Gait analysis with the Kinect v2: normative study with healthy individuals and comprehensive study of its sensitivity, validity, and reliability in individuals with stroke

A validation study demonstrating portable motion capture cameras accurately characterize gait metrics when compared to a pressure-sensitive walkway

Digital quantification of gait can be used to measure aging- and disease-related decline in mobility. Gait performance also predicts prognosis, disease progression, and response to therapies. Most gait analysis systems require large amounts of space, resources, and expertise to implement and are not widely accessible. Thus, there is a need for a portable system that accurately characterizes gait. Here, depth video from two portable cameras accurately reconstructed gait metrics comparable to those reported by a pressure-sensitive walkway. 392 research participants walked across a four-meter pressure-sensitive walkway while depth video was recorded. Gait speed, cadence, and step and stride durations and lengths strongly correlated (r > 0.9) between modalities, with root-mean-squared-errors (RMSE) of 0.04 m/s, 2.3 steps/min, 0.03 s, and 0.05-0.08 m for speed, cadence, step/stride duration, and step/stride length, respectively. Step, stance, and double support durations (gait cycle percentage) significantly correlated (r > 0.6) between modalities, with 5% RMSE for step and stance and 10% RMSE for double support. In an exploratory analysis, gait speed from both modalities significantly related to healthy, mild, moderate, or severe categorizations of Charleson Comorbidity Indices (ANOVA, Tukey's HSD, p < 0.0125). These findings demonstrate the viability of using depth video to expand access to quantitative gait assessments.

Smartphone videos-driven musculoskeletal multibody dynamics modelling workflow to estimate the lower limb joint contact forces and ground reaction forces

The estimation of joint contact forces in musculoskeletal multibody dynamics models typically requires the use of expensive and time-consuming technologies, such as reflective marker-based motion capture (Mocap) system. In this study, we aim to propose a more accessible and cost-effective solution that utilizes the dual smartphone videos (SPV)-driven musculoskeletal multibody dynamics modeling workflow to estimate the lower limb mechanics. Twelve participants were recruited to collect marker trajectory data, force plate data, and motion videos during walking and running. The smartphone videos were initially analyzed using the OpenCap platform to identify key joint points and anatomical markers. The markers were used as inputs for the musculoskeletal multibody dynamics model to calculate the lower limb joint kinematics, joint contact forces, and ground reaction forces, which were then evaluated by the Mocap-based workflow. The root mean square error (RMSE), mean absolute deviation (MAD), and Pearson correlation coefficient (ρ) were adopted to evaluate the results. Excellent or strong Pearson correlations were observed in most lower limb joint angles (ρ = 0.74 ~ 0.94). The averaged MADs and RMSEs for the joint angles were 1.93 ~ 6.56° and 2.14 ~ 7.08°, respectively. Excellent or strong Pearson correlations were observed in most lower limb joint contact forces and ground reaction forces (ρ = 0.78 ~ 0.92). The averaged MADs and RMSEs for the joint lower limb joint contact forces were 0.18 ~ 1.07 bodyweight (BW) and 0.28 ~ 1.32 BW, respectively. Overall, the proposed smartphone video-driven musculoskeletal multibody dynamics simulation workflow demonstrated reliable accuracy in predicting lower limb mechanics and ground reaction forces, which has the potential to expedite gait dynamics analysis in a clinical setting.

Test-Retest Reliability of Movement Displacement during a 20-s Stepping-in-Place Test in Community-Dwelling Older Adults with and without Supportive Care

INTRODUCTION

Our recent reports have shown that movement pattern and displacement assessed with an infrared depth sensor during a 20-s stepping-in-place test (ST) correlates with measures of balance and need for assistance in older individuals. This study investigated ST test-retest reliability in community-dwelling older adults with and without supportive care.

METHODS

Two groups were included: those not participating (HO: n = 25, 74.7 ± 5.2 years) and those participating (DSU: n = 41, 78.8 ± 5.8 years) in regular senior day services (supportive care). HO completed three ST trials, separated by 1 week, while DSU completed two ST trials during the same half-day supportive care visit. Testing was conducted with eyes open. ST measures included head movement path distance (TMD), maximum movement displacement (MMD), knee movement path length (KMD), and stepping rate (STEP). TMD×KMD-1 ratio indicated upper-body sway relative to lower-body. Test-retest reliability (intra-class correlation coefficients, ICCs) and between-trial and between group differences (ANCOVA, adjusting for age) were assessed.

RESULTS

After adjusting for age, TMD, KMD, TMD×KMD-1 were lesser and STEP was greater in HO than DUS. HO ST variables did not differ across testing days. HO ICC (1, 3) for TMD (0.911 [95% confidence interval: 0.827-0.958]), MMD (0.918 [0.814-0.961]), KMD (0.838 [0.685-0.923]), and TMD×KMD-1 (0.940 [0.884-0.872]) showed strong to very strong reliability. Similarly, DSU ST variables did not differ across same day trials and ICC (1, 2) for TMD, KMD, and TMD×KMD-1 displayed very strong reliability.

CONCLUSION

These ST variables exhibited excellent test-retest reliability of discriminating between community-dwelling older adults with and without supportive care.

Investigating spatiotemporal and kinematic gait parameters in individuals with Parkinson's disease with a history of freezing of gait and exploring the effects of dopaminergic therapy on freezing of gait subtypes

Introduction

Freezing of Gait (FOG) is a prevalent and debilitating symptom in idiopathic Parkinson's disease (PD). This study evaluated spatiotemporal and kinematic gait parameters in individuals with PD with a history of FOG and explored the effects of dopaminergic therapy on FOG subtypes.

Methods

One hundred and nine individuals with PD underwent clinical assessments and quantitative biomechanical measures during walking cycles before and after dopaminergic therapy. Individuals with FOG were classified into levodopa-responsive and levodopa-unresponsive groups.

Results

Individuals with FOG displayed longer disease duration and higher Unified Parkinson's Disease Rating Scale (UPDRS) II, III, IV scores, and total scores and levodopa equivalent dose, than those without FOG (all p < 0.0001). Following propensity score matching of 15 pairs based on UPDRS total score and disease duration during the off-medication state, the analysis comparing the FOG and non-FOG groups revealed no significant differences in spatiotemporal and kinematic parameters. In 39 cases of FOG, dopaminergic therapy improved gait performance in individuals with PD, enhancing spatiotemporal parameters (speed, stride length, step length, step variability) and kinematic parameters (shoulder and elbow flexion/extension range of motion (ROM), pelvic rotation, and hip abduction/adduction ROM) regardless of FOG responsiveness to dopaminergic therapy. A significant difference in trunk sway ROM (p = 0.029) remained before and after dopaminergic therapy, even after adjusting for disease duration and clinical severity.

Discussion

Dopaminergic therapy had varying effects on PD with FOG, improving several spatiotemporal and kinematic gait parameters but being less effective in levodopa-unresponsive cases. Quantitative biomechanical measures offer detailed insights into gait performance, aiding personalized fall risk assessment and guiding individualized rehabilitation programs.

