Clinical Feasibility of a Markerless Gait Analysis System.

Background: The gait analysis method that has been used in clinical practice to date is an optical tracking system (OTS) using a marker, but a markerless gait analysis (MGA) system is being developed because of the expensive cost and complicated examination of the OTS. To apply this MGA clinically, a comparative study of the MGA and OTS methods is necessary. The purpose of this study was to evaluate the compatibility between the OTS and the MGA methods and to evaluate the usefulness of the MGA system in actual clinical settings. Methods: From March 2021 to August 2021, 14 patients underwent gait analysis using the OTS and MGA system, and the spatiotemporal parameters and kinematic results obtained by the 2 methods were compared. To evaluate the practicality of the MGA system in an actual clinical setting, MGA was performed on 14 symptomatic children with idiopathic toe walking, who had been treated with a corrective cast, and the pre-cast and post-cast results were compared. For the OTS, the Motion Analysis Eagle system was used, and for MGA, DH Walk was used. Results: The spatiotemporal parameters showed no significant difference between the OTS and MGA system. The joint angle graphs of the kinematics along the sagittal plane showed similar shapes as a whole, with particularly high correlations in the hip and knee (pelvis: 29.4%, hip joint: 96.7%, knee joint: 94.9%, and ankle joint: 68.5%). A quantified comparison using the CORrelation and Analysis (CORA) score also showed high similarity between the 2 methods. The MGA results of pre-cast application and post-cast removal for children with idiopathic toe walking showed a statistically significant improvement in ankle dorsiflexion after treatment (p < 0.001). Conclusions: MGA showed a good correlation with the conventional OTS in terms of spatiotemporal parameters and kinematics. We demonstrated that ankle sagittal kinematics improved after treatment by corrective cast in children with idiopathic toe walking using the MGA method. Thus, after the improvement of a few limitations, the MGA system may soon be able to be clinically applied.

Change of Segmental Motion Following Total Ankle Arthroplasty Using a 3-Dimensional Multi-segment Foot Model.

Background: Total ankle arthroplasty (TAA) enhances patients' subjective outcomes with respect to pain and function. The aim of this study was to analyze the biomechanical changes of the affected limb following TAA using gait analysis with a 3-dimensional multi-segment foot model (3D MFM). Methods: We reviewed medical records, simple radiographs, and gait analyses using a 3D MFM of patients who underwent TAA for severe varus ankle arthritis. Preoperative and postoperative gait data of 24 patients were compared. Postoperative gait analyses were done at least 1 year after surgery. Results: TAA significantly increased stride length (p = 0.024). The total range of motion of all planes in the hindfoot and forefoot showed no significant changes between preoperative and postoperative states. Hindfoot was significantly plantarflexed and pronated after TAA, while forefoot was significantly supinated in all phases. After appropriate calculations, the genuine coronal motion of the hindfoot showed no changes after TAA in all phases. Conclusions: TAA did not result in biomechanical improvements of segmental motions in the forefoot and hindfoot, except for changes to the bony structures. Therefore, it is important to point out to patients that TAA will not result in significant improvement of ankle function and range of motion. Clinicians can consider this information during preoperative counseling.

Evaluation of forelimb gait variation overground at a walk in sound and lame dogs using a combination of diagnostic techniques.

BACKGROUND: Kinetic and kinematic gait analysis is increasingly practised as a part of lameness evaluation in dogs. The aim of this study was to examine the normal short- and long-term variation in forelimb gait in sound control dogs (CD) at a walk using seven selected variables of objective kinetic and kinematic gait analyses. Also, to compare the findings in CD to a group of forelimb lame dogs with elbow osteoarthritis (OAD). An additional aim was to test a kinetic based graphic method for lameness detection; symmetry squares (SS). A prospective longitudinal study was carried out on client owned CD and OAD. Clinical and orthopaedic evaluations were performed to ensure soundness and detect and grade lameness. Seven kinetic and kinematic variables and SS were tested for lameness evaluation. The CD were divided into two subgroups, CD1 and CD2, and examined twice: CD1 with two months interval and CD2 with 3-4 h interval. The OAD group was evaluated once and compared to the CD groups' first examination. RESULTS: Thirteen CD and 19 OAD were included. For CD1 and CD2, there were no significant differences in any examined variable between examination occasions. Total peak force/impulse symmetry and fore-hind peak force/impulse symmetry differed significantly between OAD and CD. Symmetry squares had a 74% agreement to subjective orthopaedic evaluations. CONCLUSIONS: In CD, no difference in the examined variables was seen between examination occasions. Four out of seven objective variables differed significantly between CD and OAD. The graphic SS method might have diagnostic potential for lameness detection, making it possible to detect a shift from lame to non-lame limbs. Potentially, this might be especially helpful in bilaterally lame dogs, which often represent a clinical challenge in lameness evaluation.

