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

Efficacy of a Single-Bout of Auditory Feedback Training on Gait Performance and Kinematics in Healthy Young Adults

This study investigated the immediate effects of auditory feedback training on gait performance and kinematics in 19 healthy young adults, focusing on bilateral changes, despite unilateral training. Baseline and post-training kinematic measurements, as well as the feedback training were performed on a treadmill with a constant velocity. Significant improvements were seen in step length (trained: 590.7 mm to 611.1 mm, 95%CI [7.609, 24.373]; untrained: 591.1 mm to 628.7 mm, 95%CI [10.698, 30.835]), toe clearance (trained: 13.9 mm to 16.5 mm, 95%CI [1.284, 3.503]; untrained: 11.8 mm to 13.7 mm, 95%CI [1.763, 3.612]), ankle dorsiflexion angle at terminal stance (trained: 8.3 deg to 10.5 deg, 95%CI [1.092, 3.319]; untrained: 9.2 deg to 12.0 deg, 95%CI [1.676, 3.573]), hip flexion angular velocity, (trained: -126.5 deg/s to -131.0 deg/s, 95%CI [-9.054, -2.623]; untrained: -130.2 deg/s to -135.3 deg/s, 95%CI [-10.536, -1.675]), ankle angular velocity at terminal stance (trained: -344.7 deg/s to -359.1 deg/s, 95%CI [-47.540, -14.924]; untrained: -340.3 deg/s to -376.9 deg/s, 95%CI [-37.280, -13.166s]), and gastrocnemius EMG activity (trained: 0.60 to 0.66, 95%CI [0.014, 0.258]; untrained: 0.55 to 0.65, 95%CI [0.049, 0.214]). These findings demonstrate the efficacy of auditory feedback training in enhancing key gait parameters, highlighting the bilateral benefits from unilateral training.

Evaluation and Rehabilitation after Adult Lumbar Spine Surgery

Purpose: With an increase in the proportion of elderly patients, the global burden of spinal disease is on the rise. This is gradually expected to increase the number of surgical procedures all over the world in the near future. As we know, rehabilitation following spine surgery is critical for optimal recovery. However, the current literature lacks consensus regarding the appropriate post-operative rehabilitation protocol. The purpose of this review is to evaluate the optimal protocol for rehabilitation after lumbar spine surgery in adults. Materials and Methods: The goals of rehabilitation after lumbar spine surgery are to improve physical and psychosocial function and may include multiple modalities such as physical therapy, cognitive behavioral therapy, specialized instruments, and instructions to be followed during activities of daily living. In recent years, not only are a greater number of spine surgeries being performed, but various different techniques of lumbar spine surgery and spinal fusion have also emerged. (1) Our review summarizes post-operative rehabilitation under the following headings-1. Historical aspects, 2. Subjective functional outcomes, and (3) Actual rehabilitation measures, including balance. Results: Physical therapy programs need to be patient-specific and surgery-specific, such that they consider patient-reported outcome measures and take into consideration the technique of spinal fusion used and the muscle groups involved in these surgeries. By doing so, it is possible to assess the level of functional impairment and then specifically target the strengthening of those muscle groups affected by surgery whilst also improving impaired balance and allowing a return to daily activities. Conclusions: Rehabilitation is a multi-faceted journey to restore mobility, function, and quality of life. The current rehabilitation practice focuses on muscle strengthening, but the importance of spinal balance is less elaborated. We thus equally emphasize muscle strengthening and balance improvement post-lumbar spine surgery.

Clinical Effect Analysis of Wearable Sensor Technology-Based Gait Function Analysis in Post-Transcranial Magnetic Stimulation Stroke Patients

(1) Background: This study evaluates the effectiveness of low-frequency repetitive transcranial magnetic stimulation (LF-rTMS) in improving gait in post-stroke hemiplegic patients, using wearable sensor technology for objective gait analysis. (2) Methods: A total of 72 stroke patients were randomized into control, sham stimulation, and LF-rTMS groups, with all receiving standard medical treatment. The LF-rTMS group underwent stimulation on the unaffected hemisphere for 6 weeks. Key metrics including the Fugl-Meyer Assessment Lower Extremity (FMA-LE), Berg Balance Scale (BBS), Modified Barthel Index (MBI), and gait parameters were measured before and after treatment. (3) Results: The LF-rTMS group showed significant improvements in the FMA-LE, BBS, MBI, and various gait parameters compared to the control and sham groups (p < 0.05). Specifically, the FMA-LE scores improved by an average of 5 points (from 15 ± 3 to 20 ± 2), the BBS scores increased by 8 points (from 35 ± 5 to 43 ± 4), the MBI scores rose by 10 points (from 50 ± 8 to 60 ± 7), and notable enhancements in gait parameters were observed: the gait cycle time was reduced from 2.05 ± 0.51 s to 1.02 ± 0.11 s, the stride length increased from 0.56 ± 0.04 m to 0.97 ± 0.08 m, and the walking speed improved from 35.95 ± 7.14 cm/s to 75.03 ± 11.36 cm/s (all p < 0.001). No adverse events were reported. The control and sham groups exhibited improvements but were not as significant. (4) Conclusions: LF-rTMS on the unaffected hemisphere significantly enhances lower-limb function, balance, and daily living activities in subacute stroke patients, with the gait parameters showing a notable improvement. Wearable sensor technology proves effective in providing detailed, objective gait analysis, offering valuable insights for clinical applications in stroke rehabilitation.

