Gait disorder rehabilitation using vision and non-vision based sensors: a systematic review

Does patella lowering as part of multilevel surgery improve knee kinematics in children with cerebral palsy and crouch gait? A meta-analysis of comparative studies

Purpose

To evaluate differences in knee kinematic outcomes of patellar-lowering surgery, specifically patellar tendon advancement or patellar tendon shortening, compared with no-patellar-lowering surgery in multilevel surgery for children with cerebral palsy and crouch gait.

Methods

Four databases were searched to retrieve studies published from inception until 2023. Three reviewers independently screened for studies with observational or randomized control designs, comparing two groups of patients with cerebral palsy and crouch gait who underwent multilevel surgery (with patellar-lowering surgery versus no-patellar-lowering surgery), where various gait analysis outcomes were reported (CRD42023450692). The risk of bias was assessed with the Risk Of Bias In Non-randomised Studies - of Interventions (ROBINS-I) tool.

Results

Seven studies (249 patients and 368 limbs) met the eligibility criteria. Patients undergoing patellar-lowering surgery demonstrated statistically significant improvements in knee flexion at initial contact (mean difference = -6.39; 95% confidence interval = [-10.4, -2.75]; p = 0.0006; I2 = 84%), minimum knee flexion in stance (mean difference = -14.27; 95% confidence interval = [-18.31, -10.23]; p < 0.00001; I2 = 89%), and clinical knee flexion contracture (mean difference = -5.6; 95% confidence interval = [-9.59, -1.6]; p = 0.006; I2 = 95%), with a significant increase in anterior pelvic tilt (mean difference = 2.97; 95% confidence interval = [0.58, 5.36]; p = 0.01; I2 = 15%). However, improvements in gait deviation index and decrease in peak knee flexion in swing did not reach statistical significance. Subgroup analysis reduced heterogeneity and revealed (1) greater improvement using patellar tendon shortening versus patellar tendon advancement techniques; (2) lack of knee flexion contracture improvement in high-quality or longer-term studies; (3) longer-term improvement only in minimum knee flexion in stance, with a decrease in peak knee flexion in swing; and (4) an inability to assess the potential benefit of rectus femoris procedure and hamstring preservation.

Conclusions

Overall, the combination of patellar-lowering surgery with multilevel surgery demonstrated superior improvements in stance-phase knee kinematics compared with multilevel surgery alone, despite an increase in anterior pelvic tilt and a longer-term knee flexion reduction during the swing phase.

Level of evidence

Level III, Systematic review of level III studies.

Wearable multisource quantitative gait analysis of Parkinson's diseases

As the motor symptoms of Parkinson's disease (PD) are complex and influenced by many factors, it is challenging to quantify gait abnormalities adequately using a single type of signal. Therefore, a wearable multisource gait monitoring system is developed to perform a quantitative analysis of gait abnormalities for improving the effectiveness of the clinical diagnosis. To detect multisource gait data for an accurate evaluation of gait abnormalities, force sensitive sensors, piezoelectric sensors, and inertial measurement units are integrated into the devised device. The modulation circuits and wireless framework are designed to simultaneously collect plantar pressure, dynamic deformation, and postural angle of the foot and then wirelessly transmit these collected data. With the designed system, multisource gait data from PD patients and healthy controls are collected. Multisource features for quantifying gait abnormalities are extracted and evaluated by a significance test of difference and correlation analysis. The results show that the features extracted from every single type of data are able to quantify the health status of the subjects (p < 0.001, ρ > 0.50). More importantly, the validity of multisource gait data is verified. The results demonstrate that the gait feature fusing multisource data achieves a maximum correlation coefficient of 0.831, a maximum Area Under Curve of 0.9206, and a maximum feature-based classification accuracy of 88.3%. The system proposed in this study can be applied to the gait analysis and objective evaluation of PD.

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.