Gait characterization in rare bone diseases in a real-world environment - A comparative controlled study.

BACKGROUND: Rare bone diseases (RBD) cause physical and sensory disability that affects quality of life. Mobility challenges are common for people with RBDs, and travelling to gait analysis labs can be very complex. Smartphone sensors could provide remote monitoring. RESEARCH QUESTION: This study aimed to search for and identify variables that can be used to discriminate between people with RBD and healthy people by using built-in smartphone sensors in a real-world setting. METHODS: In total, 18 participants (healthy: n=9; RBD: n=9), controlled by age and sex, were included in this cross-sectional study. A freely available App (Phyphox) was used to gather data from built-in smartphone sensors (accelerometer & gyroscope) at 60 Hz during a 15-min walk on a level surface without turns or stops. Temporal gait parameters like cadence, mean stride time and, coefficient variance (CoVSt) and nonlinear analyses, as the largest Lyapunov exponent (LLE) & sample entropy (SE) in the three accelerometer axes were used to distinguish between the groups and describe gait patterns. RESULTS: The LLE (p=0.04) and the SE of the z-axis (p=0.01), which are correlated with balance control during walking and regularity of the gait, are sufficiently sensitive to distinguish between RBD and controls. SIGNIFICANCE: The use of smartphone sensors to monitor gait in people with RBD allows for the identification of subtle changes in gait patterns, which can be used to inform assessment and management strategies in larger cohorts.

Automatic gait events detection with inertial measurement units: healthy subjects and moderate to severe impaired patients.

BACKGROUND: Recently, the use of inertial measurement units (IMUs) in quantitative gait analysis has been widely developed in clinical practice. Numerous methods have been developed for the automatic detection of gait events (GEs). While many of them have achieved high levels of efficiency in healthy subjects, detecting GEs in highly degraded gait from moderate to severely impaired patients remains a challenge. In this paper, we aim to present a method for improving GE detection from IMU recordings in such cases. METHODS: We recorded 10-meter gait IMU signals from 13 healthy subjects, 29 patients with multiple sclerosis, and 21 patients with post-stroke equino varus foot. An instrumented mat was used as the gold standard. Our method detects GEs from filtered acceleration free from gravity and gyration signals. Firstly, we use autocorrelation and pattern detection techniques to identify a reference stride pattern. Next, we apply multiparametric Dynamic Time Warping to annotate this pattern from a model stride, in order to detect all GEs in the signal. RESULTS: We analyzed 16,819 GEs recorded from healthy subjects and achieved an F1-score of 100%, with a median absolute error of 8 ms (IQR [3-13] ms). In multiple sclerosis and equino varus foot cohorts, we analyzed 6067 and 8951 GEs, respectively, with F1-scores of 99.4% and 96.3%, and median absolute errors of 18 ms (IQR [8-39] ms) and 26 ms (IQR [12-50] ms). CONCLUSIONS: Our results are consistent with the state of the art for healthy subjects and demonstrate a good accuracy in GEs detection for pathological patients. Therefore, our proposed method provides an efficient way to detect GEs from IMU signals, even in degraded gaits. However, it should be evaluated in each cohort before being used to ensure its reliability.

Estimation of minimum foot clearance using a single foot-mounted inertial sensor and personalized foot geometry scan.

The real-world measurement of minimum foot clearance (mFC) during the swing phase of gait is critical in efforts to understand and reduce the risk of trip-and-fall incidents in populations with gait impairments. Past research has focused on measuring clearance of a single point on a person's foot, typically the toe-however, this may overestimate mFC and may even be the wrong region of the foot in cases of gait impairments or interventions. In this work, we present a novel method to reconstruct the swing-phase trajectory of an arbitrary number of points on a person's shoe and estimate the instantaneous height and location of whole-foot mFC. This is achieved using a single foot-mounted inertial sensor and personalized shoe geometry scan, assuming a rigid-body IMU-shoe system. This combination allows collection and analysis using out-of-lab tests, potentially including clinical environments. Validation of single marker location using the proposed method vs. motion capture showed height errors with bias less than 0.05 mm, and 95% confidence interval of - 8.18 to + 8.09 mm. The method is demonstrated in an example data set comparing different interventions for foot drop, and it shows clear differences among no intervention, functional electrical stimulation, and ankle-foot orthosis conditions. This method offers researchers and clinicians a rich understanding of a person's gait by providing objective 3D foot kinematics and allowing a unique opportunity to view the regions of the foot where minimum clearance occurs. This information can contribute to a more informed recommendation of specific interventions or assistive technology than is currently possible in standard clinical practice.