Gait Impairment Analysis Using Silhouette Sinogram Signals and Assisted Knowledge Learning

The analysis of body motion is a valuable tool in the assessment and diagnosis of gait impairments, particularly those related to neurological disorders. In this study, we propose a novel automated system leveraging artificial intelligence for efficiently analyzing gait impairment from video-recorded images. The proposed methodology encompasses three key aspects. First, we generate a novel one-dimensional representation of each silhouette image, termed a silhouette sinogram, by computing the distance and angle between the centroid and each detected boundary points. This process enables us to effectively utilize relative variations in motion at different angles to detect gait patterns. Second, a one-dimensional convolutional neural network (1D CNN) model is developed and trained by incorporating the consecutive silhouette sinogram signals of silhouette frames to capture spatiotemporal information via assisted knowledge learning. This process allows the network to capture a broader context and temporal dependencies within the gait cycle, enabling a more accurate diagnosis of gait abnormalities. This study conducts training and an evaluation utilizing the publicly accessible INIT GAIT database. Finally, two evaluation schemes are employed: one leveraging individual silhouette frames and the other operating at the subject level, utilizing a majority voting technique. The outcomes of the proposed method showed superior enhancements in gait impairment recognition, with overall F1-scores of 100%, 90.62%, and 77.32% when evaluated based on sinogram signals, and 100%, 100%, and 83.33% when evaluated based on the subject level, for cases involving two, four, and six gait abnormalities, respectively. In conclusion, by comparing the observed locomotor function to a conventional gait pattern often seen in healthy individuals, the recommended approach allows for a quantitative and non-invasive evaluation of locomotion.

Vision-based motion capture for the gait analysis of neurodegenerative diseases: A review

BACKGROUND

Developments in vision-based systems and human pose estimation algorithms have the potential to detect, monitor and intervene early on neurodegenerative diseases through gait analysis. However, the gap between the technology available and actual clinical practice is evident as most clinicians still rely on subjective observational gait analysis or objective marker-based analysis that is time-consuming.

RESEARCH QUESTION

This paper aims to examine the main developments of vision-based motion capture and how such advances may be integrated into clinical practice.

METHODS

The literature review was conducted in six online databases using Boolean search terms. A commercial system search was also included. A predetermined methodological criterion was then used to assess the quality of the selected articles.

RESULTS

A total of seventeen studies were evaluated, with thirteen studies focusing on gait classification systems and four studies on gait measurement systems. Of the gait classification systems, nine studies utilized artificial intelligence-assisted techniques, while four studies employed statistical techniques. The results revealed high correlations of gait features identified by classifier models with existing clinical rating scales. These systems demonstrated generally high classification accuracies and were effective in diagnosing disease severity levels. Gait measurement systems that extract spatiotemporal and kinematic joint information from video data generally found accurate measurements of gait parameters with low mean absolute errors, high intra- and inter-rater reliability.

SIGNIFICANCE

Low cost, portable vision-based systems can provide proof of concept for the quantification of gait, expansion of gait assessment tools, remote gait analysis of neurodegenerative diseases and a point of care system for orthotic evaluation. However, certain challenges, including small sample sizes, occlusion risks, and selection bias in training models, need to be addressed. Nevertheless, these systems can serve as complementary tools, equipping clinicians with essential gait information to objectively assess disease severity and tailor personalized treatment for enhanced patient care.

BlazePose-Seq2Seq: Leveraging Regular RGB Cameras for Robust Gait Assessment

Evaluation of human gait through smartphone-based pose estimation algorithms provides an attractive alternative to costly lab-bound instrumented assessment and offers a paradigm shift with real time gait capture for clinical assessment. Systems based on smart phones, such as OpenPose and BlazePose have demonstrated potential for virtual motion assessment but still lack the accuracy and repeatability standards required for clinical viability. Seq2seq architecture offers an alternative solution to conventional deep learning techniques for predicting joint kinematics during gait. This study introduces a novel enhancement to the low-powered BlazePose algorithm by incorporating a Seq2seq autoencoder deep learning model. To ensure data accuracy and reliability, synchronized motion capture involving an RGB camera and ten Vicon cameras were employed across three distinct self-selected walking speeds. This investigation presents a groundbreaking avenue for remote gait assessment, harnessing the potential of Seq2seq architectures inspired by natural language processing (NLP) to enhance pose estimation accuracy. When comparing BlazePose alone to the combination of BlazePose and 1D convolution Long Short-term Memory Network (1D-LSTM), Gated Recurrent Unit (GRU) and Long Short-Term Memory (LSTM), the average mean absolute errors decreased from 13.4° to 5.3° for fast gait, from 16.3° to 7.5° for normal gait, and from 15.5° to 7.5° for slow gait at the left ankle joint angle respectively. The strategic utilization of synchronized data and rigorous testing methodologies further bolsters the robustness and credibility of these findings.

