Gait analysis methods: an overview of wearable and non-wearable systems, highlighting clinical applications. Sensors (Basel). 2014;14:3362-3394. [PMID: 24556672 PMCID: PMC3958266 DOI: 10.3390/s140203362]

Predicting Human Motion Signals Using Modern Deep Learning Techniques and Smartphone Sensors

The global adoption of smartphone technology affords many conveniences, and not surprisingly, healthcare applications using wearable sensors like smartphones have received much attention. Among the various potential applications and research related to healthcare, recent studies have been conducted on recognizing human activities and characterizing human motions, often with wearable sensors, and with sensor signals that generally operate in the form of time series. In most studies, these sensor signals are used after pre-processing, e.g., by converting them into an image format rather than directly using the sensor signals themselves. Several methods have been used for converting time series data to image formats, such as spectrograms, raw plots, and recurrence plots. In this paper, we deal with the health care task of predicting human motion signals obtained from sensors attached to persons. We convert the motion signals into image formats with the recurrence plot method, and use it as an input into a deep learning model. For predicting subsequent motion signals, we utilize a recently introduced deep learning model combining neural networks and the Fourier transform, the Fourier neural operator. The model can be viewed as a Fourier-transform-based extension of a convolution neural network, and in these experiments, we compare the results of the model to the convolution neural network (CNN) model. The results of the proposed method in this paper show better performance than the results of the CNN model and, furthermore, we confirm that it can be utilized for detecting potential accidental falls more quickly via predicted motion signals.

Gait Assessment Using Wearable Sensor-Based Devices in People Living with Dementia: A Systematic Review

The ability of people living with dementia to walk independently is a key contributor to their overall well-being and autonomy. For this reason, understanding the relationship between dementia and gait is significant. With rapidly emerging developments in technology, wearable devices offer a portable and affordable alternative for healthcare experts to objectively estimate kinematic parameters with great accuracy. This systematic review aims to provide an updated overview and explore the opportunities in the current research on wearable sensors for gait analysis in adults over 60 living with dementia. A systematic search was conducted in the following scientific databases: PubMed, Cochrane Library, and IEEE Xplore. The targeted search identified 1992 articles that were potentially eligible for inclusion, but, following title, abstract, and full-text review, only 6 articles were deemed to meet the inclusion criteria. Most studies performed adequately on measures of reporting, in and out of a laboratory environment, and found that sensor-derived data are successful in their respective objectives and goals. Nevertheless, we believe that additional studies utilizing standardized protocols should be conducted in the future to explore the impact and usefulness of wearable devices in gait-related characteristics such as fall prognosis and early diagnosis in people living with dementia.

A-GAS: A Probabilistic Approach for Generating Automated Gait Assessment Score for Cerebral Palsy Children

Gait disorders in children with cerebral palsy (CP) affect their mental, physical, economic, and social lives. Gait assessment is one of the essential steps of gait management. It has been widely used for clinical decision making and evaluation of different treatment outcomes. However, most of the present methods of gait assessment are subjective, less sensitive to small pathological changes, time-taking and need a great effort of an expert. This work proposes an automated, comprehensive gait assessment score (A-GAS) for gait disorders in CP. Kinematic data of 356 CP and 41 typically developing subjects is used to validate the performance of A-GAS. For the computation of A-GAS, instance abnormality index (AII) and abnormality index (AI) are calculated. AII quantifies gait abnormality of a gait cycle instance, while AI quantifies gait abnormality of a joint angle profile during walking. AII is calculated for all gait cycle instances by performing probabilistic and statistical analyses. Abnormality index (AI) is a weighted sum of AII, computed for each joint angle profile. A-GAS is a weighted sum of AI, calculated for a lower limb. Moreover, a graphical representation of the gait assessment report, including AII, AI, and A-GAS is generated for providing a better depiction of the assessment score. Furthermore, the work compares A-GAS with a present rating-based gait assessment scores to understand fundamental differences. Finally, A-GAS's performance is verified for a high-cost multi-camera set-up using nine joint angle profiles and a low-cost single camera set-up using three joint angle profiles. Results show no significant differences in performance of A-GAS for both the set-ups. Therefore, A-GAS for both the set-ups can be used interchangeably.

Application of Machine Vision in Classifying Gait Frailty Among Older Adults

Background: Frail older adults have an increased risk of adverse health outcomes and premature death. They also exhibit altered gait characteristics in comparison with healthy individuals. Methods: In this study, we created a Fried's frailty phenotype (FFP) labelled casual walking video set of older adults based on the West China Health and Aging Trend study. A series of hyperparameters in machine vision models were evaluated for body key point extraction (AlphaPose), silhouette segmentation (Pose2Seg, DPose2Seg, and Mask R-CNN), gait feature extraction (Gaitset, LGaitset, and DGaitset), and feature classification (AlexNet and VGG16), and were highly optimised during analysis of gait sequences of the current dataset. Results: The area under the curve (AUC) of the receiver operating characteristic (ROC) at the physical frailty state identification task for AlexNet was 0.851 (0.827-0.8747) and 0.901 (0.878-0.920) in macro and micro, respectively, and was 0.855 (0.834-0.877) and 0.905 (0.886-0.925) for VGG16 in macro and micro, respectively. Furthermore, this study presents the machine vision method equipped with better predictive performance globally than age and grip strength, as well as than 4-m-walking-time in healthy and pre-frailty classifying. Conclusion: The gait analysis method in this article is unreported and provides promising original tool for frailty and pre-frailty screening with the characteristics of convenience, objectivity, rapidity, and non-contact. These methods can be extended to any gait-related disease identification processes, as well as in-home health monitoring.

Gait Analysis Using Accelerometry Data from a Single Smartphone: Agreement and Consistency between a Smartphone Application and Gold-Standard Gait Analysis System

Spatio-temporal parameters of human gait, currently measured using different methods, provide valuable information on health. Inertial Measurement Units (IMUs) are one such method of gait analysis, with smartphone IMUs serving as a good substitute for current gold-standard techniques. Here we investigate the concurrent validity of a smartphone placed in a front-facing pocket to perform gait analysis. Sixty community-dwelling healthy adults equipped with a smartphone and an application for gait analysis completed a 2-min walk on a marked path. Concurrent validity was assessed against an APDM mobility lab (APDM Inc.; Portland, OR, USA). Bland–Altman plots and intraclass correlation coefficients (agreement and consistency) for gait speed, cadence, and step length indicate good to excellent agreement (ICC_2,1 > 0.8). For right leg stance and swing % of gait cycle and double support % of gait cycle, results were moderate (0.52 < ICC_2,1 < 0.62). For left leg stance and swing % of gait cycle left results show poor agreement (ICC_2,1 < 0.5). Consistency of results was good to excellent for all tested parameters (ICC_3,1 > 0.8). Thus we have a valid and reliable instrument for measuring healthy adults’ spatio-temporal gait parameters in a controlled walking environment.

Gait and Axial Spondyloarthritis: Comparative Gait Analysis Study Using Foot-Worn Inertial Sensors

Background

Axial spondyloarthritis (axSpA) can lead to spinal mobility restrictions associated with restricted lower limb ranges of motion, thoracic kyphosis, spinopelvic ankylosis, or decrease in muscle strength. It is well known that these factors can have consequences on spatiotemporal gait parameters during walking. However, no study has assessed spatiotemporal gait parameters in patients with axSpA. Divergent results have been obtained in the studies assessing spatiotemporal gait parameters in ankylosing spondylitis, a subgroup of axSpA, which could be partly explained by self-reported pain intensity scores at time of assessment. Inertial measurement units (IMUs) are increasingly popular and may facilitate gait assessment in clinical practice.

Objective

This study compared spatiotemporal gait parameters assessed with foot-worn IMUs in patients with axSpA and matched healthy individuals without and with pain intensity score as a covariate.

Methods

A total of 30 patients with axSpA and 30 age- and sex-matched healthy controls performed a 10-m walk test at comfortable speed. Various spatiotemporal gait parameters were computed from foot-worn inertial sensors including gait speed in ms^–1 (mean walking velocity), cadence in steps/minute (number of steps in a minute), stride length in m (distance between 2 consecutive footprints of the same foot on the ground), swing time in percentage (portion of the cycle during which the foot is in the air), stance time in percentage (portion of the cycle during which part of the foot touches the ground), and double support time in percentage (portion of the cycle where both feet touch the ground).