Kinematic synergies show good consistency when extracted with a low-cost markerless device and a marker-based motion tracking system

Recently, markerless tracking systems, such as RGB-Depth cameras, have spread to overcome some of the limitations of the gold standard marker-based tracking systems. Although these systems are valuable substitutes for human motion analysis, as they guarantee higher flexibility, faster setup time and lower costs, their tracking accuracy is lower with respect to marker-based systems. Many studies quantified the error made by markerless systems in terms of body segment length estimation, articular angles, and biomechanics, concluding that they are appropriate for many clinical applications related to motion analysis. We propose an innovative approach to compare a markerless tracking system (Kinect V2) with a gold standard marker-based system (Vicon), based on motor control assessment. We quantified kinematic synergies from the tracking data of fifteen participants performing multi-directional upper limb movements. Kinematic synergy analysis decomposes the kinematic data into a reduced set of motor primitives that describe how the central nervous system coordinates motion at spatial and temporal level. Synergies were extracted with the recently released mixed-matrix factorization algorithm. Four synergies were extracted from both marker-based and markerless datasets and synergies were grouped in 6 clusters for each dataset. Cosine similarity in each cluster was ⩾0.60 in both systems, showing good consistency of synergies. Good matching was found between synergies extracted from markerless and from marker-based data, with a cosine similarity between matched synergies ⩾0.60 in 5 out 6 synergies. These results showed that the markerless sensor can be feasible for kinematic synergy analysis for gross movements, as it correctly estimates the number of synergies and in most cases also their spatial and temporal organization.

The impact of exergames on the functional balance of a teenager with cerebral palsy - a case report

PURPOSE

To understand the impact of an intensive rehabilitation program based on exergames in balance and lower limb function in a teenager with cerebral palsy.

METHODS

The rehabilitation program comprised different customised exergames and was delivered in 5 weekly sessions of 30 min for 4 weeks. Pre-, post-, and 1-month Follow-up assessments included the following metrics: Berg Balance Scale (BBS), Dynamic Gait Index (DGI), Gross Motor Function Measure (GMFM), Posturography, and Gait analysis.

RESULTS

We observed increased scores after the intervention of 9/72 points in GMF - Module E (Walk, Run and Jump) and of 9/56 points in BBS, sustained at Follow-up. Changes in function, specifically in the quality and independence of the performance of specific movements such as turning 360°, increased distance reaching forward, walk behind, step over obstacles, and step stairs up and down were also observed. Gait kinematics and Spatio-temporal parameters tended to get closer to the 50th percentile.

CONCLUSIONS

We observed positive changes in motor function of a teenager with cerebral palsy, with sustained increased scores at a 1-month Follow-up. Findings are suggestive that intensive rehabilitation programs using exergames with high customisation features are a potentially valuable rehabilitation tool for training balance in teenagers with Cerebral Palsy.Implications for rehabilitationExergames may be a useful for providing balance training for teenagers who have a mixed form of cerebral palsy.Exergames that require body displacement may be suitable for modulating gait kinematics and spatio-temporal parameters.The customisation of virtual rehabilitation tools seems to impact the motivation and effort of the user positively.

Reliability and validity of the Spanish adaptation of the Functional Independence Measure + Functional Assessment Measure

BACKGROUND

The Functional Independence Measure + Functional Assessment Measure (FIM+FAM) Scale is one of the most widely used instruments to measure functional independence post-stroke, and features many cultural adaptations to various languages.

AIM

The aim of this study was to determine the psychometric properties of a Spanish cross-cultural adaptation of the FIM+FAM for use in the stroke population.

DESIGN

Observational study.

SETTING

Outpatient long-term service of a neurorehabilitation unit.

POPULATION

One hundred and twenty-two individuals with stroke.

METHODS

The functional independence of the participants was assessed with the adapted version of the FIM+FAM. Additionally, the functional, motor and cognitive condition of the participants was assessed with a battery of standardized clinical instruments. Finally, a group of 31 participants out of the total were evaluated a second time with the FIM+FAM by a different evaluator than the one who performed the first evaluation. Internal consistency, inter-rater reliability and convergent validity with other clinical instruments of the adapted version of the FIM+FAM were determined.

RESULTS

The internal consistency of the adapted version of the FIM+FAM was excellent, as evidenced by Cronbach's α values that exceeded 0.973. The inter-rater reliability was likewise excellent, with correlations above 0.990 in all domains and subscales. Additionally, the convergent validity of the scale adaptation with clinical instruments was variable, with values ranging from 0.264 to 0.983, but consistent with the construct assessed in the different instruments examined.

CONCLUSIONS

The internal consistency, inter-rater reliability and convergent validity of the Spanish-adapted version of the FIM+FAM Scale showed excellent reliability and validity of the adaptation, which supports its use to assess functional independence after stroke.

CLINICAL REHABILITATION IMPACT

Availability of a valid adaptation for the evaluation of functional independence after stroke in Spanish population.

Automated Gait Analysis Based on a Marker-Free Pose Estimation Model

Gait analysis is an essential tool for detecting biomechanical irregularities, designing personalized rehabilitation plans, and enhancing athletic performance. Currently, gait assessment depends on either visual observation, which lacks consistency between raters and requires clinical expertise, or instrumented evaluation, which is costly, invasive, time-consuming, and requires specialized equipment and trained personnel. Markerless gait analysis using 2D pose estimation techniques has emerged as a potential solution, but it still requires significant computational resources and human involvement, making it challenging to use. This research proposes an automated method for temporal gait analysis that employs the MediaPipe Pose, a low-computational-resource pose estimation model. The study validated this approach against the Vicon motion capture system to evaluate its reliability. The findings reveal that this approach demonstrates good (ICC_(2,1) > 0.75) to excellent (ICC_(2,1) > 0.90) agreement in all temporal gait parameters except for double support time (right leg switched to left leg) and swing time (right), which only exhibit a moderate (ICC_(2,1) > 0.50) agreement. Additionally, this approach produces temporal gait parameters with low mean absolute error. It will be useful in monitoring changes in gait and evaluating the effectiveness of interventions such as rehabilitation or training programs in the community.

Gait Analysis in Neurorehabilitation: From Research to Clinical Practice

When brain damage occurs, gait and balance are often impaired. Evaluation of the gait cycle, therefore, has a pivotal role during the rehabilitation path of subjects who suffer from neurological disorders. Gait analysis can be performed through laboratory systems, non-wearable sensors (NWS), and/or wearable sensors (WS). Using these tools, physiotherapists and neurologists have more objective measures of motion function and can plan tailored and specific gait and balance training early to achieve better outcomes and improve patients' quality of life. However, most of these innovative tools are used for research purposes (especially the laboratory systems and NWS), although they deserve more attention in the rehabilitation field, considering their potential in improving clinical practice. In this narrative review, we aimed to summarize the most used gait analysis systems in neurological patients, shedding some light on their clinical value and implications for neurorehabilitation practice.

Spinal alignment measurement with Kinect sensor is valid for thoracic kyphosis but not for lumbar lordosis

BACKGROUND

Spinal alignment evaluation is commonly performed in the clinical setting during rehabilitation. However, there is no simple method for its quantitative measurement. Recently, the depth cameras in Kinect sensors have been employed in various commercial and research projects in the healthcare field. We hypothesized that the time-of-flight technology of the Kinect sensor could be applied to quantitatively evaluate spinal alignment. The purpose of this study was to develop a simple and noninvasive evaluation for spinal alignment using the Kinect sensor and to investigate its validity.

METHODS

Twenty-four healthy men participated in the study. Measurement outcomes were the thoracic kyphosis and lumbar lordosis angles in the standing position, using a Spinal Mouse, the validity of which has been previously reported, and the Kinect sensor. In the measurement by the Kinect sensor, a program was created to obtain the three-dimensional coordinates of each point within an area marked on the monitor, and the sums of the angles at each vertebral level were calculated for the thoracic and lumbar areas. Pearson's correlation coefficient was used to analyze the relationship between the Kinect sensor and Spinal Mouse measurements of thoracic kyphosis and lumbar lordosis angles.