The Effect of a Cognitive Dual Task on Gait Parameters among Healthy Young Adults with Good and Poor Sleep Quality: A Cross-Sectional Analysis

Background: Sleep quality is known to affect automatic and executive brain functions such as gait control and cognitive processing. This study aimed to investigate the effect of dual tasks on gait spatiotemporal parameters among young adults with good and poor sleep quality. Methods: In total, 65 young adults with a mean age of 21.1 ± 2.5 were assessed for gait analysis during single-task and dual-task conditions. The participants' sleep quality was assessed using the Pittsburgh Sleep Quality Index (PSQI) and gait was assessed using the BTS Gaitlab System. The participants were asked to walk at natural speed as a single-task condition, followed by walking while performing a cognitive task as a dual-task condition. The parameters assessed included the gait velocity (m/s), cadence (steps/min), step width (m), and stride length (m). The dual-task cost (DTC) on each gait parameter was calculated. The Mann-Whitney U test was used to compare the differences in the DTC on gait variables between the good and poor sleep quality groups and the Spearman correlation test was used to assess the correlation between total PSQI scores and the DTC. Results: At a significance level of p < 0.05, a significant difference in cadence between the two sleep quality groups was observed, in addition to a positive correlation between sleep quality and the DTC effect on gait mean velocity, cadence, and stride length. Our findings also revealed a greater DTC in participants with poorer sleep quality. Conclusions: These findings contribute to our perception of the significance of sleep quality in gait performance while multitasking in younger populations.

Impact of Structural Compliance of a Six Degree of Freedom Joint Simulator on Virtual Ligament Force Calculation in Total Knee Endoprosthesis Testing

The AMTI VIVO™ six degree of freedom joint simulator allows reproducible preclinical testing of joint endoprostheses under specific kinematic and loading conditions. When testing total knee endoprosthesis, the articulating femoral and tibial components are each mounted on an actuator with two and four degrees of freedom, respectively. To approximate realistic physiological conditions with respect to soft tissues, the joint simulator features an integrated virtual ligament model that calculates the restoring forces of the ligament apparatus to be applied by the actuators. During joint motion, the locations of the ligament insertion points are calculated depending on both actuators' coordinates. In the present study, we demonstrate that unintended elastic deformations of the actuators due to the specifically high contact forces in the artificial knee joint have a considerable impact on the calculated ligament forces. This study aims to investigate the effect of this structural compliance on experimental results. While the built-in algorithm for calculating the ligament forces cannot be altered by the user, a reduction of the ligament force deviations due to the elastic deformations could be achieved by preloading the articulating implant components in the reference configuration. As a proof of concept, a knee flexion motion with varying ligament conditions was simulated on the VIVO simulator and compared to data derived from a musculoskeletal multibody model of a total knee endoprosthesis.

Accuracy, concurrent validity, and test-retest reliability of pressure-based insoles for gait measurement in chronic stroke patients

Introduction

Wearables are potentially valuable tools for understanding mobility behavior in individuals with neurological disorders and how it changes depending on health status, such as after rehabilitation. However, the accurate detection of gait events, which are crucial for the evaluation of gait performance and quality, is challenging due to highly individual-specific patterns that also vary greatly in movement and speed, especially after stroke. Therefore, the purpose of this study was to assess the accuracy, concurrent validity, and test-retest reliability of a commercially available insole system in the detection of gait events and the calculation of stance duration in individuals with chronic stroke.

Methods

Pressure insole data were collected from 17 individuals with chronic stroke during two measurement blocks, each comprising three 10-min walking tests conducted in a clinical setting. The gait assessments were recorded with a video camera that served as a ground truth, and pressure insoles as an experimental system. We compared the number of gait events and stance durations between systems.

Results and discussion

Over all 3,820 gait events, 90.86% were correctly identified by the insole system. Recall values ranged from 0.994 to 1, with a precision of 1 for all measurements. The F1 score ranged from 0.997 to 1. Excellent absolute agreement (Intraclass correlation coefficient, ICC = 0.874) was observed for the calculation of the stance duration, with a slightly longer stance duration recorded by the insole system (difference of -0.01 s). Bland-Altmann analysis indicated limits of agreement of 0.33 s that were robust to changes in walking speed. This consistency makes the system well-suited for individuals post-stroke. The test-retest reliability between measurement timepoints T1 and T2 was excellent (ICC = 0.928). The mean difference in stance duration between T1 and T2 was 0.03 s. We conclude that the insole system is valid for use in a clinical setting to quantitatively assess continuous walking in individuals with stroke.