Results

Age, height, and weight were not significantly different between groups. Self-reported pain intensity was significantly higher in patients with axSpA than healthy controls (P<.001). Independent sample t tests indicated that patients with axSpA presented lower gait speed (P<.001) and cadence (P=.004), shorter stride length (P<.001) and swing time (P<.001), and longer double support time (P<.001) and stance time (P<.001) than healthy controls. When using pain intensity as a covariate, spatiotemporal gait parameters were still significant with patients with axSpA exhibiting lower gait speed (P<.001), shorter stride length (P=.001) and swing time (P<.001), and longer double support time (P<.001) and stance time (P<.001) than matched healthy controls. Interestingly, there were no longer statistically significant between-group differences observed for the cadence (P=.17).

Conclusions

Gait was significantly altered in patients with axSpA with reduced speed, cadence, stride length, and swing time and increased double support and stance time. Taken together, these changes in spatiotemporal gait parameters could be interpreted as the adoption of a so-called cautious gait pattern in patients with axSpA. Among factors that may influence gait in patients with axSpA, patient self-reported pain intensity could play a role. Finally, IMUs allowed computation of spatiotemporal gait parameters and are usable to assess gait in patients with axSpA in clinical routine.

Trial Registration

ClinicalTrials.gov NCT03761212; https://clinicaltrials.gov/ct2/show/NCT03761212

International Registered Report Identifier (IRRID)

RR2-10.1007/s00296-019-04396-4

Objectifying clinical gait assessment: using a single-point wearable sensor to quantify the spatiotemporal gait metrics of people with lumbar spinal stenosis

Background

Wearable accelerometer-containing devices have become a mainstay in clinical studies which attempt to classify the gait patterns in various diseases. A gait profile for lumbar spinal stenosis (LSS) has not been developed, and no study has validated a simple wearable system for the clinical assessment of gait in lumbar stenosis. This study identifies the changes to gait patterns that occur in LSS to create a preliminary disease-specific gait profile. In addition, this study compares a chest-based wearable sensor, the MetaMotionC^© device and inertial measurement unit python script (MMC/IMUPY) system, against a reference-standard, videography, to preliminarily assess its accuracy in measuring the gait features of patients with LSS.

Methods

We conduct a cross-sectional observational study examining the walking patterns of 25 LSS patients and 33 healthy controls. To construct a preliminary disease-specific gait profile for LSS, the gait patterns of the 25 LSS patients and 25 healthy controls with similar ages were compared. To assess the accuracy of the MMC/IMUPY system in measuring the gait features of patients with LSS, its results were compared with videography for the 21 LSS and 33 healthy controls whose walking bouts exceeded 30 m.

Results

Patients suffering from LSS walked significantly slower, with shorter, less frequent steps and higher asymmetry compared to healthy controls. The MMC/IMUPY system had >90% agreement with videography for all spatiotemporal gait metrics that both methods could measure.

Conclusions

The MMC/IMUPY system is a simple and feasible system for the construction of a preliminary disease-specific gait profile for LSS. Before clinical application in everyday living conditions is possible, further studies involving the construction of a more detailed disease-specific gait profile for LSS by disease severity, and the validation of the MMC/IMUPY system in the home environment, are required.

Camera-Based Human Gait Speed Monitoring and Tracking for Performance Assessment of Elderly Patients with Cancer

This paper presents a camera-based device for monitoring walking gait speed. The walking gait speed data will be used for performance assessment of elderly patients with cancer and calibrating wearable walking gait speed monitoring devices. This standalone device has a Raspberry Pi computer, three cameras (two cameras for finding the trajectory and gait speed of the subject and one camera for tracking the subject), and two stepper motors. The stepper motors turn the camera platform left and right and tilt it up and down by using video footage from the center camera. The left and right cameras are used to record videos of the person walking. The algorithm for operating the proposed system is developed in Python. The measured data and calculated outputs of the system consist of times for frames, distances from the center camera, horizontal angles, distances moved, instantaneous gait speed (frame-by-frame), total distance walked, and average speed. This system covers a large Lab area of 134.3 m² and has achieved errors of less than 5% for gait speed calculation.Clinical Relevance- This project will help specialists to adjust the chemo dosage for elderly patients with cancer. The results will be used to analyze the human walking movements for estimating frailty and rehabilitation applications, too.

Gait-based Human Identification through Minimum Gait-phases and Sensors

The incredible pace at which the world's elderly population is growing will put severe burdens on current healthcare systems and resources. To alleviate this concern the health care systems must rely on the transformation of eldercare and old homes to use Ambient Assisted Living (AAL). Human identification is one of the most common and critical tasks for condition monitoring, human-machine interaction, and providing assistive services in such environments. Recently, human gait has gained new attention as a biometric for identification to achieve contactless identification from a distance robust to physical appearances. However, an important aspect of gait identification through wearables and image-based systems alike is accurate identification when limited information is available for example, when only a fraction of the whole gait cycle or only a part of the subject's body is visible. In this paper, we present a gait identification technique based on temporal and descriptive statistic parameters of different gait phases as the features and we investigate the performance of using only single gait phases for the identification task using a minimum number of sensors. Gait data were collected from 60 individuals through pelvis and foot sensors. Six different machine learning algorithms were used for identification. It was shown that it is possible to achieve high accuracy of over 95.5% by monitoring a single phase of the whole gait cycle through only a single sensor. It was also shown that the proposed methodology could be used to achieve 100% identification accuracy when the whole gait cycle was monitored through pelvis and foot sensors combined. The ANN was found to be more robust to less number of data features compared to SVM and was concluded as the best machine algorithm for the purpose.

A Wearable Walking Gait Speed-Sensing Device using Frequency Bifurcations of Multi-Resonator Inductive Link

This paper describes a wearable inductive sensing system to monitor (i.e., sense and estimate) walking gait speed. This proposed design relies on the multi-resonance inductive link to quantify the angle of the human legs for calculating the speed of walking. The walking gait speed can be used to estimate the frailty in elderly patients with cancer. We have designed, optimized, and implemented a multi-resonator sensor unit to precisely measure the angle between human legs during walking. The couplings between resonators change by lateral displacements due to walking, and a reading coil senses the frequency bifurcations, corresponding to the changes in angle between legs. The proposed design is optimized using ANSYS HFSS and implemented using copper foil. The Specific Absorption Rate, SAR, in the human body is calculated 0.035 W/kg using the developed HFSS model. The operating frequency range of the proposed sensor is from 25 MHz to 46 MHz, and it can measure angles up to 90° (-45° to +45°). The measured resolution for estimating the angle shows the capability of the sensor for calculating the walking speed with a resolution of less than 0.1 m/s.

Relationship between Swimming Performance, Biomechanical Variables and the Calculated Predicted 1-RM Push-up in Competitive Swimmers

One repetition maximum (1RM) push-ups, based upon the load–velocity relationship, are able to predict the maximum upper body strength. The aim of the present study was to examine the relationship between the predicted 1RM push-up based upon the load–velocity relationship and swimming performance and kinematical variables in competitive swimmers. Thirty-three competitive male swimmers (age = 16.46 ± 0.59 years, body mass = 72.82 ± 8.41 kg, body height = 180.56 ± 5.69 cm) performed push-up exercises without a weight vest and with a 10, 20 and 30 kg weight vests. A load–velocity relationship was established as a product of the load and velocity of the push-up per participant, and the equation was used to establish a predicted 1RM. Our findings showed a predicted 1RM push-up of 82.98 ± 9.95 kg. Pearson correlations revealed a nearly perfect relationship between the 1RM push-up and the 25 or 50 m front crawl (r = −0.968, r = −0.955), and between 1RM push-up and the 25 or 50 m front crawl with arms (r = −0.955, r = x0.941). Similarly, our results revealed significant near-perfect correlations between 1RM push-up and kinematical variables (r = 0.93–0.96) except the stroke index, which had a large relationship (r = 0.56). This study suggests that swimming performance and kinematical variables are correlated with the predicted 1RM push-up. The 1RM push-up based upon the load–velocity relationship is a low cost and time-effective alternative for swimmers and coaches to predict maximum upper body strength to optimize swimming performance in short races.