RESULTS

There was a significant positive and moderate correlation between the thoracic kyphosis measurements taken by each device. Contrarily, there was no significant correlation in the lordosis angle between measurements using the Kinect sensor and Spinal Mouse.

CONCLUSIONS

Our results demonstrated the validity of measuring the thoracic kyphosis angle using the Kinect sensor. This indicates that the depth camera in the Kinect sensor is able to perform accurate thoracic alignment measurements quickly and noninvasively.

The development of a home-based technology to improve gait in people with Parkinson's disease: a feasibility study

BACKGROUND

People with Parkinson's disease (PwP) may experience gait impairment and freezing of gait (FOG), a major cause of falls. External cueing, including visual (e.g., spaced lines on the floor) and auditory (e.g., rhythmic metronome beats) stimuli, are considered effective in alleviating mobility deficits and FOG. Currently, there is a need for a technology that delivers automatic, individually adjusted cues in the homes of PwP. The aims of this feasibility study were to describe the first step toward the development of a home-based technology that delivers external cues, test its effect on gait, and assess user experience.

METHODS

Iterative system development was performed by our multidisciplinary team. The system was designed to deliver visual and auditory cues: light stripes projected on the floor and metronome beats, separately. Initial testing was performed using the feedback of five healthy elderly individuals on the cues' clarity (clear visibility of the light stripes and the sound of metronome beats) and discomfort experienced. A pilot study was subsequently conducted in the homes of 15 PwP with daily FOG. We measured participants' walking under three conditions: baseline (with no cues), walking with light stripes, and walking to metronome beats. Outcome measures included step length and step time. User experience was also captured in semi-structured interviews.

RESULTS

Repeated-measures ANOVA of gait assessment in PwP revealed that light stripes significantly improved step length (p = 0.009) and step time (p = 0.019) of PwP. No significant changes were measured in the metronome condition. PwP reported that both cueing modalities improved their gait, confidence, and stability. Most PwP did not report any discomfort in either modality and expressed a desire to have such a technology in their homes. The metronome was preferred by the majority of participants.

CONCLUSIONS

This feasibility study demonstrated the usability and potential effect of a novel cueing technology on gait, and represents an important first step toward the development of a technology aimed to prevent FOG by delivering individually adjusted cues automatically. A further full-scale study is needed. Trial registration This study was registered in ClinicalTrials.gov at 1/2/2022 NCT05211687.

RGB-Depth Camera-Based Assessment of Motor Capacity: Normative Data for Six Standardized Motor Tasks

BACKGROUND

Instrumental motion analysis constitutes a promising development in the assessment of motor function in clinical populations affected by movement disorders. To foster implementation and facilitate interpretation of respective outcomes, we aimed to establish normative data of healthy subjects for a markerless RGB-Depth camera-based motion analysis system and to illustrate their use.

METHODS

We recorded 133 healthy adults (56% female) aged 20 to 60 years with an RGB-Depth camera-based motion analysis system. Forty-three spatiotemporal parameters were extracted from six short, standardized motor tasks-including three gait tasks, stepping in place, standing-up and sitting down, and a postural control task. Associations with confounding factors, height, weight, age, and sex were modelled using a predictive linear regression approach. A z-score normalization approach was provided to improve usability of the data.

RESULTS

We reported descriptive statistics for each spatiotemporal parameter (mean, standard deviation, coefficient of variation, quartiles). Robust confounding associations emerged for step length and step width in comfortable speed gait only. Accessible normative data usage was lastly exemplified with recordings from one randomly selected individual with multiple sclerosis.

CONCLUSION

We provided normative data for an RGB depth camera-based motion analysis system covering broad aspects of motor capacity.

Present and future of gait assessment in clinical practice: Towards the application of novel trends and technologies

Background

Despite being available for more than three decades, quantitative gait analysis remains largely associated with research institutions and not well leveraged in clinical settings. This is mostly due to the high cost/cumbersome equipment and complex protocols and data management/analysis associated with traditional gait labs, as well as the diverse training/experience and preference of clinical teams. Observational gait and qualitative scales continue to be predominantly used in clinics despite evidence of less efficacy of quantifying gait.

Research objective

This study provides a scoping review of the status of clinical gait assessment, including shedding light on common gait pathologies, clinical parameters, indices, and scales. We also highlight novel state-of-the-art gait characterization and analysis approaches and the integration of commercially available wearable tools and technology and AI-driven computational platforms.

Methods

A comprehensive literature search was conducted within PubMed, Web of Science, Medline, and ScienceDirect for all articles published until December 2021 using a set of keywords, including normal and pathological gait, gait parameters, gait assessment, gait analysis, wearable systems, inertial measurement units, accelerometer, gyroscope, magnetometer, insole sensors, electromyography sensors. Original articles that met the selection criteria were included.

Results and significance

Clinical gait analysis remains highly observational and is hence subjective and largely influenced by the observer's background and experience. Quantitative Instrumented gait analysis (IGA) has the capability of providing clinicians with accurate and reliable gait data for diagnosis and monitoring but is limited in clinical applicability mainly due to logistics. Rapidly emerging smart wearable technology, multi-modality, and sensor fusion approaches, as well as AI-driven computational platforms are increasingly commanding greater attention in gait assessment. These tools promise a paradigm shift in the quantification of gait in the clinic and beyond. On the other hand, standardization of clinical protocols and ensuring their feasibility to map the complex features of human gait and represent them meaningfully remain critical challenges.

Usefulness of the Kinect-V2 System for Determining the Global Gait Index to Assess Functional Recovery after Total Knee Arthroplasty

OBJECTIVE

The Korean Knee Society (KKS) score is used for functional evaluation during follow-up after total knee arthroplasty (TKA), but it is time-consuming to measure and is limited by its subjective nature. We investigated whether the global gait asymmetry index (GGA) that can be obtained using the Kinect-V2 system could overcome the KKS limitations.

METHODS

Forty-three patients who underwent TKA from January 2019 to December 2019 were included. Postoperatively, regular follow-up was performed at 2, 4, 6, 8, and 12 weeks, and at 4, 6, and 12 months. At each follow-up visit, the KKS was measured, and the walking path was followed with six Kinect-V2 systems. After allowing the participants to walk naturally, the range of motion of each joint of the lower extremity and GGA were obtained. Changes in the KKS and GGA scores and measurement times were investigated until the final follow-up. A statistical model was made to predict the KKS from the GGA score using data at all observed time points, and analysis of variance (ANOVA) with Turkey's post-hoc tests and Pearson correlation tests were used for evaluation.

RESULTS

Both the KKS and GGA scores improved significantly from 4 weeks postoperatively until the final follow-up. The measurement time was significantly shorter for the GGA (9.3 ± 1.4 min) than for the KKS (32.4 ± 9.2 min; P < 0.001) score. The predicted and actual KKS values clustered close to a straight line on the scatter plot, but the prediction was less accurate in the initial stage (2 weeks post-surgery) than at later time points. The mean absolute error (MAE) and root mean square of the error (RMSE) were considered to be poorly predicted in the initial stage (8 weeks post-surgery) compared to the later time-points (MAE ≥ 5 and RMSE ≥ 6 for 8 weeks post-surgery).

CONCLUSION

In the early phase after knee joint surgery (up to 12 weeks post-surgery), the GGA index does not predict the KKS well. However, after this time point, the GGA index can be simply measured in the outpatient department and may be able to replace the KKS. Thus, evaluation of the GGA index using the Kinect-V2 may be a useful method to evaluate functional recovery in the outpatient clinic after knee joint surgery.