Development of a Low-Cost Markerless Optical Motion Capture System for Gait Analysis and Anthropometric Parameter Quantification.

Technological advancements have expanded the range of methods for capturing human body motion, including solutions involving inertial sensors (IMUs) and optical alternatives. However, the rising complexity and costs associated with commercial solutions have prompted the exploration of more cost-effective alternatives. This paper presents a markerless optical motion capture system using a RealSense depth camera and intelligent computer vision algorithms. It facilitates precise posture assessment, the real-time calculation of joint angles, and acquisition of subject-specific anthropometric data for gait analysis. The proposed system stands out for its simplicity and affordability in comparison to complex commercial solutions. The gathered data are stored in comma-separated value (CSV) files, simplifying subsequent analysis and data mining. Preliminary tests, conducted in controlled laboratory environments and employing a commercial MEMS-IMU system as a reference, revealed a maximum relative error of 7.6% in anthropometric measurements, with a maximum absolute error of 4.67 cm at average height. Stride length measurements showed a maximum relative error of 11.2%. Static joint angle tests had a maximum average error of 10.2%, while dynamic joint angle tests showed a maximum average error of 9.06%. The proposed optical system offers sufficient accuracy for potential application in areas such as rehabilitation, sports analysis, and entertainment.

Assessment of Gait Patterns during Crutch Assisted Gait through Spatial and Temporal Analysis.

The use of crutches is a common method of assisting people during recovery from musculoskeletal injuries in the lower limbs. There are several different ways to walk with crutches depending on the patient's needs. The structure of crutch gaits or crutch gait patterns varies based on the delay between the aid and foot placement, the number of concurrent points of contact, and laterality. In a rehabilitation process, the prescribed pattern may differ according to the injury, the treatment and the individual's condition. Clinicians may improve diagnosis, assessment, training, and treatment by monitoring and analyzing gait patterns. This study aimed to assess and characterize four crutch walking patterns using spatial and temporal parameters obtained from the instrumented crutches. For this purpose, 27 healthy users performed four different gait patterns over multiple trials. Each trial was recorded using a portable system integrated into the crutches, which measured force, position, and acceleration. Based on the data angle, an algorithm was developed to segment the trials into gait cycles and identify gait phases. The next step was to determine the most appropriate metrics to describe each gait pattern. Several metrics were used to analyze the collected data, including force, acceleration, angle, and stride time. Among 27 participants, significant differences were found between crutch gait patterns. Through the use of these spatial and temporal parameters, promising results were obtained for monitoring assisted gait with crutches. Furthermore, the results demonstrated the possibility of using instrumented crutches as a clinical tool.

The Reliability and Validity of the OneStep Smartphone Application for Gait Analysis among Patients Undergoing Rehabilitation for Unilateral Lower Limb Disability.

An easy-to-use and reliable tool is essential for gait assessment of people with gait pathologies. This study aimed to assess the reliability and validity of the OneStep smartphone application compared to the C-Mill-VR+ treadmill (Motek, Nederlands), among patients undergoing rehabilitation for unilateral lower extremity disability. Spatiotemporal gait parameters were extracted from the treadmill and from two smartphones, one on each leg. Inter-device reliability was evaluated using Pearson correlation, intra-cluster correlation coefficient (ICC), and Cohen's d, comparing the application's readings from the two phones. Validity was assessed by comparing readings from each phone to the treadmill. Twenty-eight patients completed the study; the median age was 45.5 years, and 61% were males. The ICC between the phones showed a high correlation (r = 0.89-1) and good-to-excellent reliability (ICC range, 0.77-1) for all the gait parameters examined. The correlations between the phones and the treadmill were mostly above 0.8. The ICC between each phone and the treadmill demonstrated moderate-to-excellent validity for all the gait parameters (range, 0.58-1). Only 'step length of the impaired leg' showed poor-to-good validity (range, 0.37-0.84). Cohen's d effect size was small (d < 0.5) for all the parameters. The studied application demonstrated good reliability and validity for spatiotemporal gait assessment in patients with unilateral lower limb disability.

