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

SAGA: Stability-Aware Gait Analysis in constraint-free environments

BACKGROUND

Gait abnormality detection is a challenging task in clinical practice. The majority of the current frameworks for gait abnormality detection involve the individual processes of segmentation, feature estimation, feature learning, and similarity assessment. Since each component of these modules is fixed and they are mutually independent, their performance under difficult circumstances is not ideal. We combine those processes into a single framework, a gait abnormality detection system with an end-to-end network.

METHODS

It is made up of convolutional neural networks and Deep-Q-learning methods: one for coordinate estimation and the other for classification. In a single joint learning technique that may be trained together, the two networks are modeled. This method is significantly more efficient for use in real life since it drastically simplifies the conventional step-by-step approach.

RESULTS

The proposed model is experimented on MATLAB R2020a. While considering into consideration the stability factor, our proposed model attained an average case accuracy of 95.3%, a sensitivity of 96.4%, and a specificity of 94.1%.

SIGNIFICANCE

Our paradigm for quantifying gait analysis using commodity equipment will improve access to quantitative gait analysis in medical facilities and rehabilitation centers while also allowing academics to conduct large-scale investigations for gait-related disorders. Numerous experimental findings demonstrate the effectiveness of the proposed strategy and its ability to provide cutting-edge outcomes.

Validity of the estimated angular information obtained using an inertial motion capture system during standing trunk forward and backward bending

BACKGROUND

Bending the trunk forward and backward while standing are common daily activities and can have various patterns. However, any dysfunction in these movements can considerably affect daily living activities. Consequently, a comprehensive evaluation of spinal motion during these activities and precise identification of any movement abnormalities are important to facilitate an effective rehabilitation. In recent years, with the development of measurement technology, the evaluation of movement patterns using an inertial motion capture system (motion sensor) has become easy. However, the accuracy of estimated angular information obtained via motion sensor measurements can be affected by angular velocity. This study aimed to compare the validity of estimated angular information obtained by assessing standing trunk forward and backward bending at different movement speeds using a motion sensor with a three-dimensional motion analysis system.

METHODS

The current study included 12 healthy older men. A three-dimensional motion analysis system and a motion sensor were used for measurement. The participants performed standing trunk forward and backward bending at comfortable and maximum speeds, and five sensors were attached to their spine. Statistical analysis was performed using the paired t-test, intraclass correlation coefficient, mean absolute error, and multiple correlation coefficient.

RESULTS

Results showed that the estimated angular information obtained using each motion sensor was not affected by angular velocity and had a high validity.

CONCLUSIONS

Therefore, the angular velocity in this study can be applied clinically for an objective evaluation in rehabilitation.

Gait abnormalities in older adults with transthyretin cardiac amyloidosis

BACKGROUND

Transthyretin cardiac amyloidosis (ATTR cardiac amyloidosis) is caused by variant (ATTRv) or wild type (ATTRwt) transthyretin. While gait abnormalities have been studied in younger patients with ATTRv amyloidosis, research on gait in older adults with ATTR cardiac amyloidosis is lacking. Given ATTR cardiac amyloidosis' association with neuropathy and orthopedic manifestations, we explore the gait in this population.

METHODS

Twenty-eight older male ATTR cardiac amyloidosis patients and 11 healthy older male controls walked overground with and without a dual cognitive task. Gait parameters: stride width, length, velocity and stance time percentage were measured using an instrumented mat. ATTR amyloidosis patients were further categorized based on clinical and functional assessments.

RESULTS

We found significant gait differences between ATTR cardiac amyloidosis patients and healthy controls; patients had more variable, slower, narrower and shorter strides, with their feet spending more time in contact with the ground as opposed to in swing. However, the observed gait differences did not correlate with clinical and functional measures of ATTR cardiac amyloidosis severity.

CONCLUSIONS

Our results suggest that gait analysis could be a complementary tool for characterizing ATTR cardiac amyloidosis patients and may inform clinical care as it relates to falls, management of anticoagulation, and functional independence.

Methods for Spatiotemporal Analysis of Human Gait Based on Data from Depth Sensors

Gait analysis may serve various purposes related to health care, such as the estimation of elderly people's risk of falling. This paper is devoted to gait analysis based on data from depth sensors which are suitable for use both at healthcare facilities and in monitoring systems dedicated to household environments. This paper is focused on the comparison of three methods for spatiotemporal gait analysis based on data from depth sensors, involving the analysis of the movement trajectories of the knees, feet, and centre of mass. The accuracy of the results obtained using those methods was assessed for different depth sensors' viewing angles and different types of subject clothing. Data were collected using a Kinect v2 device. Five people took part in the experiments. Data from a Zebris FDM platform were used as a reference. The obtained results indicate that the viewing angle and the subject's clothing affect the uncertainty of the estimates of spatiotemporal gait parameters, and that the method based on the trajectories of the feet yields the most information, while the method based on the trajectory of the centre of mass is the most robust.