The Use of Portable Devices for the Instrumental Assessment of Balance in Patients with Chronic Stroke: A Systematic Review

BACKGROUND

Improving balance remains a challenge in stroke rehabilitation. The technological development has allowed the design of more accessible and user-friendly systems for assessing postural control.

OBJECTIVES

The aim of this review was to analyze portable devices for the instrumental assessment of balance in patients with chronic stroke.

METHODS

PRISMA guidelines were used to carry out the systematic review. The literature search was restricted to articles written in English or Spanish published from 2013 to December 2022 in Pubmed, Web of Science, Scopus, PEDro, and CINAHL. Of the 309 search results, 229 unique references were reviewed after duplicates were removed. The PEDro scale was applied to evaluate the methodological quality of the included papers, and the degree of evidence and level of recommendation were determined through the Oxford Centre for Evidence-Based Medicine.

RESULTS

A total of seven articles reporting on five different balance testing devices were included in this systematic review; they regarded BIORescue, a smartphone application, and the Biodex-BioSway Balance System for the evaluation of standing balance, and SwayStar Balance and Xsens ForceShoes™ for the evaluation of dynamic balance during walking.

CONCLUSIONS

The use of portable devices that assess balance in adult patients with chronic stroke is scarce.

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.

Do people with low back pain walk differently? A systematic review and meta-analysis

BACKGROUND

The biomechanics of the trunk and lower limbs during walking and running gait are frequently assessed in individuals with low back pain (LBP). Despite substantial research, it is still unclear whether consistent and generalizable changes in walking or running gait occur in association with LBP. The purpose of this systematic review was to identify whether there are differences in biomechanics during walking and running gait in individuals with acute and persistent LBP compared with back-healthy controls.

METHODS

A search was conducted in PubMed, CINAHL, SPORTDiscus, and PsycINFO in June 2019 and was repeated in December 2020. Studies were included if they reported biomechanical characteristics of individuals with and without LBP during steady-state or perturbed walking and running. Biomechanical data included spatiotemporal, kinematic, kinetic, and electromyography variables. The reporting quality and potential for bias of each study was assessed. Data were pooled where possible to compare the standardized mean differences (SMD) between back pain and back-healthy control groups.

RESULTS

Ninety-seven studies were included and reviewed. Two studies investigated acute pain and the rest investigated persistent pain. Nine studies investigated running gait. Of the studies, 20% had high reporting quality/low risk of bias. In comparison with back-healthy controls, individuals with persistent LBP walked slower (SMD = -0.59, 95% confidence interval (95%CI): -0.77 to -0.42)) and with shorter stride length (SMD = -0.38, 95%CI: -0.60 to -0.16). There were no differences in the amplitude of motion in the thoracic or lumbar spine, pelvis, or hips in individuals with LBP. During walking, coordination of motion between the thorax and the lumbar spine/pelvis was significantly more in-phase in the persistent LBP groups (SMD = -0.60, 95%CI: -0.90 to -0.30), and individuals with persistent LBP exhibited greater amplitude of activation in the paraspinal muscles (SMD = 0.52, 95%CI: 0.23-0.80). There were no consistent differences in running biomechanics between groups.

CONCLUSION

There is moderate-to-strong evidence that individuals with persistent LBP demonstrate differences in walking gait compared to back-healthy controls.

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.

Evaluation of lower extremity gait analysis using Kinect V2® tracking system

Introduction: Microsoft Kinect V2^® (Kinect) is a peripheral device of Xbox^® and acquires information such as depth, posture, and skeleton definition. In this study, we investigated whether Kinect can be used for human gait analysis. Methods: Ten healthy volunteers walked 20 trials, and each walk was recorded by a Kinect and infrared- and marker-based-motion capture system. Pearson’s correlation and overall agreement with a method of meta-analysis of Pearson’s correlation coefficient were used to assess the reliability of each parameter, including gait velocity, gait cycle time, step length, hip and knee joint angle, ground contact time of foot, and max ankle velocity. Hip and knee angles in one gait cycle were calculated in Kinect and motion capture groups. Results: The coefficients of correlation for gait velocity (r = 0.92), step length (r = 0.81) were regarded as strong reliability. Gait cycle time (r = 0.65), minimum flexion angle of hip joint (r = 0.68) were regarded as moderate reliability. The maximum flexion angle of the hip joint (r = 0.43) and maximum flexion angle of the knee joint (r = 0.54) were regarded as fair reliability. Minimum flexion angle of knee joint (r = 0.23), ground contact time of foot (r = 0.23), and maximum ankle velocity (r = 0.22) were regarded as poor reliability. The method of meta-analysis revealed that participants with small hip and knee flexion angles tended to have poor correlations in maximum flexion angle of hip and knee joints. Similar trajectories of hip and knee angles were observed in Kinect and motion capture groups. Conclusions: Our results strongly suggest that Kinect could be a reliable device for evaluating gait parameters, including gait velocity, gait cycle time, step length, minimum flexion angle of the hip joint, and maximum flexion angle of the knee joint.

Agreement, Reliability, and Concurrent Validity of an Outdoor, Wearable-Based Walk Ratio Assessment in Healthy Adults and Chronic Stroke Survivors

Purpose: The walk ratio (WR)—the step-length/cadence relation—is a promising measure for gait control. GPS-running watches deliver clinically relevant outcomes including the WR. The aim of this study was to determine test-retest agreement, reliability and concurrent validity of an outdoor WR assessment using a GPS-running watch.

Methods: Healthy adults and moderate—high functioning stroke survivors (≥6 months), performed the 1 km-outdoor walk twice using a GPS-running watch (Garmin Forerunner 35, GFR35) and a Step Activity Monitor (SAM 3). Global cognition was assessed using the Montreal Cognitive Assessment. Test-retest agreement and reliability were assessed using Bland-Altman plots, standard error of measurement (SEM), intraclass correlation coefficients (ICCs) and smallest detectable changes (SDCs). Concurrent validity was determined by the mean difference (MD), standard error (SE), mean absolute percentage errors (MAPEs) and Spearman’s Rho between GFR35 and SAM3. WR values of the two groups were compared by a Welch’s test. A hierarchical multiple regression was performed with the WR as dependent variable and possible predictors as independent variables.

Results: Fifty-one healthy adults [median: 60.0 (47.0, 67.0) years) and 20 stroke survivors [mean: 63.1 (12.4) years, median: 76 (30, 146) months post-stroke] were included. Test-retest agreement and reliability were excellent (SEM% ≤ 2.2, ICCs > 0.9, SDC% ≤ 6.1) and concurrent validity was high (MAPE < 5, ρ > 0.7) for those walking ≥ 1 m/s. Walking < 1 m/s impaired accurate step counting and reduced agreement, reliability, and validity. The WR differed between healthy adults and stroke survivors (t = −2.126, p = 0.045). The hierarchical regression model including stroke and global cognition (Montreal Cognitive Assessment, 0—30) explained 25% of the WR variance (ΔR² = 0.246, p < 0.001). Stroke had no effect (β = −0.05, p = 0.682), but global cognition was a predictor for an altered WR (β = 0.44, p = 0.001).

Discussion: The outdoor WR assessment using the GFR35 showed excellent test-retest agreement, reliability and concurrent validity in healthy adults and chronic stroke survivors walking at least 1 m/s. As the WR seems relevant in chronic stroke, future studies should further investigate this parameter.