Special Considerations in Podiatric Science: Translational Research, Cadavers, Gait Analysis, Dermatology, and Databases

The objective of this article is to provide a brief overview of the critical analysis and design of unique and perhaps less common methodologies in podiatric science. These include basic science translational designs, cadaveric investigations, gait analyses, dermatologic studies, and database analysis. The relative advantages, disadvantages, and inherent limitations are reviewed with an intention to improve the interpretation of results and advance future foot and ankle scientific endeavors.

Use of a Triaxial Accelerometer to Measure Changes in Gait Sway and Related Motor Function after Corrective Spinal Fusion Surgery for Adult Spinal Deformity

Background: Adult spinal deformity is a complex condition that causes lower back pain, causing spinal imbalance and discomfort in activities of daily life. After corrective spinal surgery, patients' gait and balance abilities might not revert to normalcy and they might be at increased risk of falling. Therefore, early evaluation of such a risk is imperative to prevent further complications such as a fall, or even worse, fractures in post-surgery ASD patients. However, there has been no report of an investigation of such early changes in gait sway before and after ASD surgery. This is a prospective to investigate changes in gait sway before and following ASD surgery, using accelerometers, and also to examine motor function related to postoperative gait sway. Methods: Twenty patients were included who underwent corrective surgery as treatment for ASD, from October 2019 to January 2023. Measurement parameters included a 10 m walking test and the timed up-and-go test (TUG), gait sway was evaluated using accelerometers (root mean square; RMS), and hip flexion and knee extension muscle strength were tested. RMS included RMS vertical: RMSV; RMS anterior posterior: RMSAP; RMS medial lateral: RMSML. The radiographic spinopelvic parameters were also evaluated preoperatively and postoperatively. p < 0.05 was noted as remarkably significant. Results: Preoperative and postoperative RMSV were 1.07 ± 0.6 and 1.31 ± 0.8, respectively (p < 0.05). RMSML significantly decreased from 0.33 ± 0.2 to 0.19 ± 0.1 postoperatively (p < 0.01). However, RMSAP did not change postoperatively (0.20 ± 0.2 vs. 0.14 ± 0.1, p > 0.05). Patients' one-month postoperative hip flexor muscle strength became significantly weaker (0.16 ± 0.04 vs. 0.10 ± 0.03 kgf/kg, p = 0.002), but TUG was maintained (11.6 ± 4.2 vs. 11.7 s, p = 0.305). RMSV was negatively correlated with quadriceps muscle strength and positively with TUG. RMSAP was negatively correlated with quadriceps muscle strength. All spinopelvic parameters became normal range after surgery. Conclusions: After corrective spinal fusion for ASD patients, the gait pattern improved significantly. Iliopsoas (hip flexor) and quadriceps femoris (knee extensor) muscles may play important roles for gait anterolateral and vertical swing, respectively.

Monitoring Activity and Gait in Children (MAGIC) using digital health technologies

BACKGROUND

Digital health technologies (DHTs) can collect gait and physical activity in adults, but limited studies have validated these in children. This study compared gait and physical activity metrics collected using DHTs to those collected by reference comparators during in-clinic sessions, to collect a normative accelerometry dataset, and to evaluate participants' comfort and their compliance in wearing the DHTs at-home.

METHODS

The MAGIC (Monitoring Activity and Gait in Children) study was an analytical validation study which enrolled 40, generally healthy participants aged 3-17 years. Gait and physical activity were collected using DHTs in a clinical setting and continuously at-home.

RESULTS

Overall good to excellent agreement was observed between gait metrics extracted with a gait algorithm from a lumbar-worn DHT compared to ground truth reference systems. Majority of participants either "agreed" or "strongly agreed" that wrist and lumbar DHTs were comfortable to wear at home, respectively, with 86% (wrist-worn DHT) and 68% (lumbar-worn DHT) wear-time compliance. Significant differences across age groups were observed in multiple gait and activity metrics obtained at home.

CONCLUSIONS

Our findings suggest that gait and physical activity data can be collected from DHTs in pediatric populations with high reliability and wear compliance, in-clinic and in home environments.

TRIAL REGISTRATION

ClinicalTrials.gov: NCT04823650 IMPACT: Digital health technologies (DHTs) have been used to collect gait and physical activity in adult populations, but limited studies have validated these metrics in children. The MAGIC study comprehensively validates the performance and feasibility of DHT-measured gait and physical activity in the pediatric population. Our findings suggest that reliable gait and physical activity data can be collected from DHTs in pediatric populations, with both high accuracy and wear compliance both in-clinic and in home environments. The identified across-age-group differences in gait and activity measurements highlighted their potential clinical value.

