements of trained observers. MIRA achieved ±11° (95% confidence interval [CI], 8.7°-12.6°) for forward flexion vs. ±16° (95% CI, 14.6°-17.6°) by trained observers. For abduction, MIRA showed ±11° (95% CI, 8.7°-12.8°) against ±15° (95% CI, 13.4°-16.2°) for trained observers. MIRA attained ±10° (95% CI, 8.1°-11.9°) during external rotation measurement, whereas trained observers only reached ±21° (95% CI, 18.7°-22.6°). For internal rotation, MIRA achieved ±9° (95% CI, 7.2°-10.4°), which was again better than TOs at ±18° (95% CI, 16.0°-19.3°).

CONCLUSIONS

A laptop combined with a Microsoft Kinect sensor and the MIRA software can measure shoulder movements with acceptable levels of accuracy. This technology, which can be easily set up, may also allow precise shoulder ROM measurement outside the clinic setting.

Self-esteem recognition based on gait pattern using Kinect

BACKGROUND

Self-esteem is an important aspect of individual's mental health. When subjects are not able to complete self-report questionnaire, behavioral assessment will be a good supplement. In this paper, we propose to use gait data collected by Kinect as an indicator to recognize self-esteem.

METHODS

178 graduate students without disabilities participate in our study. Firstly, all participants complete the 10-item Rosenberg Self-Esteem Scale (RSS) to acquire self-esteem score. After completing the RRS, each participant walks for two minutes naturally on a rectangular red carpet, and the gait data are recorded using Kinect sensor. After data preprocessing, we extract a few behavioral features to train predicting model by machine learning. Based on these features, we build predicting models to recognize self-esteem.

RESULTS

For self-esteem prediction, the best correlation coefficient between predicted score and self-report score is 0.45 (p<0.001). We divide the participants according to gender, and for males, the correlation coefficient is 0.43 (p<0.001), for females, it is 0.59 (p<0.001).

CONCLUSION

Using gait data captured by Kinect sensor, we find that the gait pattern could be used to recognize self-esteem with a fairly good criterion validity. The gait predicting model can be taken as a good supplementary method to measure self-esteem.

Using a Kinect sensor to acquire biological motion: Toolbox and evaluation

Biological motion (BM) is the movement of animate entities, which conveys rich social information. To obtain pure BM, researchers nowadays predominantly use point-light displays (PLDs), which depict BM through a set of light points (e.g., 12 points) placed at distinct joints of a moving human body. Most prevalent BM stimuli are created by state-of-the-art motion capture systems. Although these stimuli are highly precise, the motion capture system is expensive and bulky, and its process of constructing a PLD-based BM is time-consuming and complex. These factors impede the investigation of BM mechanisms. In this study, we propose a free Kinect-based biological motion capture (KBC) toolbox based on the Kinect Sensor 2.0 in C++. The KBC toolbox aims to help researchers acquire PLD-based BM in an easy, low-cost, and user-friendly way. We conducted three experiments to examine whether KBC-generated BM can genuinely reflect the processing characteristics of BM: (1) Is BM from this source processed globally in vision? (2) Does its BM (e.g., from the feet) retain detailed local information? and (3) Does the BM convey emotional information? We obtained positive results in response to all three questions. Therefore, we think that the KBC toolbox can be useful in generating BM for future research.

Gait rehabilitation for a patient with an osseointegrated prosthesis following transfemoral amputation

BACKGROUND

In patients with a transfemoral amputation socket-related problems are associated with reduced prosthetic use, activity, and quality of life. Furthermore, gait asymmetries are present that may explain secondary complaints. Bone-anchored prostheses (BAPs) may help these patients. Two types of BAP are available, screw and press-fit implants. Rehabilitation following surgery for a press-fit BAP is poorly described.

PURPOSE

To describe a rehabilitation program designed to minimize compensation strategies and increase activity using a case-report of an active, 70-year-old man with a traumatic transfemoral amputation who had used a socket prosthesis for 52 years and received a press-fit BAP [Endo-Exo Femoral Prosthesis - EEFP].

INTERVENTION

A 13-week physiotherapy program.

OUTCOMES

Outcomes were assessed before surgery, at the end of rehabilitation, and six-month and one-year follow-ups. After rehabilitation gait had improved, the patient had more arm movement, more pelvic shift, less hip rotation during swing phase on the prosthetic side, and absence of vaulting on the sound side. Isometric hip abductor strength was 15% higher on the sound side and 16% higher on the prosthetic side, and walking distance increased from 200 m to 1500 m. At the six-month follow-up, the patient had lower back complications and reduced hip abductor strength and walking distance. At one-year follow-up, walking distance had recovered to 1000 m and gait pattern had improved again, with yielding and absence of terminal impact on the prosthetic side.

CONCLUSION

The described rehabilitation program may be an effective method of improving gait in patients with an EEFP even after long-term socket usage.