Ground reaction force and joint moment estimation during gait using an Azure Kinect-driven musculoskeletal modeling approach

BACKGROUND

Gait analysis is burdened by time and equipment costs, interpretation, and accessibility of three-dimensional motion analysis systems. Evidence suggests growing adoption of gait testing in the shift toward evidence-based medicine. Further developments addressing these barriers will aid its efficacy in clinical practice. Previous research aiming to develop gait analysis systems for kinetics estimation using the Kinect V2 have provided promising results yet modified approaches using the latest hardware may further aid kinetics estimation accuracy RESEARCH QUESTION: Can a single Azure Kinect sensor combined with a musculoskeletal modeling approach provide kinetics estimations during gait similar to those obtained from marker-based systems with embedded force platforms?

METHODS

Ten subjects were recruited to perform three walking trials at their normal speed. Trials were recorded using an eight-camera optoelectronic system with two embedded force plates and a single Azure Kinect sensor. Marker and depth data were both used to drive a musculoskeletal model using the AnyBody Modeling System. Predicted kinetics from the Azure Kinect-driven model, including ground reaction force (GRF) and joint moments, were compared to measured values using root meansquared error (RMSE), normalized RMSE, Pearson correlation, concordance correlation, and statistical parametric mapping RESULTS: High to very high correlations were observed for anteroposterior GRF (ρ = 0.889), vertical GRF (ρ = 0.940), and sagittal hip (ρ = 0.805) and ankle (ρ = 0.876) moments. RMSEs were 1.2 ± 2.2 (%BW), 3.2 ± 5.7 (%BW), 0.7 ± 0.1.3 (%BWH), and 0.6 ± 1.0 (%BWH) SIGNIFICANCE: The proposed approach using the Azure Kinect provided higher accuracy compared to previous studies using the Kinect V2 potentially due to improved foot tracking by the Azure Kinect. Future studies should seek to optimize ground contact parameters and focus on regions of error between predicted and measured kinetics highlighted currently for further improvements in kinetic estimations.

Individuals with a COVID-19 history exhibit asymmetric gait patterns despite full recovery

COVID-19 is a multisystem infectious disease affecting the body systems. Its neurologic complications include -but are not limited to headache, loss of smell, encephalitis, and cerebrovascular accidents. Even though gait analysis is an objective measure of the neuro-motor system and may provide significant information about the pathophysiology of specific diseases, no studies have investigated the gait characteristics in adults after full recovery from COVID-19. This was a cross-sectional, controlled study that included 12 individuals (mean age, 23.0 ± 4.1 years) with mild-to-moderate COVID-19 history (COVD) and 20 sedentary controls (CONT; mean age, 24.0 ± 3.6 years). Gait was evaluated using inertial sensors on a motorized treadmill. Spatial-temporal gait parameters and gait symmetry were calculated by using at least 512 consecutive steps for each participant. The effect-size analyses were utilized to interpret the impact of the results. Spatial-temporal gait characteristics were comparable between the two groups. The COVD group showed more asymmetrical gait patterns than the CONT group in the double support duration symmetry (p = 0.042), single support duration symmetry (p = 0.006), loading response duration symmetry (p = 0.042), and pre-swing duration symmetry (p = 0.018). The effect size analyses of the differences showed large effects (d = 0.68-0.831). Individuals with a history of mild-to-moderate COVID-19 showed more asymmetrical gait patterns than individuals without a disease history. Regardless of its severity, the multifaceted long-term effects of COVID-19 need to be examined and the scope of clinical follow-up should be detailed.

Clinical Disease Severity Mediates the Relationship between Stride Length and Speed and the Risk of Falling in Parkinson’s Disease

The shuffling gait with slowed speed and reduced stride length has been considered classic clinical features in idiopathic Parkinson’s disease (PD), and the risk of falling increases as the disease progresses. This raises the possibility that clinical disease severity might mediate the relationship between stride length and speed and the risk of falling in patients with PD. Sixty-one patients with PD patients underwent the clinical scores as well as quantitative biomechanical measures during walking cycles before and after dopamine replacement therapy. Mediation analysis tests whether the direct effect of an independent variable (stride length and speed) on a dependent variable (three-step fall prediction model score) can be explained by the indirect influence of the mediating variable (Unified Parkinson’s Disease Rating Scale (UPDRS) total scores). The results demonstrate that decreased stride length, straight walking speed, and turning speed is associated with increased three-step fall prediction model score (r = −0.583, p < 0.0001, r = −0.519, p < 0.0001, and r = −0.462, p < 0.0001, respectively). We further discovered that UPDRS total scores value is negatively correlated with stride length, straight walking, and turning speed (r = −0.651, p < 0.0001, r = −0.555, p < 0.0001, and r = −0.372, p = 0.005, respectively) but positively correlated with the fall prediction model score value (r = 0.527, p < 0.0001). Further mediation analysis shows that the UPDRS total score values serve as mediators between lower stride length, straight walking, and turning speed and higher fall prediction model score values. Our results highlighted the relationship among stride length and speed, clinical disease severity, and risk of falling. As decreased stride length and speed are hallmarks of falls, monitoring the changes of quantitative biomechanical measures along with the use of wearable technology in a longitudinal study can provide a scientific basis for pharmacology, rehabilitation programs, and selecting high-risk candidates for surgical treatment to reduce future fall risk.

Experimental Verification of Micro-Doppler Radar Measurements of Fall-Risk-Related Gait Differences for Community-Dwelling Elderly Adults

In a previous study, we developed a classification model to detect fall risk for elderly adults with a history of falls (fallers) using micro-Doppler radar (MDR) gait measurements via simulation. The objective was to create daily monitoring systems that can identify elderly people with a high risk of falls. This study aimed to verify the effectiveness of our model by collecting actual MDR data from community-dwelling elderly people. First, MDR gait measurements were performed in a community setting, and the efficient gait parameters for the classification of fallers were extracted. Then, a support vector machine model that was trained and validated using the simulated MDR data was tested for the gait parameters extracted from the actual MDR data. A classification accuracy of 78.8% was achieved for the actual MDR data. The validity of the experimental results was confirmed based on a comparison with the results of our previous simulation study. Thus, the practicality of the faller classification model constructed using the simulated MDR data was verified for the actual MDR data.

Computation of Gait Parameters in Post Stroke and Parkinson's Disease: A Comparative Study Using RGB-D Sensors and Optoelectronic Systems

The accurate and reliable assessment of gait parameters is assuming an important role, especially in the perspective of designing new therapeutic and rehabilitation strategies for the remote follow-up of people affected by disabling neurological diseases, including Parkinson’s disease and post-stroke injuries, in particular considering how gait represents a fundamental motor activity for the autonomy, domestic or otherwise, and the health of neurological patients. To this end, the study presents an easy-to-use and non-invasive solution, based on a single RGB-D sensor, to estimate specific features of gait patterns on a reduced walking path compatible with the available spaces in domestic settings. Traditional spatio-temporal parameters and features linked to dynamic instability during walking are estimated on a cohort of ten parkinsonian and eleven post-stroke subjects using a custom-written software that works on the result of a body-tracking algorithm. Then, they are compared with the “gold standard” 3D instrumented gait analysis system. The statistical analysis confirms no statistical difference between the two systems. Data also indicate that the RGB-D system is able to estimate features of gait patterns in pathological individuals and differences between them in line with other studies. Although they are preliminary, the results suggest that this solution could be clinically helpful in evolutionary disease monitoring, especially in domestic and unsupervised environments where traditional gait analysis is not usable.