Anatomical leg length discrepancy in children: Can it be accurately determined using 3-D motion capturing?

BACKGROUND

Leg length discrepancy (LLD) is common in youth and is cause by several conditions. Long leg X-rays is the gold standard technique of measuring LLD. It is highly accurate and reliable compared to clinical method, but expose the subject to radiation. Instrumented Gait Analysis (IGA) serves not only as a means to measure joint kinematics during gait but also as a valuable tool for assessing Leg Length Discrepancy (LLD) while standing.

RESEARCH QUESTION

The purpose of this study was to compare different methods of determining the LLD in paediatric population. We hypothesize that IGA using joint centres is more accurate and precise than the tape measurement.

METHODS

Thirty-one patients with mean age 12.3 (SD=2.4) years were retrospectively included in the study. Their LLD varied between 0 and 36 mm. Three methods for determining LLD were compared to radiography using Bland-Altman analysis: 1. Tape measurement, 2. IGA, summarizing the distance from the spina iliaca anterior superior to the medial malleolus marker via the medial knee condyle marker. 3. IGA, summarizing distances between ankle, knee, and hip joints centres where the latter is calculated with different equations.

RESULTS

The IGA joints method performed better than the tape measurement or IGA markers method. The equations of Davis calculating the hip joint centre had the highest accuracy with mean difference to radiography of 0.7 mm (SD=6.3). The simple Harrington method resulted in a slightly reduced accuracy but higher precision 0.9 mm (SD=6.2). The Harrington method with leg length as input was less accurate 1.0 mm (SD=6.7), but was still considerably better than the tape measurement 1.8 mm (SD=7.0) or IGA markers method 1.1 mm (SD=11.5).

SIGNIFICANCE

Determining LLD with IGA using the distances between ankle, knee and hip joints centres is a feasible method that can be applied in clinical practice to calculate LLD.

The role of ankle and knee muscle characteristics in spatiotemporal gait parameters at different walking speeds: A cross-sectional study

BACKGROUND

Understanding the intricate interplay between ankle and knee muscle characteristics and their impact on gait parameters is crucial for enhancing our comprehension of human locomotion, particularly in the context of varying walking speeds among healthy young adults.

RESEARCH QUESTION

The study aimed to identify the relative importance of ankle and knee flexor and extensor muscle characteristics (e.g., strength estimated by peak torque [PT] and rate of torque development [RTD]) in the spatiotemporal gait parameters and variability in self-selected (SSWS) and fast walking speeds (FWS) in healthy young adults.

METHODS

One hundred and thirty-nine adults (75 men - 54% and 64 women - 46%; 29.04 ± 9.55 years) were assessed about their muscle characteristics (PT and RTD by an isokinetic dynamometer) and spatiotemporal gait parameters at different walking speeds (SSWS and FWS by an instrumented walkway).

RESULTS

Data analysis indicated a weak relationship between the PT and RTD of the ankle and knee and spatiotemporal gait parameters and variability in both walking conditions (SSWS: R2 0.14-0.05; FWS: R2 0.40-0.05). The strength of the knee muscles was more relevant when walking at a self-selected speed, while the strength of the ankle muscles played a more prominent role when walking at a fast pace.

SIGNIFICANCE

The findings underscore the critical role of ankle muscles (plantar and dorsiflexors) at fast walking speeds. Therefore, targeted interventions for strength and optimization of these muscles are paramount.

Vascular risk, gait, behavioral, and plasma indicators of VCID

INTRODUCTION

Cost-effective screening tools for vascular contributions to cognitive impairment and dementia (VCID) has significant implications. We evaluated non-imaging indicators of VCID using magnetic resonance imaging (MRI)-measured white matter (WM) damage and hypothesized that these indicators differ based on age.

METHODS

In 745 participants from the Mayo Clinic Study of Aging (≥50 years of age) with serial WM assessments from diffusion MRI and fluid-attenuated inversion recovery (FLAIR)-MRI, we examined associations between baseline non-imaging indicators (demographics, vascular risk factors [VRFs], gait, behavioral, plasma glial fibrillary acidic protein [GFAP], and plasma neurofilament light chain [NfL]) and WM damage across three age tertiles.

RESULTS

VRFs and gait were associated with diffusion changes even in low age strata. All measures (VRFs, gait, behavioral, plasma GFAP, plasma NfL) were associated with white matter hyperintensities (WMHs) but mainly in intermediate and high age strata.

DISCUSSION

Non-imaging indicators of VCID were related to WM damage and may aid in screening participants and assessing outcomes for VCID.