Gait Recognition Using Optical Motion Capture: A Decision Fusion Based Method

Human identification based on motion capture data has received signification attentions for its wide applications in authentication and surveillance systems. The optical motion capture system (OMCS) can dynamically capture the high-precision three-dimensional locations of optical trackers that are implemented on a human body, but its potential in applications on gait recognition has not been studied in existing works. On the other hand, a typical OMCS can only support one player one time, which limits its capability and efficiency. In this paper, our goals are investigating the performance of OMCS-based gait recognition performance, and realizing gait recognition in OMCS such that it can support multiple players at the same time. We develop a gait recognition method based on decision fusion, and it includes the following four steps: feature extraction, unreliable feature calibration, classification of single motion frame, and decision fusion of multiple motion frame. We use kernel extreme learning machine (KELM) for single motion classification, and in particular we propose a reliability weighted sum (RWS) decision fusion method to combine the fuzzy decisions of the motion frames. We demonstrate the performance of the proposed method by using walking gait data collected from 76 participants, and results show that KELM significantly outperforms support vector machine (SVM) and random forest in the single motion frame classification task, and demonstrate that the proposed RWS decision fusion rule can achieve better fusion accuracy compared with conventional fusion rules. Our results also show that, with 10 motion trackers that are implemented on lower body locations, the proposed method can achieve 100% validation accuracy with less than 50 gait motion frames.

Using a Deep Learning Method and Data from Two-Dimensional (2D) Marker-Less Video-Based Images for Walking Speed Classification

Human body measurement data related to walking can characterize functional movement and thereby become an important tool for health assessment. Single-camera-captured two-dimensional (2D) image sequences of marker-less walking individuals might be a simple approach for estimating human body measurement data which could be used in walking speed-related health assessment. Conventional body measurement data of 2D images are dependent on body-worn garments (used as segmental markers) and are susceptible to changes in the distance between the participant and camera in indoor and outdoor settings. In this study, we propose five ratio-based body measurement data that can be extracted from 2D images and can be used to classify three walking speeds (i.e., slow, normal, and fast) using a deep learning-based bidirectional long short-term memory classification model. The results showed that average classification accuracies of 88.08% and 79.18% could be achieved in indoor and outdoor environments, respectively. Additionally, the proposed ratio-based body measurement data are independent of body-worn garments and not susceptible to changes in the distance between the walking individual and camera. As a simple but efficient technique, the proposed walking speed classification has great potential to be employed in clinics and aged care homes.

Concurrent validity and within-session reliability of gait kinematics measured using an inertial motion capture system with repeated calibration

INTRODUCTION

Wearable inertial measurement units (IMUs) enable gait analysis in the clinic, but require calibrations that may affect subsequent gait measurements. This study assessed concurrent validity and within-session reliability of gait kinematics measured by a frequently calibrated IMU-based system. Calibration pose accuracy and intra-rater repeatability, and IMU orientation tracking accuracy, were additionally quantified.

METHODS

Calibration poses and gait were recorded in 15 women using IMUs and optical motion capture (OMC) (reference standard) simultaneously. Participants performed six consecutive trials: each comprising a calibration pose and a walk. IMU tracking was assessed separately (once-off) using technical static and dynamic tests. Differences of > 5° constituted clinical significance.

RESULTS

Concurrent validity for gait revealed clinically significant between-system differences for sagittal angles (root-mean-square error [RMSE] 6.7°-15.0°; bias -9.3°-3.0°) and hip rotation (RMSE 7.9°; bias -4.2°). After removing modelling offsets, differences for all angles (except hip rotation) were < 5°. Gait curves correlated highly between systems (r > 0.8), except hip rotation, pelvic tilt and -obliquity. Within-session reliability of IMU-measured gait angles was clinically acceptable (standard error of measurement [SEM] < 5°). Calibration poses were repeatable (SEM 0.3°-2.2°). Pose accuracy revealed mean absolute differences (MAD) < 5° for all angles except sagittal ankle, hip and pelvis. IMU tracking accuracy demonstrated RMSE ≤ 2.0°.

CONCLUSION

A frequently calibrated IMU system provides reliable gait measurements; comparing highly to OMC after removing modelling differences. Calibration poses can be implemented accurately for most angles and consistently. IMU-measured gait data are clinically useful and comparable within participants, but should not be compared to OMC-measured data.