Joint Constraints Based Dynamic Calibration of IMU Position on Lower Limbs in IMU-MoCap

The position calibration of inertial measurement units (IMUs) is an important part of human motion capture, especially in wearable systems. In realistic applications, static calibration is quickly invalid during the motions for IMUs loosely mounted on the body. In this paper, we propose a dynamic position calibration algorithm for IMUs mounted on the waist, upper leg, lower leg, and foot based on joint constraints. To solve the problem of IMUs’ position displacement, we introduce the Gauss–Newton (GN) method based on the Jacobian matrix, the dynamic weight particle swarm optimization (DWPSO), and the grey wolf optimizer (GWO) to realize IMUs’ position calibration. Furthermore, we establish the coordinate system of human lower limbs to estimate each joint angle and use the fusion algorithm in the field of quaternions to improve the attitude calibration performance of a single IMU. The performances of these three algorithms are analyzed and evaluated by gait tests on the human body and comparisons with a high-precision IMU-Mocap reference device. The simulation results show that the three algorithms can effectively calibrate the IMU’s position for human lower limbs. Additionally, when the degree of freedom (DOF) of a certain dimension is limited, the performances of the DWPSO and GWO may be better than GN, when the joint changes sufficiently, the performances of the three are close. The results confirm that the dynamic calibration algorithm based on joint constraints can effectively reduce the position offset errors of IMUs on upper or lower limbs in practical applications.

A principal component analysis (PCA) based assessment of the gait performance

The gait assessment is instrumental for evaluating the efficiency of rehabilitation of persons with a motor impairment of the lower extremities. The protocol for quantifying the gait performance needs to be simple and easy to implement; therefore, a wearable system and user-friendly computer program are preferable. We used the Gait Master (instrumented insoles) with the industrial quality ground reaction forces (GRF) sensors and 6D inertial measurement units (IMU). WiFi transmitted 10 signals from the GRF sensors and 12 signals from the accelerometers and gyroscopes to the host computer. The clinician was following in real-time the acquired data to be assured that the WiFi operated correctly. We developed a method that uses principal component analysis (PCA) to provide a clinician with easy to interpret cyclograms showing the difference between the recorded and healthy-like gait performance. The cyclograms formed by the first two principal components in the PCA space show the step-to-step reproducibility. We suggest that a cyclogram and its orientation to the coordinate system PC1 vs. PC2 allow a simple assessment of the gait. We show results for six healthy persons and five patients with hemiplegia.

The Effects of Auditory Feedback Gait Training Using Smart Insole on Stroke Patients

This study aimed to assess the effect of the auditory feedback gait training (AFGT) using smart insole on the gait variables, dynamic balance, and activities of daily living (ADL) of stroke patients. In this case, 45 chronic stroke patients who were diagnosed with a stroke before 6 months and could walk more than 10 m were included in this study. Participants were randomly allocated to the smart insole training group (n = 23), in which the AFGT system was used, or to the general gait training group (GGTG) (n = 22). Both groups completed conventional rehabilitation, including conventional physiotherapy and gait training, lasting 60 min per session, five times per week for 4 weeks. Instead of gait training, the smart insole training group received smart insole training twice per week for 4 weeks. Participants were assessed using the GAITRite for gait variables and Timed Up and Go test (TUG), Berg Balance Scale (BBS) for dynamic balance, and Modified Barthel Index (MBI) for ADL. The spatiotemporal gait parameters, symmetry of gait, TUG, BBS, and MBI in the smart insole training group were significantly improved compared to those in the GGTG (p < 0.05). The AFGT system approach is a helpful method for improving gait variables, dynamic balance, and ADL in chronic stroke patients.

Detecting Fall Risk and Frailty in Elders with Inertial Motion Sensors: A Survey of Significant Gait Parameters

In the elderly, geriatric problems such as the risk of fall or frailty are a challenge for society. Patients with frailty present difficulties in walking and higher fall risk. The use of sensors for gait analysis allows the detection of objective parameters related to these pathologies and to make an early diagnosis. Inertial Measurement Units (IMUs) are wearables that, due to their accuracy, portability, and low price, are an excellent option to analyze human gait parameters in health-monitoring applications. Many relevant gait parameters (e.g., step time, walking speed) are used to assess motor, or even cognitive, health problems in the elderly, but we perceived that there is not a full consensus on which parameters are the most significant to estimate the risk of fall and the frailty state. In this work, we analyzed the different IMU-based gait parameters proposed in the literature to assess frailty state (robust, prefrail, or frail) or fall risk. The aim was to collect the most significant gait parameters, measured from inertial sensors, able to discriminate between patient groups and to highlight those parameters that are not relevant or for which there is controversy among the examined works. For this purpose, a literature review of the studies published in recent years was carried out; apart from 10 previous relevant reviews using inertial and other sensing technologies, a total of 22 specific studies giving statistical significance values were analyzed. The results showed that the most significant parameters are double-support time, gait speed, stride time, step time, and the number of steps/day or walking percentage/day, for frailty diagnosis. In the case of fall risk detection, parameters related to trunk stability or movements are the most relevant. Although these results are important, the total number of works found was limited and most of them performed the significance statistics on subsets of all possible gait parameters; this fact highlights the need for new frailty studies using a more complete set of gait parameters.

State-of-the-Art Wearable Sensors and Possibilities for Radar in Fall Prevention

Radar technology is constantly evolving, and new applications are arising, particularly for the millimeter wave bands. A novel application for radar is gait monitoring for fall prevention, which may play a key role in maintaining the quality of life of people as they age. Alarming statistics indicate that one in three adults aged 65 years or older will experience a fall every year. A review of the sensors used for gait analysis and their applications to technology-based fall prevention interventions was conducted, focusing on wearable devices and radar technology. Knowledge gaps were identified, such as wearable radar development, application specific signal processing and the use of machine learning algorithms for classification and risk assessment. Fall prevention through gait monitoring in the natural environment presents significant opportunities for further research. Wearable radar could be useful for measuring gait parameters and performing fall risk-assessment using statistical methods, and could also be used to monitor obstacles in real-time.

The Functional Assessment of Balance in Concussion (FAB-C) Battery

Background

There is no clinical tool that assesses multiple components of postural control potentially impacted by sport-related concussion (SRC).

Objective

To develop and assess the feasibility and construct validity of the Functional Assessment of Balance in Concussion (FAB-C) battery.

Study Design

Cross-sectional study.

Methods

Tests for inclusion in the FAB-C battery were identified through a search of the literature. The feasibility and construct validity of the battery was assessed with a convenience sample of active individuals (13-24 years) with and without a SRC. Feasibility outcomes included battery completion (yes/no), number of adverse events, time to administer (minutes) and cost of the battery (Canadian Dollars). Construct validity was assessed by examining correlations between tests included in the battery, and describing differences [mean (standard deviation), median (range) or proportion] in outcomes between uninjured participants and participants with SRC.

Results

Seven tests were included in the FAB-C battery. All 40 uninjured participants [12 female; median age 17 years] completed the FAB-C assessment compared to 86% of seven participants with SRC [1 female; median age 17]. No participants demonstrated adverse effects. The median administration time of the battery was 49 minutes (range 44-60). The cost of the battery was low (~$100 Canadian Dollars). Limited correlations (r<0.7) between tests in the battery were observed. A greater percentage of uninjured participants (52% to 82%) passed individual tests in the battery compared to participants with SRC (17% to 66%).

Conclusion

Although promising, the FAB-C battery requires further evaluation before adoption for widespread clinical use.

Level of Evidence

Level 3b.