HIGHLIGHTS

Non-imaging indicators of VCID can aid in prediction of MRI-measured WM damage but their importance differed by age. Vascular risk and gait measures were associated with early VCID changes measured using diffusion MRI. Plasma markers explained variability in WMH across age strata. Most non-imaging measures explained variability in WMH and vascular WM scores in intermediate and older age groups. The framework developed here can be used to evaluate new non-imaging VCID indicators proposed in the future.

Factors influencing the clinical adoption of quantitative gait analysis technology with a focus on clinical efficacy and clinician perspectives: A scoping review

INTRODUCTION

Quantitative gait analysis (QGA) has the potential to support clinician decision-making. However, it is not yet widely accepted in practice. Evidence for clinical efficacy (i.e., efficacy and effectiveness), as well as a users' perspective on using the technology in clinical practice (e.g., ease of use and usefulness) can help impact their widespread adoption.

OBJECTIVE

To synthesize the literature on the clinical efficacy and clinician perspectives on the use of gait analysis technologies in the clinical care of adult populations.

METHODS

This scoping review followed the Joanna Briggs Institute (JBI) methodology for scoping reviews. We included peer-reviewed and gray literature (i.e., conference abstracts). A search was conducted in MEDLINE (Ovid), CENTRAL (Ovid), EMBASE (Ovid), CINAHL (EBSCO) and SPORTDiscus (EBSCO). Included full-text studies were critically appraised using the JBI critical appraisal tools.

RESULTS

A total of 15 full-text studies and two conference abstracts were included in this review. Results suggest that QGA technologies can influence decision-making with some evidence to suggest their role in improving patient outcomes. The main barrier to ease of use was a clinician's lack of data expertise, and main facilitator was receiving support from staff. Barriers to usefulness included challenges finding suitable reference data and data accuracy, while facilitators were enhancing patient care and supporting clinical decision-making.

SIGNIFICANCE

This review is the first step to understanding how QGA technologies can optimize clinical practice. Many gaps in the literature exist and reveal opportunities to improve the clinical adoption of gait analysis technologies. Further research is needed in two main areas: 1) examining the clinical efficacy of gait analysis technologies and 2) gathering clinician perspectives using a theoretical model like the Technology Acceptance Model to guide study design. Results will inform research aimed at evaluating, developing, or implementing these technologies.

FUNDING

This work was supported by the Walter and Maria Schroeder Institute for Brain Innovation and Recovery and AGE-WELL Graduate Student Award in Technology and Aging [2021,2022].

Kinematic Analysis of Human Gait in Healthy Young Adults Using IMU Sensors: Exploring Relevant Machine Learning Features for Clinical Applications

BACKGROUND

Gait is the manner or style of walking, involving motor control and coordination to adapt to the surrounding environment. Knowing the kinesthetic markers of normal gait is essential for the diagnosis of certain pathologies or the generation of intelligent ortho-prostheses for the treatment or prevention of gait disorders. The aim of the present study was to identify the key features of normal human gait using inertial unit (IMU) recordings in a walking test.

METHODS

Gait analysis was conducted on 32 healthy participants (age range 19-29 years) at speeds of 2 km/h and 4 km/h using a treadmill. Dynamic data were obtained using a microcontroller (Arduino Nano 33 BLE Sense Rev2) with IMU sensors (BMI270). The collected data were processed and analyzed using a custom script (MATLAB 2022b), including the labeling of the four relevant gait phases and events (Stance, Toe-Off, Swing, and Heel Strike), computation of statistical features (64 features), and application of machine learning techniques for classification (8 classifiers).

RESULTS

Spider plot analysis revealed significant differences in the four events created by the most relevant statistical features. Among the different classifiers tested, the Support Vector Machine (SVM) model using a Cubic kernel achieved an accuracy rate of 92.4% when differentiating between gait events using the computed statistical features.

CONCLUSIONS

This study identifies the optimal features of acceleration and gyroscope data during normal gait. The findings suggest potential applications for injury prevention and performance optimization in individuals engaged in activities involving normal gait. The creation of spider plots is proposed to obtain a personalised fingerprint of each patient's gait fingerprint that could be used as a diagnostic tool. A deviation from a normal gait pattern can be used to identify human gait disorders. Moving forward, this information has potential for use in clinical applications in the diagnosis of gait-related disorders and developing novel orthoses and prosthetics to prevent falls and ankle sprains.

Quantitative Gait and Balance Outcomes for Ataxia Trials: Consensus Recommendations by the Ataxia Global Initiative Working Group on Digital-Motor Biomarkers

With disease-modifying drugs on the horizon for degenerative ataxias, ecologically valid, finely granulated, digital health measures are highly warranted to augment clinical and patient-reported outcome measures. Gait and balance disturbances most often present as the first signs of degenerative cerebellar ataxia and are the most reported disabling features in disease progression. Thus, digital gait and balance measures constitute promising and relevant performance outcomes for clinical trials.This narrative review with embedded consensus will describe evidence for the sensitivity of digital gait and balance measures for evaluating ataxia severity and progression, propose a consensus protocol for establishing gait and balance metrics in natural history studies and clinical trials, and discuss relevant issues for their use as performance outcomes.