A kinematic-geometric model based on ankles' depth trajectory in frontal plane for gait analysis using a single RGB-D camera

The emergence of RGB-D cameras and the development of pose estimation algorithms offer opportunities in biomechanics. However, some challenges still remain when using them for gait analysis, including noise which leads to misidentification of gait events and inaccuracy. Therefore, we present a novel kinematic-geometric model for spatio-temporal gait analysis, based on ankles' trajectory in the frontal plane and distance-to-camera data (depth). Our approach consists of three main steps: identification of the gait pattern and modeling via parameterized curves, development of a fitting algorithm, and computation of locomotive indices. The proposed fitting algorithm applies on both ankles' depth data simultaneously, by minimizing through numerical optimization some geometric and biomechanical error functions. For validation, 15 subjects were asked to walk inside the walkway of the OptoGait, while the OptoGait and an RGB-D camera (Microsoft Azure Kinect) were both recording. Then, the spatio-temporal parameters of both feet were computed using the OptoGait and the proposed model. Validation results show that the proposed model yields good to excellent absolute statistical agreement (0.86 ≤ R_c ≤ 0.99). Our kinematic-geometric model offers several benefits: (1) It relies only on the ankles' depth trajectory both for gait events extraction and spatio-temporal parameters' calculation; (2) it is usable with any kind of RGB-D camera or even with 3D marker-based motion analysis systems in absence of toes' and heels' markers; and (3) it enables improving the results by denoising and smoothing the ankles' depth trajectory. Hence, the proposed kinematic-geometric model facilitates the development of portable markerless systems for accurate gait analysis.

Digital Devices for Assessing Motor Functions in Mobility-Impaired and Healthy Populations: Systematic Literature Review

Background

With the advent of smart sensing technology, mobile and wearable devices can provide continuous and objective monitoring and assessment of motor function outcomes.

Objective

We aimed to describe the existing scientific literature on wearable and mobile technologies that are being used or tested for assessing motor functions in mobility-impaired and healthy adults and to evaluate the degree to which these devices provide clinically valid measures of motor function in these populations.

Methods

A systematic literature review was conducted by searching Embase, MEDLINE, CENTRAL (January 1, 2015, to June 24, 2020), the United States and European Union clinical trial registries, and the United States Food and Drug Administration website using predefined study selection criteria. Study selection, data extraction, and quality assessment were performed by 2 independent reviewers.

Results

A total of 91 publications representing 87 unique studies were included. The most represented clinical conditions were Parkinson disease (n=51 studies), followed by stroke (n=5), Huntington disease (n=5), and multiple sclerosis (n=2). A total of 42 motion-detecting devices were identified, and the majority (n=27, 64%) were created for the purpose of health care–related data collection, although approximately 25% were personal electronic devices (eg, smartphones and watches) and 11% were entertainment consoles (eg, Microsoft Kinect or Xbox and Nintendo Wii). The primary motion outcomes were related to gait (n=30), gross motor movements (n=25), and fine motor movements (n=23). As a group, sensor-derived motion data showed a mean sensitivity of 0.83 (SD 7.27), a mean specificity of 0.84 (SD 15.40), a mean accuracy of 0.90 (SD 5.87) in discriminating between diseased individuals and healthy controls, and a mean Pearson r validity coefficient of 0.52 (SD 0.22) relative to clinical measures. We did not find significant differences in the degree of validity between in-laboratory and at-home sensor-based assessments nor between device class (ie, health care–related device, personal electronic devices, and entertainment consoles).

Conclusions

Sensor-derived motion data can be leveraged to classify and quantify disease status for a variety of neurological conditions. However, most of the recent research on digital clinical measures is derived from proof-of-concept studies with considerable variation in methodological approaches, and much of the reviewed literature has focused on clinical validation, with less than one-quarter of the studies performing analytical validation. Overall, future research is crucially needed to further consolidate that sensor-derived motion data may lead to the development of robust and transformative digital measurements intended to predict, diagnose, and quantify neurological disease state and its longitudinal change.

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.