Comparative Study of Markerless Vision-Based Gait Analyses for Person Re-Identification

The model-based gait analysis of kinematic characteristics of the human body has been used to identify individuals. To extract gait features, spatiotemporal changes of anatomical landmarks of the human body in 3D were preferable. Without special lab settings, 2D images were easily acquired by monocular video cameras in real-world settings. The 2D and 3D locations of key joint positions were estimated by the 2D and 3D pose estimators. Then, the 3D joint positions can be estimated from the 2D image sequences in human gait. Yet, it has been challenging to have the exact gait features of a person due to viewpoint variance and occlusion of body parts in the 2D images. In the study, we conducted a comparative study of two different approaches: feature-based and spatiotemporal-based viewpoint invariant person re-identification using gait patterns. The first method is to use gait features extracted from time-series 3D joint positions to identify an individual. The second method uses a neural network, a Siamese Long Short Term Memory (LSTM) network with the 3D spatiotemporal changes of key joint positions in a gait cycle to classify an individual without extracting gait features. To validate and compare these two methods, we conducted experiments with two open datasets of the MARS and CASIA-A datasets. The results show that the Siamese LSTM outperforms the gait feature-based approaches on the MARS dataset by 20% and 55% on the CASIA-A dataset. The results show that feature-based gait analysis using 2D and 3D pose estimators is premature. As a future study, we suggest developing large-scale human gait datasets and designing accurate 2D and 3D joint position estimators specifically for gait patterns. We expect that the current comparative study and the future work could contribute to rehabilitation study, forensic gait analysis and early detection of neurological disorders.

Gait changes over time in hospitalized older adults with advanced dementia: Predictors of mobility change

People with dementia are at risk of mobility decline. In this study, we measured changes in quantitative gait measures over a maximum 10-week period during the course of a psychogeriatric admission in older adults with dementia, with the aims to describe mobility changes over the duration of the admission, and to determine which factors were associated with this change. Fifty-four individuals admitted to a specialized dementia inpatient unit participated in this study. A vision-based markerless motion capture system was used to record participants' natural gait. Mixed effect models were developed with gait measures as the dependent variables and clinical and demographic variables as predictors. We found that gait stability, step time, and step length decreased, and step time variability and step length variability increased over 10 weeks. Gait stability of men decreased more than that of women, associated with an increased sacrum mediolateral range of motion over time. In addition, the sacrum mediolateral range of motion decreased in those with mild neuropsychiatric symptoms over 10 weeks, but increased in those with more severe neuropsychiatric symptoms. Our study provides evidence of worsening of gait mechanics and control over the course of a hospitalization in older adults with dementia. Quantitative gait monitoring in hospital environments may provide opportunities to intervene to prevent adverse events, decelerate mobility decline, and monitor rehabilitation outcomes.

A Review on the Use of Microsoft Kinect for Gait Abnormality and Postural Disorder Assessment

Gait and posture studies have gained much prominence among researchers and have attracted the interest of clinicians. The ability to detect gait abnormality and posture disorder plays a crucial role in the diagnosis and treatment of some diseases. Microsoft Kinect is presented as a noninvasive sensor essential for medical diagnostic and therapeutic purposes. There are currently no relevant studies that attempt to summarise the existing literature on gait and posture abnormalities using Kinect technology. The purpose of this study is to critically evaluate the existing research on gait and posture abnormalities using the Kinect sensor as the main diagnostic tool. Our studies search identified 458 for gait abnormality, 283 for posture disorder of which 26 studies were included for gait abnormality, and 13 for posture. The results indicate that Kinect sensor is a useful tool for the assessment of kinematic features. In conclusion, Microsoft Kinect sensor is presented as a useful tool for gait abnormality, postural disorder analysis, and physiotherapy. It can also help track the progress of patients who are undergoing rehabilitation.

Assessment of upper limb abnormalities using the Kinect: Reliability, validity and detection accuracy in people living with acquired brain injury

Upper limb kinematic abnormalities are prevalent in people with acquired brain injury (ABI). We examined if the Microsoft Kinect for Xbox One (Kinect) reliably (test-retest) and validly (concurrent) quantifies upper limb kinematics, and accurately classifies abnormalities (sensitivity/specificity), in an ABI cohort when compared to three-dimensional motion analysis (3DMA) and a subjective rating scale. We compared 42 adults with ABI to 36 healthy control (HC) participants. Walking trials were recorded by 3DMA and Kinect at self-selected (SSWS) and fast (FWS) walking speeds. When classifying abnormalities for 3DMA and Kinect, a 95% reference range (based on HC data) was calculated using the Kinematic Deviation Score worst axis (KDSw); values outside of this range were classified abnormal. Scores ≥ 2 in the subjective rating scale, based on International Classification of Functioning, Disability and Health Framework's Qualifiers Scale, were considered abnormal. Test-retest reliability and concurrent validity were determined using intra-class correlation coefficient (Absolute ICC_2,1) and Pearson's or Spearman's correlation respectively. Fisher's Exact Test was conducted to determine sensitivity and specificity between each combination of the two methods. Strong test-retest reliability was observed for 3DMA (median(IQR) ICC:0.86(0.85-0.90)). Kinect showed overall strong SSWS test-retest reliability (ICC:0.87(0.84-0.91)) and moderate FWS test-retest reliability (ICC:0.61(0.56-0.65)). Concurrent validity between 3DMA and Kinect was overall moderate. Sensitivity and specificity between 3DMA, Kinect and subjective scores were overall modest. Our results suggest caution should be used if implementing Kinect as its validity is modest against criterion-reference 3DMA; however, given its reliability and similar sensitivity/specificity to 3DMA further responsiveness research is warranted.

Concurrent validity of human pose tracking in video for measuring gait parameters in older adults: a preliminary analysis with multiple trackers, viewing angles, and walking directions

Background

Many of the available gait monitoring technologies are expensive, require specialized expertise, are time consuming to use, and are not widely available for clinical use. The advent of video-based pose tracking provides an opportunity for inexpensive automated analysis of human walking in older adults using video cameras. However, there is a need to validate gait parameters calculated by these algorithms against gold standard methods for measuring human gait data in this population.

Methods

We compared quantitative gait variables of 11 older adults (mean age = 85.2) calculated from video recordings using three pose trackers (AlphaPose, OpenPose, Detectron) to those calculated from a 3D motion capture system. We performed comparisons for videos captured by two cameras at two different viewing angles, and viewed from the front or back. We also analyzed the data when including gait variables of individual steps of each participant or each participant’s averaged gait variables.

Results

Our findings revealed that, i) temporal (cadence and step time), but not spatial and variability gait measures (step width, estimated margin of stability, coefficient of variation of step time and width), calculated from the video pose tracking algorithms correlate significantly to that of motion capture system, and ii) there are minimal differences between the two camera heights, and walks viewed from the front or back in terms of correlation of gait variables, and iii) gait variables extracted from AlphaPose and Detectron had the highest agreement while OpenPose had the lowest agreement.

Conclusions

There are important opportunities to evaluate models capable of 3D pose estimation in video data, improve the training of pose-tracking algorithms for older adult and clinical populations, and develop video-based 3D pose trackers specifically optimized for quantitative gait measurement.