Remote Gait Type Classification System Using Markerless 2D Video

Several pathologies can alter the way people walk, i.e., their gait. Gait analysis can be used to detect such alterations and, therefore, help diagnose certain pathologies or assess people's health and recovery. Simple vision-based systems have a considerable potential in this area, as they allow the capture of gait in unconstrained environments, such as at home or in a clinic, while the required computations can be done remotely. State-of-the-art vision-based systems for gait analysis use deep learning strategies, thus requiring a large amount of data for training. However, to the best of our knowledge, the largest publicly available pathological gait dataset contains only 10 subjects, simulating five types of gait. This paper presents a new dataset, GAIT-IT, captured from 21 subjects simulating five types of gait, at two severity levels. The dataset is recorded in a professional studio, making the sequences free of background camouflage, variations in illumination and other visual artifacts. The dataset is used to train a novel automatic gait analysis system. Compared to the state-of-the-art, the proposed system achieves a drastic reduction in the number of trainable parameters, memory requirements and execution times, while the classification accuracy is on par with the state-of-the-art. Recognizing the importance of remote healthcare, the proposed automatic gait analysis system is integrated with a prototype web application. This prototype is presently hosted in a private network, and after further tests and development it will allow people to upload a video of them walking and execute a web service that classifies their gait. The web application has a user-friendly interface usable by healthcare professionals or by laypersons. The application also makes an association between the identified type of gait and potential gait pathologies that exhibit the identified characteristics.

Applying deep neural networks and inertial measurement unit in recognizing irregular walking differences in the real world

Falling injuries pose serious health risks to people of all ages, and knowing the extent of exposure to irregular surfaces will increase the ability to measure fall risk. Current gait analysis methods require overly complicated instrumentation and have not been tested for external factors such as walking surfaces that are encountered in the real-world, thus the results are difficult to extrapolate to real-world situations. Artificial intelligence approaches (in particular deep learning networks of varied architectures) to analyze data collected from wearable sensors were used to identify irregular surface exposure in a real-world setting. Thirty young adults wore six Inertial Measurement Unit (IMU) sensors placed on their body (right wrist, trunks at the L5/S1 level, left and right thigh, left and right shank) while walking over eight different surfaces commonly encountered in the living community as well as occupational settings. Three variations of deep learning models were trained to solve this walking surface recognition problem: 1) convolution neural network (CNN); 2) long short term memory (LSTM) network and 3) LSTM structure with an extra global pooling layer (Global-LSTM) which learns the coordination between different data streams (e.g. different channels of the same sensor as well as different sensors). Results indicated that all three deep learning models can recognize walking surfaces with above 0.90 accuracy, with the Global-LSTM yielding the best performance at 0.92 accuracy. In terms of individual sensors, the right thigh based Global-LSTM model reported the highest accuracy (0.90 accuracy). Results from this study provide further evidence that deep learning and wearable sensors can be utilized to recognize irregular walking surfaces induced motion alteration and applied to prevent falling injuries.

Sit-to-Stand Test for Neurodegenerative Diseases Video Classification

In this extended version of this paper, an automatic video diagnosis system for dementia classification is presented. Starting from video recordings of patients and control subjects, performing sit-to-stand test, the designed system is capable of extracting relevant patterns for binary discern patients with dementia from healthy subjects. The original system achieved an accuracy 0.808 by using the rigorous inter-patient separation scheme especially suited for medical purposes. This separation scheme provides the use of some people for training and others, different, people for testing. The implementation of features from the kinematic theory of rapid human movement and its sigma-lognormal model together with classic features increased the overall accuracy of the system to 0.947 F1 score. In addition, multi-class classification was performed with the aim of classifying neurodegenerative disease severities. This work is an original and pioneering work on sit-to-stand video classification for neurodegenerative diseases, its novelties are on phases segmentation, experimental setup and the application of kinematic theory of rapid human movements to sit-to-stand videos for neurodegenerative disease assessment.

An Inertial Sensor-Based Gait Analysis Pipeline for the Assessment of Real-World Stair Ambulation Parameters

Climbing stairs is a fundamental part of daily life, adding additional demands on the postural control system compared to level walking. Although real-world gait analysis studies likely contain stair ambulation sequences, algorithms dedicated to the analysis of such activities are still missing. Therefore, we propose a new gait analysis pipeline for foot-worn inertial sensors, which can segment, parametrize, and classify strides from continuous gait sequences that include level walking, stair ascending, and stair descending. For segmentation, an existing approach based on the hidden Markov model and a feature-based gait event detection were extended, reaching an average segmentation F1 score of 98.5% and gait event timing errors below ±10ms for all conditions. Stride types were classified with an accuracy of 98.2% using spatial features derived from a Kalman filter-based trajectory reconstruction. The evaluation was performed on a dataset of 20 healthy participants walking on three different staircases at different speeds. The entire pipeline was additionally validated end-to-end on an independent dataset of 13 Parkinson’s disease patients. The presented work aims to extend real-world gait analysis by including stair ambulation parameters in order to gain new insights into mobility impairments that can be linked to clinically relevant conditions such as a patient’s fall risk and disease state or progression.

A Spatiotemporal Deep Learning Approach for Automatic Pathological Gait Classification

Human motion analysis provides useful information for the diagnosis and recovery assessment of people suffering from pathologies, such as those affecting the way of walking, i.e., gait. With recent developments in deep learning, state-of-the-art performance can now be achieved using a single 2D-RGB-camera-based gait analysis system, offering an objective assessment of gait-related pathologies. Such systems provide a valuable complement/alternative to the current standard practice of subjective assessment. Most 2D-RGB-camera-based gait analysis approaches rely on compact gait representations, such as the gait energy image, which summarize the characteristics of a walking sequence into one single image. However, such compact representations do not fully capture the temporal information and dependencies between successive gait movements. This limitation is addressed by proposing a spatiotemporal deep learning approach that uses a selection of key frames to represent a gait cycle. Convolutional and recurrent deep neural networks were combined, processing each gait cycle as a collection of silhouette key frames, allowing the system to learn temporal patterns among the spatial features extracted at individual time instants. Trained with gait sequences from the GAIT-IT dataset, the proposed system is able to improve gait pathology classification accuracy, outperforming state-of-the-art solutions and achieving improved generalization on cross-dataset tests.

Adaptation of bilateral coordination of gait during split belt walking as reflected by the phase coordination index

BACKGROUND

The Split belt treadmill (SBTM) has recently been used in research and rehabilitation to study and utilize gait adaptations. The Phase coordination index (PCI) is useful in assessing bilateral coordination of gait by quantifying the consistency and accuracy in generating the anti-phased left-right stepping pattern. Recently we proposed that 23 strides are sufficient to reliably characterize PCI values from regular over ground and treadmill walking RESEARCH QUESTION: Can we detect the effect of SBTM on PCI using only 23 gait cycles also from SBTM walking?

METHODS

Young healthy participants (n = 13) with right side motor dominance performed SBTM walking trials. Experiment protocol began by walking in tied belt (TB) mode, followed by an incremental speed increase of one of the belts by 50 % - split belt (SB) mode. This was performed for each side. Two 1-minute segments were analyzed per participant, TB and SB. PCI analysis was carried out upon fewer strides (n = 23) and compared to PCI that was obtained based on all available strides (n = 56 ± 5).

RESULTS

Clear SBTM walking effects on PCI were seen in both experiments, for example, PCI increased from 4.46 ± 1.5 % (TB) to 10.07 ± 3.6 % (SB) for left belt speed increase. Twenty three strides from each trail were sufficient to demonstrate the effect.

SIGNIFICANCE

PCI can be a useful metric to characterize changes in bilateral coordination of gait during SBTM gait adaptations. The fact that 23 strides are sufficient for its reliable estimation, contribute to the continued monitoring through the adaptation process (i.e., by using time windows).

Gait Kinematic Parameters in Parkinson's Disease: A Systematic Review

BACKGROUND

Gait impairments are common and highly disabling for Parkinson's disease (PD) patients. With the development of technology-based tools, it is now possible to measure the spatiotemporal parameters of gait with a reduced margin of error, thereby enabling a more accurate characterization of impairment.

OBJECTIVE

To summarize and critically appraise the characteristics of technology-based gait analysis in PD and to provide mean and standard deviation values for spatiotemporal gait parameters.

METHODS

A systematic review was conducted using the databases CENTRAL, MEDLINE, Embase, and PEDro from their inception to September 2019 to identify all observational and experimental studies conducted in PD or atypical parkinsonism that included a technology-based gait assessment. Two reviewers independently screened citations and extracted data.

RESULTS

We included 95 studies, 82.1% (n = 78) reporting a laboratory gait assessment and 61.1% (n = 58 studies) using a wearable sensor. The most frequently reported parameters were gait velocity, stride and step length, and cadence. A statistically significant difference was found when comparing the mean values of each of these parameters in PD patients versus healthy controls. No statistically significant differences were found in the mean value of the parameters when comparing wearable versus non-wearable sensors, different types of wearable sensors, and different sensor locations.