Validity and reliability of the DANU sports system for walking and running gait assessment

Objective. Gait assessments have traditionally been analysed in laboratory settings, but this may not reflect natural gait. Wearable technology may offer an alternative due to its versatility. The purpose of the study was to establish the validity and reliability of temporal gait outcomes calculated by the DANU sports system, against a 3D motion capture reference system.Approach. Forty-one healthy adults (26 M, 15 F, age 36.4 ± 11.8 years) completed a series of overground walking and jogging trials and 60 s treadmill walking and running trials at various speeds (8-14 km hr-1), participants returned for a second testing session to repeat the same testing.Main results. For validity, 1406 steps and 613 trials during overground and across all treadmill trials were analysed respectively. Temporal outcomes generated by the DANU sports system included ground contact time, swing time and stride time all demonstrated excellent agreement compared to the laboratory reference (intraclass correlation coefficient (ICC) > 0.900), aside from ground contact time during overground jogging which had good agreement (ICC = 0.778). For reliability, 666 overground and 511 treadmill trials across all speeds were examined. Test re-test agreement was excellent for all outcomes across treadmill trials (ICC > 0.900), except for swing time during treadmill walking which had good agreement (ICC = 0.886). Overground trials demonstrated moderate to good test re-test agreement (ICC = 0.672-0.750), which may be due to inherent variability of self-selected (rather than treadmill set) pacing between sessions.Significance. Overall, this study showed that temporal gait outcomes from the DANU Sports System had good to excellent validity and moderate to excellent reliability in healthy adults compared to an established laboratory reference.

A Systematic Review of Gait Analysis in the Context of Multimodal Sensing Fusion and AI

BACKGROUND

Neurological diseases are a leading cause of disability and mortality. Gait, or human walking, is a significant predictor of quality of life, morbidity, and mortality. Gait patterns and other kinematic, kinetic, and balance gait features are accurate and powerful diagnostic and prognostic tools.

OBJECTIVE

This review article focuses on the applicability of gait analysis using fusion techniques and artificial intelligence (AI) models. The aim is to examine the significance of mixing several types of wearable and non-wearable sensor data and the impact of this combination on the performance of AI models.

METHOD

In this systematic review, 66 studies using more than two modalities to record and analyze gait were identified. 40 studies incorporated multiple gait analysis modalities without the use of artificial intelligence to extract gait features such as kinematic, kinetic, margin of stability, temporal, and spatial gait parameters, as well as cerebral activity. Similarly, 26 studies analyzed gait data using multimodal fusion sensors and AI algorithms.

RESULTS

The research summarized here demonstrates that the quality of gait analysis and the effectiveness of AI models can both benefit from the integration of data from many sensors. Meanwhile, the utilization of EMG signals in fusion data is especially advantageous.

CONCLUSION

The findings of this review suggest that a smart, portable, wearable-based gait and balance assessment system can be developed using multimodal sensing of the most cutting-edge, clinically relevant tools and technology available. The information presented in this article may serve as a vital springboard for such development.

Pharmacological targeting of coagulation factor XI attenuates experimental autoimmune encephalomyelitis in mice

Multiple sclerosis (MS) is the most common causes of non-traumatic disability in young adults worldwide. MS pathophysiologies include the formation of inflammatory lesions, axonal damage and demyelination, and blood brain barrier (BBB) disruption. Coagulation proteins, including factor (F)XII, can serve as important mediators of the adaptive immune response during neuroinflammation. Indeed, plasma FXII levels are increased during relapse in relapsing-remitting MS patients, and previous studies showed that reducing FXII levels was protective in a murine model of MS, experimental autoimmune encephalomyelitis (EAE). Our objective was to determine if pharmacological targeting of FXI, a major substrate of activated FXII (FXIIa), improves neurological function and attenuates CNS damage in the setting of EAE. EAE was induced in male mice using murine myelin oligodendrocyte glycoprotein peptides combined with heat-inactivated Mycobacterium tuberculosis and pertussis toxin. Upon onset of symptoms, mice were treated every other day intravenously with anti-FXI antibody, 14E11, or saline. Disease scores were recorded daily until euthanasia for ex vivo analyses of inflammation. Compared to the vehicle control, 14E11 treatment reduced the clinical severity of EAE and total mononuclear cells, including CD11b+CD45high macrophage/microglia and CD4+ T cell numbers in brain. Following pharmacological targeting of FXI, BBB disruption was reduced, as measured by decreased axonal damage and fibrin(ogen) accumulation in the spinal cord. These data demonstrate that pharmacological inhibition of FXI reduces disease severity, immune cell migration, axonal damage, and BBB disruption in mice with EAE. Thus, therapeutic agents targeting FXI and FXII may provide a useful approach for treating autoimmune and neurologic disorders.