Monitoring of Gait Parameters in Post-Stroke Individuals: A Feasibility Study Using RGB-D Sensors

Stroke is one of the most significant causes of permanent functional impairment and severe motor disability. Hemiplegia or hemiparesis are common consequences of the acute event, which negatively impacts daily life and requires continuous rehabilitation treatments to favor partial or complete recovery and, consequently, to regain autonomy, independence, and safety in daily activities. Gait impairments are frequent in stroke survivors. The accurate assessment of gait anomalies is therefore crucial and a major focus of neurorehabilitation programs to prevent falls or injuries. This study aims to estimate, using a single RGB-D sensor, gait patterns and parameters on a short walkway. This solution may be suitable for monitoring the improvement or worsening of gait disorders, including in domestic and unsupervised scenarios. For this purpose, some of the most relevant spatiotemporal parameters, estimated by the proposed solution on a cohort of post-stroke individuals, were compared with those estimated by a gold standard system for a simultaneous instrumented 3D gait analysis. Preliminary results indicate good agreement, accuracy, and correlation between the gait parameters estimated by the two systems. This suggests that the proposed solution may be employed as an intermediate tool for gait analysis in environments where gold standard systems are impractical, such as home and ecological settings in real-life contexts.

Assessment Methods of Post-stroke Gait: A Scoping Review of Technology-Driven Approaches to Gait Characterization and Analysis

Background: Gait dysfunction or impairment is considered one of the most common and devastating physiological consequences of stroke, and achieving optimal gait is a key goal for stroke victims with gait disability along with their clinical teams. Many researchers have explored post stroke gait, including assessment tools and techniques, key gait parameters and significance on functional recovery, as well as data mining, modeling and analyses methods.

Research Question: This study aimed to review and summarize research efforts applicable to quantification and analyses of post-stroke gait with focus on recent technology-driven gait characterization and analysis approaches, including the integration of smart low cost wearables and Artificial Intelligence (AI), as well as feasibility and potential value in clinical settings.

Methods: A comprehensive literature search was conducted within Google Scholar, PubMed, and ScienceDirect using a set of keywords, including lower extremity, walking, post-stroke, and kinematics. Original articles that met the selection criteria were included.

Results and Significance: This scoping review aimed to shed light on tools and technologies employed in post stroke gait assessment toward bridging the existing gap between the research and clinical communities. Conventional qualitative gait analysis, typically used in clinics is mainly based on observational gait and is hence subjective and largely impacted by the observer's experience. Quantitative gait analysis, however, provides measured parameters, with good accuracy and repeatability for the diagnosis and comparative assessment throughout rehabilitation. Rapidly emerging smart wearable technology and AI, including Machine Learning, Support Vector Machine, and Neural Network approaches, are increasingly commanding greater attention in gait research. Although their use in clinical settings are not yet well leveraged, these tools promise a paradigm shift in stroke gait quantification, as they provide means for acquiring, storing and analyzing multifactorial complex gait data, while capturing its non-linear dynamic variability and offering the invaluable benefits of predictive analytics.

Validity, reliability, and sensitivity to motor impairment severity of a multi-touch app designed to assess hand mobility, coordination, and function after stroke

BACKGROUND

The assessment of upper-limb motor impairments after stroke is usually performed using clinical scales and tests, which may lack accuracy and specificity and be biased. Although some instruments exist that are capable of evaluating hand functions and grasping during functional tasks, hand mobility and dexterity are generally either not specifically considered during clinical assessments or these examinations lack accuracy. This study aimed to determine the convergent validity, reliability, and sensitivity to impairment severity after a stroke of a dedicated, multi-touch app, named the Hand Assessment Test.

METHODS

The hand mobility, coordination, and function of 88 individuals with stroke were assessed using the app, and their upper-limb functions were assessed using the Fugl-Meyer Assessment for Upper Extremity, the Jebsen-Taylor Hand Function Test, the Box and Block Test, and the Nine Hole Peg Test. Twenty-three participants were further considered to investigate inter- and intra-rater reliability, standard error of measurement, and the minimal detectable change threshold of the app. Finally, participants were categorized according to motor impairment severity and the sensitivity of the app relative to these classifications was investigated.

RESULTS

Significant correlations, of variable strengths, were found between the measurements performed by the app and the clinical scales and tests. Variable reliability, ranging from moderate to excellent, was found for all app measurements. Exercises that involved tapping and maximum finger-pincer grasp were sensitive to motor impairment severity.

CONCLUSIONS

The convergent validity, reliability, and sensitivity to motor impairment severity of the app, especially of those exercises that involved tapping and the maximum extension of the fingers, together with the widespread availability of the app, could support the use of this and similar apps to complement conventional clinical assessments of hand function after stroke.

Kinect V2-Based Gait Analysis for Children with Cerebral Palsy: Validity and Reliability of Spatial Margin of Stability and Spatiotemporal Variables

Children with cerebral palsy (CP) have high risks of falling. It is necessary to evaluate gait stability for children with CP. In comparison to traditional motion capture techniques, the Kinect has the potential to be utilised as a cost-effective gait stability assessment tool, ensuring frequent and uninterrupted gait monitoring. To evaluate the validity and reliability of this measurement, in this study, ten children with CP performed two testing sessions, of which gait data were recorded by a Kinect V2 sensor and a referential Motion Analysis system. The margin of stability (MOS) and gait spatiotemporal metrics were examined. For the spatiotemporal parameters, intraclass correlation coefficient (ICC_2,k) values were from 0.83 to 0.99 between two devices and from 0.78 to 0.88 between two testing sessions. For the MOS outcomes, ICC_2,k values ranged from 0.42 to 0.99 between two devices and 0.28 to 0.69 between two test sessions. The Kinect V2 was able to provide valid and reliable spatiotemporal gait parameters, and it could also offer accurate outcome measures for the minimum MOS. The reliability of the Kinect V2 when assessing time-specific MOS variables was limited. The Kinect V2 shows the potential to be used as a cost-effective tool for CP gait stability assessment.

Automatic Ankle Angle Detection by Integrated RGB and Depth Camera System

Depth cameras are developing widely. One of their main virtues is that, based on their data and by applying machine learning algorithms and techniques, it is possible to perform body tracking and make an accurate three-dimensional representation of body movement. Specifically, this paper will use the Kinect v2 device, which incorporates a random forest algorithm for 25 joints detection in the human body. However, although Kinect v2 is a powerful tool, there are circumstances in which the device's design does not allow the extraction of such data or the accuracy of the data is low, as is usually the case with foot position. We propose a method of acquiring this data in circumstances where the Kinect v2 device does not recognize the body when only the lower limbs are visible, improving the ankle angle's precision employing projection lines. Using a region-based convolutional neural network (Mask RCNN) for body recognition, raw data extraction for automatic ankle angle measurement has been achieved. All angles have been evaluated by inertial measurement units (IMUs) as gold standard. For the six tests carried out at different fixed distances between 0.5 and 4 m to the Kinect, we have obtained (mean ± SD) a Pearson's coefficient, r = 0.89 ± 0.04, a Spearman's coefficient, ρ = 0.83 ± 0.09, a root mean square error, RMSE = 10.7 ± 2.6 deg and a mean absolute error, MAE = 7.5 ± 1.8 deg. For the walking test, or variable distance test, we have obtained a Pearson's coefficient, r = 0.74, a Spearman's coefficient, ρ = 0.72, an RMSE = 6.4 deg and an MAE = 4.7 deg.