CONCLUSION

Our results provide useful information for performing objective technology-based gait assessment in PD, as well as mean values to better interpret the results. Further studies should explore the clinical meaningfulness of each parameter and how they behave in a free-living context and throughout disease progression.

Gait characteristics during crossing over obstacle in patients with glaucoma using insole foot pressure

BACKGROUND

Glaucoma, is the most common cause of irreversible visual deficits, presents as an injury to the optic nerve and it is mainly associated with elevated intraocular pressure. The main symptom of glaucoma is a reduction of the visual field, which is usually a source of complaint at the advanced stage of disease. Because of visual deficit, gait dysfunctions, including low gait speed and increased bumping into objects, postural sway, and falling are occurred. Many studies have used stopwatch or motion-sensing devices to report on gait function following glaucoma. However, there are few reports on gait dysfunction assessed by examining foot pressure. This study investigated gait ability following glaucoma according to different gait conditions by assessing foot pressure.

METHODS

Thirty older adults (15 in the sex- and age-matched normal group and 15 in the glaucoma group) were recruited for this study. All participants were walked under 2 different gait conditions in an F-scan system and the subject' assessments were randomly assigned to rule out the order effect. Conditions included: gait over an obstacle in a straight 6 m path, gait in a straight path without an obstacle in the 6 m path. Gait variables included cadence, gait cycle, stance time, center of force (COF) deviation, and COF excursion. About 10 minutes were taken for gait evaluation.

RESULTS

When walking without an obstacle on a 6 m path, there were significant differences between the 2 groups in gait speed, cadence, gait cycle, and stance time (P < .05). There were significant differences when walking with an obstacle on a 6 m path (P < .05). Two-way analysis of variance showed significant effects associated with "glaucoma" not gait condition on all outcomes except for COF deviation and excursion. Also, there was no the interaction effect between "glaucoma" and "gait condition."

CONCLUSION

We demonstrated that glaucoma patients selected the gait strategy such as lower gait function in both gait conditions particularly, slower gait speed and cadence and longer gait cycle and stance time, as determined by examining foot pressure. We believe that our results could help to improve the quality of life of patients with glaucoma.

A Clinically Interpretable Computer-Vision Based Method for Quantifying Gait in Parkinson's Disease

Gait is a core motor function and is impaired in numerous neurological diseases, including Parkinson's disease (PD). Treatment changes in PD are frequently driven by gait assessments in the clinic, commonly rated as part of the Movement Disorder Society (MDS) Unified PD Rating Scale (UPDRS) assessment (item 3.10). We proposed and evaluated a novel approach for estimating severity of gait impairment in Parkinson's disease using a computer vision-based methodology. The system we developed can be used to obtain an estimate for a rating to catch potential errors, or to gain an initial rating in the absence of a trained clinician-for example, during remote home assessments. Videos (n=729) were collected as part of routine MDS-UPDRS gait assessments of Parkinson's patients, and a deep learning library was used to extract body key-point coordinates for each frame. Data were recorded at five clinical sites using commercially available mobile phones or tablets, and had an associated severity rating from a trained clinician. Six features were calculated from time-series signals of the extracted key-points. These features characterized key aspects of the movement including speed (step frequency, estimated using a novel Gamma-Poisson Bayesian model), arm swing, postural control and smoothness (or roughness) of movement. An ordinal random forest classification model (with one class for each of the possible ratings) was trained and evaluated using 10-fold cross validation. Step frequency point estimates from the Bayesian model were highly correlated with manually labelled step frequencies of 606 video clips showing patients walking towards or away from the camera (Pearson's r=0.80, p<0.001). Our classifier achieved a balanced accuracy of 50% (chance = 25%). Estimated UPDRS ratings were within one of the clinicians' ratings in 95% of cases. There was a significant correlation between clinician labels and model estimates (Spearman's ρ=0.52, p<0.001). We show how the interpretability of the feature values could be used by clinicians to support their decision-making and provide insight into the model's objective UPDRS rating estimation. The severity of gait impairment in Parkinson's disease can be estimated using a single patient video, recorded using a consumer mobile device and within standard clinical settings; i.e., videos were recorded in various hospital hallways and offices rather than gait laboratories. This approach can support clinicians during routine assessments by providing an objective rating (or second opinion), and has the potential to be used for remote home assessments, which would allow for more frequent monitoring.

Gait Analysis Accuracy Difference with Different Dimensions of Flexible Capacitance Sensors

Stroke causes neurological pathologies, including gait pathologies, which are diagnosed by gait analysis. However, existing gait analysis devices are difficult to use in situ or are disrupted by external conditions. To overcome these drawbacks, a flexible capacitance sensor was developed in this study. To date, a performance comparison of flexible sensors with different dimensions has not been carried out. The aim of this study was to provide optimized sensor dimension information for gait analysis. To accomplish this, sensors with seven different dimensions were fabricated. The dimensions of the sensors were based on the average body size and movement range of 20- to 59-year-old adults. The sensors were characterized by 100 oscillations. The minimum hysteresis error was 8%. After that, four subjects were equipped with the sensor and walked on a treadmill at a speed of 3.6 km/h. All walking processes were filmed at 50 fps and analyzed in Kinovea. The RMS error was calculated using the same frame rate of the video and the sampling rate of the signal from the sensor. The smallest RMS error between the sensor data and the ankle angle was 3.13° using the 49 × 8 mm sensor. In this study, we confirm the dimensions of the sensor with the highest gait analysis accuracy; therefore, the results can be used to make decisions regarding sensor dimensions.

Anomalous gait feature classification from 3-D motion capture data

The gait kinematics of an individual is affected by various factors, including age, anthropometry, gender, and disease. Detecting anomalous gait features aids in the diagnosis and treatment of gait-related diseases. The objective of this study was to develop a machine learning method for automatically classifying five anomalous gait features, i.e., toe-out, genu varum, pes planus, hindfoot valgus, and forward head posture features, from three-dimensional data on gait kinematics. Gait data and gait feature labels of 488 subjects were acquired. The orientations of the human body segments during a gait cycle were mapped to a low-dimensional latent gait vector using a variational autoencoder. A two-layer neural network was trained to classify five gait features using logistic regression and calculate an anomalous gait feature vector (AGFV). The proposed network showed balanced accuracies of 82.8% for a toe-out, 85.9% for hindfoot valgus, 80.2% for pes planus, 73.2% for genu varum, and 92.9% for forward head posture when the AGFV was rounded to the nearest zero or 1. Multiple anomalous gait features were detectable using the proposed method, which has a practical advantage over current gait indices, including the gait deviation index with a single value. The overall results confirmed the feasibility of using the proposed method for screening subjects with anomalous gait features using three-dimensional motion capture data.

Reliability of the infrared motion-time acquisition system for each motion segment in the timed up-and-go test

[Purpose] This study aimed to investigate the reliability of an infrared motion-time acquisition system by measuring the time taken for five motion segments (sit-to-stand, forward gait, mid-turn, return gait, and turn-stand-to-sit) in the timed up-and-go test. [Participants and Methods] In total, 30 healthy adults (25.1 ± 4.6 years, 19 males and 11 females) were included in this study. Tester A and Tester B measured the time taken in the timed up-and-go test and its five motion segments with an infrared motion-time acquisition system, and two measurements were made by Tester A and one by Tester B. [Results] Intraclass correlation coefficients of the time taken for the five motion segments in the timed up-and-go test and the intra- and inter-rater intraclass correlation coefficients were greater than 0.9. [Conclusion] Infrared motion-time acquisition systems and its five motion segments are reliable and provide accurate measurements during the timed up-and-go test.