Improved kinect-based spatiotemporal and kinematic treadmill gait assessment

A cost-effective, clinician friendly gait assessment tool that can automatically track patients' anatomical landmarks can provide practitioners with important information that is useful in prescribing rehabilitative and preventive therapies. This study investigated the validity and reliability of the Microsoft Kinect v2 as a potential inexpensive gait analysis tool. Ten healthy subjects walked on a treadmill at 1.3 and 1.6m·s^-1, as spatiotemporal parameters and kinematics were extracted concurrently using the Kinect and three-dimensional motion analysis. Spatiotemporal measures included step length and width, step and stride times, vertical and mediolateral pelvis motion, and foot swing velocity. Kinematic outcomes included hip, knee, and ankle joint angles in the sagittal plane. The absolute agreement and relative consistency between the two systems were assessed using interclass correlations coefficients (ICC2,1), while reproducibility between systems was established using Lin's Concordance Correlation Coefficient (rc). Comparison of ensemble curves and associated 90% confidence intervals (CI90) of the hip, knee, and ankle joint angles were performed to investigate if the Kinect sensor could consistently and accurately assess lower extremity joint motion throughout the gait cycle. Results showed that the Kinect v2 sensor has the potential to be an effective clinical assessment tool for sagittal plane knee and hip joint kinematics, as well as some spatiotemporal temporal variables including pelvis displacement and step characteristics during the gait cycle.

A Real-Time Kinect Signature-Based Patient Home Monitoring System

Assessment of body kinematics during performance of daily life activities at home plays a significant role in medical condition monitoring of elderly people and patients with neurological disorders. The affordable and non-wearable Microsoft Kinect ("Kinect") system has been recently used to estimate human subject kinematic features. However, the Kinect suffers from a limited range and angular coverage, distortion in skeleton joints' estimations, and erroneous multiplexing of different subjects' estimations to one. This study addresses these limitations by incorporating a set of features that create a unique "Kinect Signature". The Kinect Signature enables identification of different subjects in the scene, automatically assign the kinematics feature estimations only to the subject of interest, and provide information about the quality of the Kinect-based estimations. The methods were verified by a set of experiments, which utilize real-time scenarios commonly used to assess motor functions in elderly subjects and in subjects with neurological disorders. The experiment results indicate that the skeleton based Kinect Signature features can be used to identify different subjects in high accuracy. We demonstrate how these capabilities can be used to assign the Kinect estimations to the Subject of Interest, and exclude low quality tracking features. The results of this work can help in establishing reliable kinematic features, which can assist in future to obtain objective scores for medical analysis of patient condition at home while not restricted to perform daily life activities.

Development and preliminary validation of an interactive remote physical therapy system

In this paper, we present an interactive physical therapy system (IPTS) for remote quantitative assessment of clients in the home. The system consists of two different interactive interfaces connected through a network, for a real-time low latency video conference using audio, video, skeletal, and depth data streams from a Microsoft Kinect. To test the potential of IPTS, experiments were conducted with 5 independent living senior subjects in Kansas City, MO. Also, experiments were conducted in the lab to validate the real-time biomechanical measures calculated using the skeletal data from the Microsoft Xbox 360 Kinect and Microsoft Xbox One Kinect, with ground truth data from a Vicon motion capture system. Good agreements were found in the validation tests. The results show potential capabilities of the IPTS system to provide remote physical therapy to clients, especially older adults, who may find it difficult to visit the clinic.

Modifying Kinect placement to improve upper limb joint angle measurement accuracy

STUDY DESIGN

Repeated measures.

INTRODUCTION

The Kinect (Microsoft, Redmond, WA) is widely used for telerehabilitation applications including rehabilitation games and assessment.

PURPOSE OF THE STUDY

To determine effects of the Kinect location relative to a person on measurement accuracy of upper limb joint angles.

METHODS

Kinect error was computed as difference in the upper limb joint range of motion (ROM) during target reaching motion, from the Kinect vs 3D Investigator Motion Capture System (NDI, Waterloo, Ontario, Canada), and compared across 9 Kinect locations.

RESULTS

The ROM error was the least when the Kinect was elevated 45° in front of the subject, tilted toward the subject. This error was 54% less than the conventional location in front of a person without elevation and tilting. The ROM error was the largest when the Kinect was located 60° contralateral to the moving arm, at the shoulder height, facing the subject. The ROM error was the least for the shoulder elevation and largest for the wrist angle.

DISCUSSION

Accuracy of the Kinect sensor for detecting upper limb joint ROM depends on its location relative to a person.

CONCLUSION

This information facilitates implementation of Kinect-based upper limb rehabilitation applications with adequate accuracy.

LEVEL OF EVIDENCE

3b.

Emotion recognition using Kinect motion capture data of human gaits

Automatic emotion recognition is of great value in many applications, however, to fully display the application value of emotion recognition, more portable, non-intrusive, inexpensive technologies need to be developed. Human gaits could reflect the walker's emotional state, and could be an information source for emotion recognition. This paper proposed a novel method to recognize emotional state through human gaits by using Microsoft Kinect, a low-cost, portable, camera-based sensor. Fifty-nine participants' gaits under neutral state, induced anger and induced happiness were recorded by two Kinect cameras, and the original data were processed through joint selection, coordinate system transformation, sliding window gauss filtering, differential operation, and data segmentation. Features of gait patterns were extracted from 3-dimentional coordinates of 14 main body joints by Fourier transformation and Principal Component Analysis (PCA). The classifiers NaiveBayes, RandomForests, LibSVM and SMO (Sequential Minimal Optimization) were trained and evaluated, and the accuracy of recognizing anger and happiness from neutral state achieved 80.5% and 75.4%. Although the results of distinguishing angry and happiness states were not ideal in current study, it showed the feasibility of automatically recognizing emotional states from gaits, with the characteristics meeting the application requirements.