Doppler Radar for the Extraction of Biomechanical Parameters in Gait Analysis

The applicability of Doppler radar for gait analysis is investigated by quantitatively comparing the measured biomechanical parameters to those obtained using motion capturing and ground reaction forces. Nineteen individuals walked on a treadmill at two different speeds, where a radar system was positioned in front of or behind the subject. The right knee angle was confined by an adjustable orthosis in five different degrees. Eleven gait parameters are extracted from radar micro-Doppler signatures. Here, new methods for obtaining the velocities of individual lower limb joints are proposed. Further, a new method to extract individual leg flight times from radar data is introduced. Based on radar data, five spatiotemporal parameters related to rhythm and pace could reliably be extracted. Further, for most of the considered conditions, three kinematic parameters could accurately be measured. The radar-based stance and flight time measurements rely on the correct detection of the time instant of maximal knee velocity during the gait cycle. This time instant is reliably detected when the radar has a back view, but is underestimated when the radar is positioned in front of the subject. The results validate the applicability of Doppler radar to accurately measure a variety of medically relevant gait parameters. Radar has the potential to unobtrusively diagnose changes in gait, e.g., to design training in prevention and rehabilitation. As contact-less and privacy-preserving sensor, radar presents a viable technology to supplement existing gait analysis tools for long-term in-home examinations.

Validity and sensitivity of instrumented postural and gait assessment using low-cost devices in Parkinson's disease

BACKGROUND

Accurate assessment of balance and gait is necessary to monitor the clinical progress of Parkinson's disease (PD). Conventional clinical scales can be biased and have limited accuracy. Novel interactive devices are potentially useful to detect subtle posture or gait-related impairments.

METHODS

Posturographic and single and dual-task gait assessments were performed to 54 individuals with PD and 43 healthy controls with the Wii Balance Board and the Kinect v2 and the, respectively. Individuals with PD were also assessed with the Tinetti Performance Oriented Mobility Assessment, the Functional Gait Assessment and the 10-m Walking Test. The influence of demographic and clinical variables on the performance in the instrumented posturographic and gait tests, the sensitivity of these tests to the clinical condition and phenotypes, and their convergent validity with clinical scales were investigated.

RESULTS

Individuals with PD in H&Y I and I.5 stages showed similar performance to controls. The greatest differences in posture and gait were found between subjects in H&Y II.5 and H&Y I-I.5 stage, as well as controls. Dual-tasking enhanced the differences among all groups in gait parameters. Akinetic/rigid phenotype showed worse postural control and gait than other phenotypes. High significant correlations were found between the limits of stability and most of gait parameters with the clinical scales.

CONCLUSIONS

Low-cost devices showed potential to objectively quantify posture and gait in established PD (H&Y ≥ II). Dual-tasking gait evaluation was more sensitive to detect differences among PD stages and compared to controls than free gait. Gait and posture were more impaired in akinetic/rigid PD.

Concurrent Validity, Test-Retest Reliability, and Sensitivity to Change of a Single Body-Fixed Sensor for Gait Analysis during Rollator-Assisted Walking in Acute Geriatric Patients

Body-fixed sensor (BFS) technology offers portable, low-cost and easy-to-use alternatives to laboratory-bound equipment for analyzing an individual's gait. Psychometric properties of single BFS systems for gait analysis in older adults who require a rollator for walking are, however, unknown. The study's aim was to evaluate the concurrent validity, test-retest-reliability, and sensitivity to change of a BFS (DynaPort MoveTest; McRoberts B.V., The Hague, The Netherlands) for measuring gait parameters during rollator-assisted walking. Fifty-eight acutely hospitalized older patients equipped with the BFS at the lower back completed a 10 m walkway using a rollator. Concurrent validity was assessed against the Mobility Lab (APDM Inc.; Portland, OR, USA), test-retest reliability over two trials within a 15 min period, and sensitivity to change in patients with improved, stable and worsened 4 m usual gait speed over hospital stay. Bland-Altman plots and intraclass correlation coefficients (ICC) for gait speed, cadence, step length, step time, and walk ratio indicate good to excellent agreement between the BFS and the Mobility Lab (ICC_2,1 = 0.87-0.99) and the repeated trials (ICC_2,1 = 0.83-0.92). Moderate to large standardized response means were observed in improved (gait speed, cadence, step length, walk ratio: 0.62-0.99) and worsened patients (gait speed, cadence, step time: -0.52 to -0.85), while those in stable patients were trivial to small (all gait parameters: -0.04-0.40). The BFS appears to be a valid, reliable and sensitive instrument for measuring spatio-temporal gait parameters during rollator-assisted walking in geriatric patients.

Test-Retest, Inter-Rater and Intra-Rater Reliability for Spatiotemporal Gait Parameters Using SANE (an eaSy gAit aNalysis systEm) as Measuring Instrument

Studies have demonstrated the validity of Kinect-based systems to measure spatiotemporal parameters of gait. However, few studies have addressed test-retest, inter-rater and intra-rater reliability for spatiotemporal gait parameters. This study aims to assess test-retest, inter-rater and intra-rater reliability of SANE (eaSy gAit aNalysis system) as a measuring instrument for spatiotemporal gait parameters. SANE comprises a depth sensor and a software that automatically estimates spatiotemporal gait parameters using distances between ankles without the need to manually indicate where each gait cycle begins and ends. Gait analysis was conducted by 2 evaluators for 12 healthy subjects during 4 sessions. The reliability was evaluated using Intraclass Correlation Coefficients (ICC). In addition, the Standard Error of the Measurement (SEM), and Smallest Detectable Change (SDC) was calculated. SANE showed from an acceptable to an excellent test-retest, inter-rater and intra-rater reliability; test-retest reliability ranged from 0.62 to 0.81, inter-rater reliability ranged from 0.70 to 0.95 and intra-rater ranged from 0.74 to 0.92. The subject behavior had a greater effect on the reliability of SANE than the evaluator performance. The reliability values of SANE were comparable with other similar studies. SANE, as a feasible and markerless system, has large potential for assessing spatiotemporal gait parameters.

Analysis of Upper-Limb and Trunk Kinematic Variability: Accuracy and Reliability of an RGB-D Sensor

In the field of motion analysis, the gold standard devices are marker-based tracking systems. Despite being very accurate, their cost, stringent working environments, and long preparation time make them unsuitable for small clinics as well as for other scenarios such as industrial application. Since human-centered approaches have been promoted even outside clinical environments, the need for easy-to-use solutions to track human motion is topical. In this context, cost-effective devices, such as RGB-Depth (RBG-D) cameras have been proposed, aiming at a user-centered evaluation in rehabilitation or of workers in industry environment. In this paper, we aimed at comparing marker-based systems and RGB-D cameras for tracking human motion. We used a Vicon system (Vicon Motion Systems, Oxford, UK) as a gold standard for the analysis of accuracy and reliability of the Kinect V2 (Microsoft, Redmond, WA, USA) in a variety of gestures in the upper limb workspace—targeting rehabilitation and working applications. The comparison was performed on a group of 15 adult healthy subjects. Each subject had to perform two types of upper-limb movements (point-to-point and exploration) in three workspace sectors (central, right, and left) that might be explored in rehabilitation and industrial working scenarios. The protocol was conceived to test a wide range of the field of view of the RGB-D device. Our results, detailed in the paper, suggest that RGB-D sensors are adequate to track the upper limb for biomechanical assessments, even though relevant limitations can be found in the assessment and reliability of some specific degrees of freedom and gestures with respect to marker-based systems.

People Walking Classification Using Automotive Radar

Automotive radars are able to guarantee high performances at the expenses of a relatively low cost, and recently their application has been extended to several fields in addition to the original one. In this paper we consider the use of this kind of radars to discriminate different types of people’s movements in a real context. To this end, we exploit two different maps obtained from radar, that is, a spectrogram and a range-Doppler map. Through the application of dimensionality reduction methods, such as principal component analysis (PCA) and t-distributed stochastic neighbor embedding (t-SNE) algorithm, and the use of machine learning techniques we prove that is possible to classify with a very good precision people’s way of walking even employing commercial devices specifically designed for other purposes.