Gait phase detection based on inertial measurement unit and force-sensitive resistors embedded in a shoe

This study proposes a system to detect the phases of gait. It consists of an intelligent shoe equipped with an inertial measurement unit (IMU) and force-sensitive resistors (FSRs), and it uses a compound method to recognize gait. The continuous wavelet transform is applied according to accelerations obtained via the IMU to identify heel strike and toe-off events. These events are used to calculate the pressure threshold and proportional factor via the Lopez-Meyer (LM) method by using minimal leave-one-out for training and validation. The LM method can identify the entire sub-phase of the stance of the gait based on ground contact forces measured by using the FSRs and rules of gait event detection. The proposed system was tested on five healthy volunteers who used the intelligent shoe. The results show that it can detect all sub-phases of the gait with an overall accuracy (96%) higher than the LM method. The proportional factor was adaptable to variable body weights, and the reported average errors of competing systems in the literature significantly exceeded the average variation of the proposed system for all phases of gait. The range of errors in the swing phase and sub-phases of stance was also acceptable for application purposes. When the size of the subject's foot was close to that of the intelligent shoe, the error between normative data and phases of gait identified by the detection system was minimal. Furthermore, the proposed system detected abnormalities in the gait circle, and thus, it can be used to monitor the walking activity and measure the motor recovery.

Latest Research Trends in Fall Detection and Prevention Using Machine Learning: A Systematic Review

Falls are unusual actions that cause a significant health risk among older people. The growing percentage of people of old age requires urgent development of fall detection and prevention systems. The emerging technology focuses on developing such systems to improve quality of life, especially for the elderly. A fall prevention system tries to predict and reduce the risk of falls. In contrast, a fall detection system observes the fall and generates a help notification to minimize the consequences of falls. A plethora of technical and review papers exist in the literature with a primary focus on fall detection. Similarly, several studies are relatively old, with a focus on wearables only, and use statistical and threshold-based approaches with a high false alarm rate. Therefore, this paper presents the latest research trends in fall detection and prevention systems using Machine Learning (ML) algorithms. It uses recent studies and analyzes datasets, age groups, ML algorithms, sensors, and location. Additionally, it provides a detailed discussion of the current trends of fall detection and prevention systems with possible future directions. This overview can help researchers understand the current systems and propose new methodologies by improving the highlighted issues.

Locomotor compromises maintain group cohesion in baboon troops on the move

When members of a group differ in locomotor capacity, coordinating collective movement poses a challenge: some individuals may have to move faster (or slower) than their preferred speed to remain together. Such compromises have energetic repercussions, yet research in collective behaviour has largely neglected locomotor consensus costs. Here, we integrate high-resolution tracking of wild baboon locomotion and movement with simulations to demonstrate that size-based variation in locomotor capacity poses an obstacle to the collective movement. While all baboons modulate their gait and move-pause dynamics during collective movement, the costs of maintaining cohesion are disproportionately borne by smaller group members. Although consensus costs are not distributed equally, all group-mates do make locomotor compromises, suggesting a shared decision-making process drives the pace of collective movement in this highly despotic species. These results highlight the importance of considering how social dynamics and locomotor capacity interact to shape the movement ecology of group-living species.

Technological advancements in the analysis of human motion and posture management through digital devices

Technological development of motion and posture analyses is rapidly progressing, especially in rehabilitation settings and sport biomechanics. Consequently, clear discrimination among different measurement systems is required to diversify their use as needed. This review aims to resume the currently used motion and posture analysis systems, clarify and suggest the appropriate approaches suitable for specific cases or contexts. The currently gold standard systems of motion analysis, widely used in clinical settings, present several limitations related to marker placement or long procedure time. Fully automated and markerless systems are overcoming these drawbacks for conducting biomechanical studies, especially outside laboratories. Similarly, new posture analysis techniques are emerging, often driven by the need for fast and non-invasive methods to obtain high-precision results. These new technologies have also become effective for children or adolescents with non-specific back pain and postural insufficiencies. The evolutions of these methods aim to standardize measurements and provide manageable tools in clinical practice for the early diagnosis of musculoskeletal pathologies and to monitor daily improvements of each patient. Herein, these devices and their uses are described, providing researchers, clinicians, orthopedics, physical therapists, and sports coaches an effective guide to use new technologies in their practice as instruments of diagnosis, therapy, and prevention.

Validity Analysis of WalkerViewTM Instrumented Treadmill for Measuring Spatiotemporal and Kinematic Gait Parameters

The detection of gait abnormalities is essential for professionals involved in the rehabilitation of walking disorders. Instrumented treadmills are spreading as an alternative to overground gait analysis. To date, the use of these instruments for recording kinematic gait parameters is still limited in clinical practice due to the lack of validation studies. This study aims to investigate the performance of a multi-sensor instrumented treadmill (i.e., WalkerView^TM, WV) for performing gait analysis. Seventeen participants performed a single gait test on the WV at three different speeds (i.e., 3 km/h, 5 km/h, and 6.6 km/h). In each trial, spatiotemporal and kinematic parameters were recorded simultaneously by the WV and by a motion capture system used as the reference. Intraclass correlation coefficient (ICC) of spatiotemporal parameters showed fair to excellent agreement at the three walking speeds for steps time, cadence, and step length (range 0.502–0.996); weaker levels of agreement were found for stance and swing time at all the tested walking speeds. Bland–Altman analysis of spatiotemporal parameters showed a mean of difference (MOD) maximum value of 0.04 s for swing/stance time and WV underestimation of 2.16 cm for step length. As for kinematic variables, ICC showed fair to excellent agreement (ICC > 0.5) for total range of motion (ROM) of hip at 3 km/h (range 0.579–0.735); weaker levels of ICC were found at 5 km/h and 6.6 km/h (range 0.219–0.447). ICC values of total knee ROM showed poor levels of agreement at all the tested walking speeds. Bland–Altman analysis of hip ROM revealed a higher MOD value at higher speeds up to 3.91°; the MOD values of the knee ROM were always higher than 7.67° with a 60° mean value of ROM. We demonstrated that the WV is a valid tool for analyzing the spatiotemporal parameters of walking and assessing the hip’s total ROM. Knee total ROM and all kinematic peak values should be carefully evaluated, having shown lower levels of agreement.

Quantitative Evaluation of Gait Changes Using APDM Inertial Sensors After the External Lumbar Drain in Patients With Idiopathic Normal Pressure Hydrocephalus

Objectives: Gait and balance disturbances are common symptoms of idiopathic normal pressure hydrocephalus (iNPH). This study aimed to quantitatively evaluate gait and balance parameters after external lumbar drainage (ELD) using APDM inertial sensors.

Methods: Two-minute walkway tests were performed in 36 patients with suspected iNPH and 20 healthy controls. A total of 36 patients underwent ELD. According to clinical outcomes, 20 patients were defined as responders, and the other 16 as non-responders. The gait parameters were documented, and the corresponding differences between responders and non-responders were calculated.

Results: When compared with healthy controls, patients with suspected iNPH exhibited decreased cadence, reduced gait speed, a higher percentage of double support, decreased elevation at mid-swing, reduced foot strike angle, shorter stride length, difficulty in turning, and impaired balance functions. After the ELD, all these manifestations, except elevation at mid-swing and balance functions, were significantly improved in responders. The change of Z-score absolute value in the six parameters, except for foot strike angle, was >1. No significant improvement was observed in non-responders.

Conclusion: APDM inertial sensors are useful for the quantitative assessment of gait impairment in patients with iNPH, which may be a valuable tool for identifying candidates that are suitable for shunting operations.

Measuring Spatiotemporal Parameters on Treadmill Walking Using Wearable Inertial System

This study aims to measure and compare spatiotemporal gait parameters in nineteen subjects using a full wearable inertial mocap system Xsens (MVN Awinda, Netherlands) and a photoelectronic system one-meter OptoGaitTM (Microgait, Italy) on a treadmill imposing a walking speed of 5 km/h. A total of eleven steps were considered for each subject constituting a dataset of 209 samples from which spatiotemporal parameters (SPT) were calculated. The step length measurement was determined using two methods. The first one considers the calculation of step length based on the inverted pendulum model, while the second considers an anthropometric approach that correlates the stature with an anthropometric coefficient. Although the absolute agreement and consistency were found for the calculation of the stance phase, cadence and gait cycle, from our study, differences in SPT were found between the two systems. Mean square error (MSE) calculation of their speed (m/s) with respect to the imposed speed on a treadmill reveals a smaller error (MSE = 0.0008) using the OptoGaitTM. Overall, our results indicate that the accurate detection of heel strike and toe-off have an influence on phases and sub-phases for the entire acquisition. Future study in this domain should investigate how to design and integrate better products and algorithms aiming to solve the problematic issues already identified in this study without limiting the user's need and performance in a different environment.