Optimizing Rare Disease Gait Classification through Data Balancing and Generative AI: Insights from Hereditary Cerebellar Ataxia.

The interpretability of gait analysis studies in people with rare diseases, such as those with primary hereditary cerebellar ataxia (pwCA), is frequently limited by the small sample sizes and unbalanced datasets. The purpose of this study was to assess the effectiveness of data balancing and generative artificial intelligence (AI) algorithms in generating synthetic data reflecting the actual gait abnormalities of pwCA. Gait data of 30 pwCA (age: 51.6 ± 12.2 years; 13 females, 17 males) and 100 healthy subjects (age: 57.1 ± 10.4; 60 females, 40 males) were collected at the lumbar level with an inertial measurement unit. Subsampling, oversampling, synthetic minority oversampling, generative adversarial networks, and conditional tabular generative adversarial networks (ctGAN) were applied to generate datasets to be input to a random forest classifier. Consistency and explainability metrics were also calculated to assess the coherence of the generated dataset with known gait abnormalities of pwCA. ctGAN significantly improved the classification performance compared with the original dataset and traditional data augmentation methods. ctGAN are effective methods for balancing tabular datasets from populations with rare diseases, owing to their ability to improve diagnostic models with consistent explainability.

Accuracy, Validity, and Reliability of Markerless Camera-Based 3D Motion Capture Systems versus Marker-Based 3D Motion Capture Systems in Gait Analysis: A Systematic Review and Meta-Analysis.

(1) Background: Marker-based 3D motion capture systems (MBS) are considered the gold standard in gait analysis. However, they have limitations for which markerless camera-based 3D motion capture systems (MCBS) could provide a solution. The aim of this systematic review and meta-analysis is to compare the accuracy, validity, and reliability of MCBS and MBS. (2) Methods: A total of 2047 papers were systematically searched according to PRISMA guidelines on 7 February 2024, in two different databases: Pubmed (1339) and WoS (708). The COSMIN-tool and EBRO guidelines were used to assess risk of bias and level of evidence. (3) Results: After full text screening, 22 papers were included. Spatiotemporal parameters showed overall good to excellent accuracy, validity, and reliability. For kinematic variables, hip and knee showed moderate to excellent agreement between the systems, while for the ankle joint, poor concurrent validity and reliability were measured. The accuracy and concurrent validity of walking speed were considered excellent in all cases, with only a small bias. The meta-analysis of the inter-rater reliability and concurrent validity of walking speed, step time, and step length resulted in a good-to-excellent intraclass correlation coefficient (ICC) (0.81; 0.98). (4) Discussion and conclusions: MCBS are comparable in terms of accuracy, concurrent validity, and reliability to MBS in spatiotemporal parameters. Additionally, kinematic parameters for hip and knee in the sagittal plane are considered most valid and reliable but lack valid and accurate measurement outcomes in transverse and frontal planes. Customization and standardization of methodological procedures are necessary for future research to adequately compare protocols in clinical settings, with more attention to patient populations.

Unobtrusive measurement of gait parameters using seismographs: An observational study.

Analyzing irregularities in walking patterns helps detect human locomotion abnormalities that can signal health changes. Traditional observation-based assessments have limitations due to subjective biases and capture only a single time point. Ambient and wearable sensor technologies allow continuous and objective locomotion monitoring but face challenges due to the need for specialized expertise and user compliance. This work proposes a seismograph-based algorithm for quantifying human gait, incorporating a step extraction algorithm derived from mathematical morphologies, with the goal of achieving the accuracy of clinical reference systems. To evaluate our method, we compared the gait parameters of 50 healthy participants, as recorded by seismographs, and those obtained from reference systems (a pressure-sensitive walkway and a camera system). Participants performed four walking tests, including traversing a walkway and completing the timed up-and-go (TUG) test. In our findings, we observed linear relationships with strong positive correlations (R2 > 0.9) and tight 95% confidence intervals for all gait parameters (step time, cycle time, ambulation time, and cadence). We demonstrated that clinical gait parameters and TUG mobility test timings can be accurately derived from seismographic signals, with our method exhibiting no significant differences from established clinical reference systems.