Kinect v2 based system for gait assessment of children with cerebral palsy in rehabilitation settings

Cerebral palsy (CP) describes a group of disorders of movement, posture and balance caused by a non-progressive brain injury in the immature brain. It is the most prevalent cause of chronic motor disability in childhood, and although two thirds of CP children are able to walk, they show gait limitations. In this context, rehabilitation therapy can improve muscle balance and gait. Previous studies showed the importance of gait analysis as part of multidisciplinary tools for effective rehabilitation treatment. However, the high cost and the infrastructure required for the implementation of commercial gait analysis systems as well as the time-consuming preparation procedures, limit the access to this service. A low cost, non-restrictive, portable and of simple operation and installation system was developed based on Kinect v2 sensor. This study aims to validate it for capturing and analysing gait parameters in children with cerebral palsy. Several gait parameters from eleven CP patients with different levels of disability classified as a function of the Gross Motor Function Classification System (GMFCS) from II to III were recorded while they walked on a flat surface. The Kinect-based gait analysis system was compared with video-recording that yielded the same results. These results show the potential of Kinect to analyse gait in frail patient populations unobtrusively and with very low cost. More importantly, regarding to spatial parameters, the Kinect system was useful even for the worst case of GMFCS III that show a large gait variability with abnormal patterns.

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

Background

Gait is usually assessed by clinical tests, which may have poor accuracy and be biased, or instrumented systems, which potentially solve these limitations at the cost of being time-consuming and expensive. The different versions of the Microsoft Kinect have enabled human motion tracking without using wearable sensors at a low-cost and with acceptable reliability. This study aims: First, to determine the sensitivity of an open-access Kinect v2-based gait analysis system to motor disability and aging; Second, to determine its concurrent validity with standardized clinical tests in individuals with stroke; Third, to quantify its inter and intra-rater reliability, standard error of measurement, minimal detectable change; And, finally, to investigate its ability to identify fall risk after stroke.

Methods

The most widely used spatiotemporal and kinematic gait parameters of 82 individuals post-stroke and 355 healthy subjects were estimated with the Kinect v2-based system. In addition, participants with stroke were assessed with the Dynamic Gait Index, the 1-min Walking Test, and the 10-m Walking Test.

Results

The system successfully characterized the performance of both groups. Significant concurrent validity with correlations of variable strength was detected between all clinical tests and gait measures. Excellent inter and intra-rater reliability was evidenced for almost all measures. Minimal detectable change was variable, with poorer results for kinematic parameters. Almost all gait parameters proved to identify fall risk.

Conclusions

Results suggest that although its limited sensitivity to kinematic parameters, the Kinect v2-based gait analysis could be used as a low-cost alternative to laboratory-grade systems to complement gait assessment in clinical settings.

Toward Pervasive Gait Analysis With Wearable Sensors: A Systematic Review

After decades of evolution, measuring instruments for quantitative gait analysis have become an important clinical tool for assessing pathologies manifested by gait abnormalities. However, such instruments tend to be expensive and require expert operation and maintenance besides their high cost, thus limiting them to only a small number of specialized centers. Consequently, gait analysis in most clinics today still relies on observation-based assessment. Recent advances in wearable sensors, especially inertial body sensors, have opened up a promising future for gait analysis. Not only can these sensors be more easily adopted in clinical diagnosis and treatment procedures than their current counterparts, but they can also monitor gait continuously outside clinics - hence providing seamless patient analysis from clinics to free-living environments. The purpose of this paper is to provide a systematic review of current techniques for quantitative gait analysis and to propose key metrics for evaluating both existing and emerging methods for qualifying the gait features extracted from wearable sensors. It aims to highlight key advances in this rapidly evolving research field and outline potential future directions for both research and clinical applications.

A Review on Technical and Clinical Impact of Microsoft Kinect on Physical Therapy and Rehabilitation

This paper reviews technical and clinical impact of the Microsoft Kinect in physical therapy and rehabilitation. It covers the studies on patients with neurological disorders including stroke, Parkinson's, cerebral palsy, and MS as well as the elderly patients. Search results in Pubmed and Google scholar reveal increasing interest in using Kinect in medical application. Relevant papers are reviewed and divided into three groups: (1) papers which evaluated Kinect's accuracy and reliability, (2) papers which used Kinect for a rehabilitation system and provided clinical evaluation involving patients, and (3) papers which proposed a Kinect-based system for rehabilitation but fell short of providing clinical validation. At last, to serve as technical comparison to help future rehabilitation design other sensors similar to Kinect are reviewed.