Human Gait Analysis in Neurodegenerative Diseases: a Review

This paper reviews the recent literature on technologies and methodologies for quantitative human gait analysis in the context of neurodegnerative diseases. The use of technological instruments can be of great support in both clinical diagnosis and severity assessment of these pathologies. In this paper, sensors, features and processing methodologies have been reviewed in order to provide a highly consistent work that explores the issues related to gait analysis. First, the phases of the human gait cycle are briefly explained, along with some non-normal gait patterns (gait abnormalities) typical of some neurodegenerative diseases. The work continues with a survey on the publicly available datasets principally used for comparing results. Then the paper reports the most common processing techniques for both feature selection and extraction and for classification and clustering. Finally, a conclusive discussion on current open problems and future directions is outlined.

Influences of treadmill speed and incline angle on the kinematics of the normal, osteoarthritic and prosthetic human knee

The objective of this paper is to measure and to study the influence of the treadmill speed and incline angle on the kinematics of flexion-extension angles of the human knee joints during 23 tests of walking overground and on plane and inclined treadmill performed by a sample of 14 healthy subjects and during of seven tests performed by a sample of five patients suffering of knee osteoarthritis (KOA), before and three months after the total knee replacement (TKR) surgery. The medium cycles computed and plotted for all experimental tests performed by the healthy subjects' sample and for the osteoarthritic (OA) patients' sample before and after TKR surgery are compared and conclusions are formulated.

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.

Effects of camera viewing angles on tracking kinematic gait patterns using Azure Kinect, Kinect v2 and Orbbec Astra Pro v2

BACKGROUND

Depth sensors could be a portable, affordable, marker-less alternative to three-dimension motion capture systems for gait analysis, but the effects of camera viewing angles on their joint angle tracking performance have not been fully investigated.

RESEARCH QUESTIONS

This study evaluated the accuracies of three depth sensors [Azure Kinect (AK); Kinect v2 (K2); Orbbec Astra (OA)] for tracking kinematic gait patterns during treadmill walking at five camera viewing angles (0°/22.5°/45°/67.5°/90°).

METHODS

Ten healthy subjects performed fifteen treadmill walking trials (3 speeds × 5 viewing angles) using the three depth sensors to measure joint angles in sagittal hip, frontal hip, sagittal knee, and sagittal ankle. Ten walking steps were recorded and averaged for each walking trial. Range of motion in terms of maximum and minimum joint angles measured by the depth sensors were compared with the Vicon motion capture system as the gold standard. Depth sensors tracking accuracies were compared against the Vicon reference using root-mean-square error (RMSE) on the joint angle time series. Effects of different walking speeds, viewing angles, and depth sensors on the tracking accuracy were observed using three-way repeated-measure analysis of variance (ANOVA).

RESULTS

ANOVA results on RMSE showed significant interaction effects between viewing angles and depth sensors for sagittal hip [F(8,72) = 4.404, p = 0.005] and for sagittal knee [F(8,72)=13.211, p < 0.001] joint angles. AK had better tracking performance when subjects walked at non-frontal camera viewing angles (22.5°/45°/67.5°/90°); while K2 performed better at frontal viewing angle (0°). The superior tracking performance of AK compared with K2/OA might be attributed to the improved depth sensor resolution and body tracking algorithm.

SIGNIFICANCE

Researchers should be cautious about camera viewing angle when using depth sensors for kinematic gait measurements. Our results demonstrated Azure Kinect had good tracking performance of sagittal hip and sagittal knee joint angles during treadmill walking tests at non-frontal camera viewing angles.

The Association Between Clinical Characteristics and Motor Symptom Laterality in Patients With Parkinson's Disease

Background and Purpose: The unilateral onset and persistent asymmetry of motor symptoms are important characteristics of Parkinson's disease (PD). By using scales and wearable sensors, this study explored whether motor symptom laterality could affect non-motor symptom and gait performance.

Methods: A total of 130 right-handed patients with PD were enrolled in our study and were divided into two groups according to the side of predominant motor symptom presentation by using the Unified Parkinson's Disease Rating Scale part III. We measured the non-motor symptoms with the Non-motor symptoms Scale, sleep quality with the Parkinson's Disease Sleep Scale and Pittsburgh sleep quality index, cognitive function with the Mini-mental State Examination and Montreal Cognitive Assessment, quality of life with the Parkinson's Disease Questionnaire-39, and the severity of anxiety and depression with the Hamilton Anxiety Scale and Hamilton Depression Scale, respectively. All participants underwent the instrumented stand and walk test, and gait data were collected using a set of JiBuEn gait analysis system.

Results: We observed that left-dominant symptom PD patients (LPD) were associated with a greater impairment of sleep quality than right-dominant symptom PD patients (RPD). We found no difference between LPD and RPD in terms of gait performance. However, compared with the severe asymmetry RPD patients (RPD-S), severe asymmetry LPD patients (LPD-S) showed a shorter stride length and decreased range of motion of hip joints.

Conclusions: In this study, LPD was associated with a more severe sleep-related dysfunction than RPD. In addition, LPD-S exhibited more gait impairments than RPD-S. Considering that motor symptom laterality may affect the non-motor symptom and gait performance, it should be taken into account when evaluating and treating PD patients.

Metrological Evaluation of Human-Robot Collaborative Environments Based on Optical Motion Capture Systems

In the context of human-robot collaborative shared environments, there has been an increase in the use of optical motion capture (OMC) systems for human motion tracking. The accuracy and precision of OMC technology need to be assessed in order to ensure safe human-robot interactions, but the accuracy specifications provided by manufacturers are easily influenced by various factors affecting the measurements. This article describes a new methodology for the metrological evaluation of a human-robot collaborative environment based on optical motion capture (OMC) systems. Inspired by the ASTM E3064 test guide, and taking advantage of an existing industrial robot in the production cell, the system is evaluated for mean error, error spread, and repeatability. A detailed statistical study of the error distribution across the capture area is carried out, supported by a Mann-Whitney U-test for median comparisons. Based on the results, optimal capture areas for the use of the capture system are suggested. The results of the proposed method show that the metrological characteristics obtained are compatible and comparable in quality to other methods that do not require the intervention of an industrial robot.

Evaluating the Dynamic Performance of Interfacial Pressure Sensors at a Simulated Body-Device Interface

BACKGROUND

Pressure sensing at the body-device interface can help assess the quality of fit and function of assistive devices during physical activities and movement such as walking and running. However, the dynamic performance of various pressure sensor configurations is not well established.

OBJECTIVES

Two common commercially available thin-film pressure sensors were tested to determine the effects of clinically relevant setup configurations focusing on loading areas, interfacing elements (i.e. 'puck') and calibration methods.

METHODOLOGY

Testing was performed using a customized universal testing machine to simulate dynamic, mobility relevant loads at the body-device interface. Sensor performance was evaluated by analyzing accuracy and hysteresis.

FINDINGS

The results suggest that sensor calibration method has a significant effect on sensor performance although the difference is mitigated by using an elastomeric loading puck. Both sensors exhibited similar performance during dynamic testing that agree with accuracy and hysteresis values reported by manufacturers and in previous studies assessing mainly static and quasi-static conditions.

CONCLUSION

These findings suggest that sensor performance under mobility relevant conditions may be adequately represented via static and quasi-testing testing. This is important since static testing is much easier to apply and reduces the burden on users to verify dynamic performance of sensors prior to clinical application. The authors also recommend using a load puck for dynamic testing conditions to achieve optimal performance.

Improved Single Inertial-Sensor-Based Attitude Estimation during Walking Using Velocity-Aided Observation

This paper presents a Kalman filter-based attitude estimation algorithm using a single body-mounted inertial sensor consisting of a triaxial accelerometer and triaxial gyroscope. The proposed algorithm has been developed for attitude estimation during dynamic conditions such as walking and running. Based on the repetitive properties of the velocity signal of human gait during walking, a novel velocity-aided observation is used as a measurement update for the filter. The performance has been evaluated in comparison to two standard Kalman filters with different measurement update methods and a smoother algorithm which is formulated in the form of a quadratic optimization problem. Whereas two standard Kalman filters give maximum 5 degrees in both pitch and roll error for short walking case, their performance gradually decrease with longer walking distance. The proposed algorithm shows the error of about 3 degrees in 15 m walking case, and indicate the robustness of the method with the same performance in 75 m trials. As far as the accuracy of the estimation is concerned, the proposed method achieves advantageous results due to its periodic error correction capability in both short and long walking cases at varying speeds. In addition, in terms of practicality and stability, with simple parameter settings and without the need of all-time data, the algorithm can achieve smoothing-algorithm-performance level with lower computational resources.