Healthcare applications of single camera markerless motion capture: a scoping review

Background

Single camera markerless motion capture has the potential to facilitate at home movement assessment due to the ease of setup, portability, and affordable cost of the technology. However, it is not clear what the current healthcare applications of single camera markerless motion capture are and what information is being collected that may be used to inform clinical decision making. This review aims to map the available literature to highlight potential use cases and identify the limitations of the technology for clinicians and researchers interested in the collection of movement data.

Survey Methodology

Studies were collected up to 14 January 2022 using Pubmed, CINAHL and SPORTDiscus using a systematic search. Data recorded included the description of the markerless system, clinical outcome measures, and biomechanical data mapped to the International Classification of Functioning, Disability and Health Framework (ICF). Studies were grouped by patient population.

Results

A total of 50 studies were included for data collection. Use cases for single camera markerless motion capture technology were identified for Neurological Injury in Children and Adults; Hereditary/Genetic Neuromuscular Disorders; Frailty; and Orthopaedic or Musculoskeletal groups. Single camera markerless systems were found to perform well in studies involving single plane measurements, such as in the analysis of infant general movements or spatiotemporal parameters of gait, when evaluated against 3D marker-based systems and a variety of clinical outcome measures. However, they were less capable than marker-based systems in studies requiring the tracking of detailed 3D kinematics or fine movements such as finger tracking.

Conclusions

Single camera markerless motion capture offers great potential for extending the scope of movement analysis outside of laboratory settings in a practical way, but currently suffers from a lack of accuracy where detailed 3D kinematics are required for clinical decision making. Future work should therefore focus on improving tracking accuracy of movements that are out of plane relative to the camera orientation or affected by occlusion, such as supination and pronation of the forearm.

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.

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.

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

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

Differences in movement limitations in different low back pain severity in functional tests using an RGB-D camera

Low back pain (LBP) can lead to motor control disturbance which can be one of the causes of reoccurrence of the complaint. It is important to improve our knowledge of movement related disturbances during assessment in LBP and to classify patients according to the severity. The aim of this study is to present differences in kinematic variables using a RGB-D camera in order to classify LBP patients with different severity. A cross-sectional study was carried out. Subjects with non-specific subacute and chronic LBP were screened 6 weeks following an episode. Functional tests were bending trunk test, sock test and sit to stand test. Participants performed as many repetitions as possible during 30 s for each functional test. Angular displacement, velocity and acceleration, linear acceleration, time and repetitions were analysed. Participants were divided into two groups to determine their different LBP severity with a k-means clusters according to the results obtained in Roland Morris questionnaire (RMQ). Comparing different severity groups based on RMQ score (high impact = 17.15, low impact = 7.47), bending trunk test obtained significative differences in linear acceleration (p = 0.002-0.01). The differences of total linear acceleration during the Sit to Stand test were significative (p = 0.004-0.02). Sock test showed not significative differences between groups (p > 0.05). Linear acceleration variables during Sit to Stand test and Bending trunk test were significatively different between the different severity groups. RGB-D camera system and functional tests can detect kinematic differences in different type of LBP according to the functionality. Trial registration: ClinicalTrials.gov NCT03293095 "Functional Task Kinematic in Musculoskeletal Pathology" September 26, 2017.

Clinical 3-D Gait Assessment of Patients With Polyneuropathy Associated With Hereditary Transthyretin Amyloidosis

Hereditary amyloidosis associated with transthyretin V30M (ATTRv V30M) is a rare and inherited multisystemic disease, with a variable presentation and a challenging diagnosis, follow-up and treatment. This condition entails a definitive and progressive motor impairment that compromises walking ability from near onset. The detection of the latter is key for the disease's diagnosis. The aim of this work is to perform quantitative 3-D gait analysis in ATTRv V30M patients, at different disease stages, and explore the potential of the obtained gait information for supporting early diagnosis and/or stage distinction during follow-up. Sixty-six subjects (25 healthy controls, 14 asymptomatic ATTRv V30M carriers, and 27 symptomatic patients) were included in this case-control study. All subjects were asked to walk back and forth for 2 min, in front of a Kinect v2 camera prepared for body motion tracking. We then used our own software to extract gait-related parameters from the camera's 3-D body data. For each parameter, the main subject groups and symptomatic patient subgroups were statistically compared. Most of the explored gait parameters can potentially be used to distinguish between the considered group pairs. Despite of statistically significant differences being found, most of them were undetected to the naked eye. Our Kinect camera-based system is easy to use in clinical settings and provides quantitative gait information that can be useful for supporting clinical assessment during ATTRv V30M onset detection and follow-up, as well as developing more objective and fine-grained rating scales to further support the clinical decisions.

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.