Gait Analysis, Metabolic Parameters and Adherence to the Mediterranean Diet in Patients with Type 2 Diabetes Mellitus Compared with Healthy Controls: A Pilot Study

BACKGROUND AND PURPOSE

Patients with type 2 diabetes mellitus (T2DM) are prone to developing diabetic peripheral neuropathy (DPN) with an increased risk of injuries while walking, potentially leading to plantar ulcers. We aimed to assess the early gait changes in T2DM patients without clinical signs of DPN in comparison to age-matched healthy controls (HC).

SUBJECTS AND METHODS

One hundred T2DM patients (78 women, mean age: 66.4 ± 11.5 years) and 50 age-matched HC (34 women, mean age 62.1 ± 7.9 years) were evaluated with the PODOSmart® gait analysis device. Anthropometric and biochemical data, as well as dietary habits were collected for all participants. T2DM patients also completed the Diabetes Distress (DS) self-report validated questionnaire.

RESULTS

One patient was excluded from the study due to lack of recent biochemical data. Among the T2DM patients, 88.9% reported little or no DS and 11.1% moderate DS. The T2DM group had higher body mass index, waist circumference, systolic blood pressure, glycated hemoglobin A1c, sodium, white blood cell count, triglycerides and low-density lipoprotein cholesterol, but lower high-density lipoprotein cholesterol than HC (p < 0.05 for all comparisons). The MedDiet score was satisfactory in both groups (p > 0.05). Significant differences were found between the two study groups in gaitline heel off, propulsion speed, foot progression angle, time taligrade phase, stride length, walking speed, angle attack, oscillation speed, pronation-supination toe off and clearance.

CONCLUSIONS

The T2DM patients without self-reported DS or clinical signs of DPN may exhibit significant differences in several gait parameters analyzed with PODOSmart®. Whether gait analysis can be used as an early diagnostic tool of T2DM complications should be further explored.

Clinical Gait Analysis: Characterizing Normal Gait and Pathological Deviations Due to Neurological Diseases

This study addresses the characterization of normal gait and pathological deviations induced by neurological diseases, considering knee angular kinematics in the sagittal plane. We propose an unsupervised approach based on Dynamic Time Warping (DTW) to identify different normal gait profiles (NGPs) corresponding to real cycles representing the overall behavior of healthy subjects, instead of considering an average reference, as done in the literature. The obtained NGPs are then used to measure the deviations of pathological gait cycles from normal gait with DTW. Hierarchical Clustering is applied to stratify deviations into clusters. Results show that three NGPs are necessary to finely characterize the heterogeneity of normal gait and accurately quantify pathological deviations. In particular, we automatically identify which lower limb is affected for Hemiplegic patients and characterize the severity of motor impairment for Paraplegic patients. Concerning Tetraplegic patients, different profiles appear in terms of impairment severity. These promising results are obtained by considering the raw description of gait signals. Indeed, we have shown that normalizing signals removes the temporal properties of signals, inducing a loss of dynamic information that is crucial for accurately measuring pathological deviations. Our methodology could be exploited to quantify the impact of therapies on gait rehabilitation.

Gait Analysis in Neurorehabilitation: From Research to Clinical Practice

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

Synergizing Intelligence and Building a Smarter Future: Artificial Intelligence Meets Bioengineering

Smart Engineering (SE) describes the methods, processes, and IT tools for the interdisciplinary, system-oriented development of innovative, intelligent, networked products, production plants, and infrastructures [...].

Base of Support, Step Length and Stride Width Estimation during Walking Using an Inertial and Infrared Wearable System

The analysis of the stability of human gait may be effectively performed when estimates of the base of support are available. The base of support area is defined by the relative position of the feet when they are in contact with the ground and it is closely related to additional parameters such as step length and stride width. These parameters may be determined in the laboratory using either a stereophotogrammetric system or an instrumented mat. Unfortunately, their estimation in the real world is still an unaccomplished goal. This study aims at proposing a novel, compact wearable system, including a magneto-inertial measurement unit and two time-of-flight proximity sensors, suitable for the estimation of the base of support parameters. The wearable system was tested and validated on thirteen healthy adults walking at three self-selected speeds (slow, comfortable, and fast). Results were compared with the concurrent stereophotogrammetric data, used as the gold standard. The root mean square errors for the step length, stride width and base of support area varied from slow to high speed between 10-46 mm, 14-18 mm, and 39-52 cm², respectively. The mean overlap of the base of support area as obtained with the wearable system and with the stereophotogrammetric system ranged between 70% and 89%. Thus, this study suggested that the proposed wearable solution is a valid tool for the estimation of the base of support parameters out of the laboratory.