Design and Experimental Research of Knee Joint Prosthesis Based on Gait Acquisition Technology

Whether the lower limb prosthesis can better meet the needs of amputees, the biomimetic performance of the knee joint is particularly important. In this paper, Nokov(metric) optical 3D motion capture system was used to collect motion data of normal human lower limbs, and the motion instantaneous center of multi-gait knee joint was obtained. Taking the error of knee joint motion instantaneous center line as the objective function, a set of six-bar mechanism prosthetic knee joint was designed based on a genetic algorithm. The experimental results show that the movement trajectory of the instantaneous center of the knee joint is basically similar to that of the human knee joint, so it can help amputees complete a variety of gaits and has good biomimetic performance. Gait acquisition technology can provide important data for prosthetic designers and it will be widely used in prosthetic design and other fields.

Quantitative and Qualitative Running Gait Analysis through an Innovative Video-Based Approach

Quantitative and qualitative running gait analysis allows the early identification and the longitudinal monitoring of gait abnormalities linked to running-related injuries. A promising calibration- and marker-less video sensor-based technology (i.e., Graal), recently validated for walking gait, may also offer a time- and cost-efficient alternative to the gold-standard methods for running. This study aim was to ascertain the validity of an improved version of Graal for quantitative and qualitative analysis of running. In 33 healthy recreational runners (mean age 41 years), treadmill running at self-selected submaximal speed was simultaneously evaluated by a validated photosensor system (i.e., Optogait—the reference methodology) and by the video analysis of a posterior 30-fps video of the runner through the optimized version of Graal. Graal is video analysis software that provides a spectral analysis of the brightness over time for each pixel of the video, in order to identify its frequency contents. The two main frequencies of variation of the pixel’s brightness (i.e., F1 and F2) correspond to the two most important frequencies of gait (i.e., stride frequency and cadence). The Optogait system recorded step length, cadence, and its variability (vCAD, a traditional index of gait quality). Graal provided a direct measurement of F2 (reflecting cadence), an indirect measure of step length, and two indexes of global gait quality (harmony and synchrony index). The correspondence between quantitative indexes (Cadence vs. F2 and step length vs. Graal step length) was tested via paired t-test, correlations, and Bland–Altman plots. The relationship between qualitative indexes (vCAD vs. Harmony and Synchrony Index) was investigated by correlation analysis. Cadence and step length were, respectively, not significantly different from and highly correlated with F2 (1.41 Hz ± 0.09 Hz vs. 1.42 Hz ± 0.08 Hz, p = 0.25, r² = 0.81) and Graal step length (104.70 cm ± 013.27 cm vs. 107.56 cm ± 13.67 cm, p = 0.55, r² = 0.98). Bland–Altman tests confirmed a non-significant bias and small imprecision between methods for both parameters. The vCAD was 1.84% ± 0.66%, and it was significantly correlated with neither the Harmony nor the Synchrony Index (0.21 ± 0.03, p = 0.92, r² = 0.00038; 0.21 ± 0.96, p = 0.87, r² = 0.00122). These findings confirm the validity of the optimized version of Graal for the measurement of quantitative indexes of gait. Hence, Graal constitutes an extremely time- and cost-efficient tool suitable for quantitative analysis of running. However, its validity for qualitative running gait analysis remains inconclusive and will require further evaluation in a wider range of absolute and relative running intensities in different individuals.

Utilization of wearable technology to assess gait and mobility post-stroke: a systematic review

Background

Extremity weakness, fatigue, and postural instability often contribute to mobility deficits in persons after stroke. Wearable technologies are increasingly being utilized to track many health-related parameters across different patient populations. The purpose of this systematic review was to identify how wearable technologies have been used over the past decade to assess gait and mobility in persons with stroke.

Methods

We performed a systematic search of Ovid MEDLINE, CINAHL, and Cochrane databases using select keywords. We identified a total of 354 articles, and 13 met inclusion/exclusion criteria. Included studies were quality assessed and data extracted included participant demographics, type of wearable technology utilized, gait parameters assessed, and reliability and validity metrics.

Results

The majority of studies were performed in either hospital-based or inpatient settings. Accelerometers, activity monitors, and pressure sensors were the most commonly used wearable technologies to assess gait and mobility post-stroke. Among these devices, spatiotemporal parameters of gait that were most widely assessed were gait speed and cadence, and the most common mobility measures included step count and duration of activity. Only 4 studies reported on wearable technology validity and reliability metrics, with mixed results.

Conclusion

The use of various wearable technologies has enabled researchers and clinicians to monitor patients’ activity in a multitude of settings post-stroke. Using data from wearables may provide clinicians with insights into their patients’ lived-experiences and enrich their evaluations and plans of care. However, more studies are needed to examine the impact of stroke on community mobility and to improve the accuracy of these devices for gait and mobility assessments amongst persons with altered gait post-stroke.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12984-021-00863-x.

Work-Related Risk Assessment According to the Revised NIOSH Lifting Equation: A Preliminary Study Using a Wearable Inertial Sensor and Machine Learning

Many activities may elicit a biomechanical overload. Among these, lifting loads can cause work-related musculoskeletal disorders. Aspiring to improve risk prevention, the National Institute for Occupational Safety and Health (NIOSH) established a methodology for assessing lifting actions by means of a quantitative method based on intensity, duration, frequency and other geometrical characteristics of lifting. In this paper, we explored the machine learning (ML) feasibility to classify biomechanical risk according to the revised NIOSH lifting equation. Acceleration and angular velocity signals were collected using a wearable sensor during lifting tasks performed by seven subjects and further segmented to extract time-domain features: root mean square, minimum, maximum and standard deviation. The features were fed to several ML algorithms. Interesting results were obtained in terms of evaluation metrics for a binary risk/no-risk classification; specifically, the tree-based algorithms reached accuracies greater than 90% and Area under the Receiver operating curve characteristics curves greater than 0.9. In conclusion, this study indicates the proposed combination of features and algorithms represents a valuable approach to automatically classify work activities in two NIOSH risk groups. These data confirm the potential of this methodology to assess the biomechanical risk to which subjects are exposed during their work activity.

Monitoring Clinical Course and Treatment Response in Chronic Inflammatory Demyelinating Polyneuropathy During Routine Care: A Review of Clinical and Laboratory Assessment Measures

Importance

Identifying clinical change in many neurologic diseases, including chronic inflammatory demyelinating polyneuropathy (CIDP), can be challenging. At the same time, how change is defined heavily influences a patient's diagnostic and treatment pathway. It can be especially problematic when equivocal subjective observations are interpreted as clinically meaningful and then used to make diagnostic and treatment decisions. Change in clinical trials is strictly defined by a preselected metric, but there is a perception that formal outcomes collection during routine clinical care is neither feasible nor necessary. Given the importance placed on how change is interpreted, there is a need to select assessments that can be applied to routine care that are representative of the neurologic disease state.

Observations

For an outcome measure to be useful during clinical trials, it must have good reliability, validity, be responsive to change, and have clinical meaning. To be useful during routine clinical care, the assessment must additionally be easy to collect without the need for extensive training or equipment and should provide an immediately available result that can be rapidly quantified and interpreted. Chronic inflammatory demyelinating polyneuropathy is clinically heterogeneous and so is best evaluated with a diverse group of assessment tools. Assessing strength impairment, disability, and quality of life is ideally suited for everyday practice when caring for patients with CIDP. While electrophysiologic studies, imaging, cerebrospinal fluid, and nodal/paranodal antibodies can provide diagnostic data, they are less practical and helpful longitudinal assessment tools.

Conclusions and Relevance

Sound clinimetric outcome measures in CIDP are widely available and have the potential to help clinicians objectify treatment response and disease progression. Such data are critically important when justifying the need for ongoing or periodic immunotherapy, documenting relapse or deterioration, or providing reassurance of disease improvement, stability, or remission.