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.

A Step Forward Understanding Directional Limitations in Markerless Smartphone-Based Gait Analysis: A Pilot Study.

The progress in markerless technologies is providing clinicians with tools to shorten the time of assessment rapidly, but raises questions about the potential trade-off in accuracy compared to traditional marker-based systems. This study evaluated the OpenCap system against a traditional marker-based system-Vicon. Our focus was on its performance in capturing walking both toward and away from two iPhone cameras in the same setting, which allowed capturing the Timed Up and Go (TUG) test. The performance of the OpenCap system was compared to that of a standard marker-based system by comparing spatial-temporal and kinematic parameters in 10 participants. The study focused on identifying potential discrepancies in accuracy and comparing results using correlation analysis. Case examples further explored our results. The OpenCap system demonstrated good accuracy in spatial-temporal parameters but faced challenges in accurately capturing kinematic parameters, especially in the walking direction facing away from the cameras. Notably, the two walking directions observed significant differences in pelvic obliquity, hip abduction, and ankle flexion. Our findings suggest areas for improvement in markerless technologies, highlighting their potential in clinical settings.

The applicability of markerless motion capture for clinical gait analysis in children with cerebral palsy.

The aim of this comparative, cross-sectional study was to determine whether markerless motion capture can track deviating gait patterns in children with cerebral palsy (CP) to a similar extent as marker-based motion capturing. Clinical gait analysis (CGA) was performed for 30 children with spastic CP and 15 typically developing (TD) children. Marker data were processed with the Human Body Model and video files with Theia3D markerless software, to calculate joint angles for both systems. Statistical parametric mapping paired t-tests were used to compare the trunk, pelvis, hip, knee and ankle joint angles, for both TD and CP, as well as for the deviation from the norm in the CP group. Individual differences were quantified using mean absolute differences. Markerless motion capture was able to track frontal plane angles and sagittal plane knee and ankle angles well, but individual deviations in pelvic tilt and transverse hip rotation as present in CP were not captured by the system. Markerless motion capture is a promising new method for CGA in children with CP, but requires improvement to better capture several clinically relevant deviations especially in pelvic tilt and transverse hip rotation.

Effect of stiffness-optimized ankle foot orthoses on joint work in adults with neuromuscular diseases is related to severity of push-off deficits.

BACKGROUND: People with plantar flexor weakness generate less ankle push-off work during walking, resulting in inefficient proximal joint compensations. To increase push-off work, spring-like ankle foot orthoses (AFOs) can be provided. However, whether and in which patients AFOs increase push-off work and reduce compensatory hip and knee work is unknown. METHODS: In 18 people with bilateral plantar flexor weakness, we performed a 3D gait analysis at comfortable walking speed with shoes-only and with AFOs of which the stiffness was optimized. To account for walking speed differences between conditions, we compared relative joint work of the hip, knee and ankle joint. The relationships between relative work generated with shoes-only and changes in joint work with AFO were tested with Pearson correlations. RESULTS: No differences in relative ankle, knee and hip work over the gait cycle were found between shoes-only and AFO (p>0.499). Percentage of total ankle work generated during pre-swing increased with the AFO (AFO: 85.3±9.1% vs Shoes: 72.4±27.1%, p=0.026). At the hip, the AFO reduced relative work in pre-swing (AFO: 31.9±7.4% vs Shoes: 34.1±10.4%, p=0.038) and increased in loading response (AFO: 18.0±11.0% vs Shoes: 11.9±9.8%, p=0.022). Ankle work with shoes-only was inversely correlated with an increase in ankle work with AFO (r=-0.839, p<0.001) and this increase correlated with reduction in hip work with AFO (r=-0.650, p=0.004). DISCUSSION: Although stiffness-optimized AFOs did not alter the work distribution across the ankle, knee and hip joint compared to shoes-only walking, relative more ankle work was generated during push-off, causing a shift in hip work from pre-swing to loading response. Furthermore, larger ankle push-off deficits when walking with shoes-only were related with an increase in ankle work with AFO and reduction in compensatory hip work, indicating that more severely affected individuals benefit more from the energy storing-and-releasing capacity of AFOs.

Accuracy of EMG linear envelope in identifying the peak of muscular activity during walking.

BACKGROUND: The linear-envelope peak (LEP) of surface EMG signal is widely used in gait analysis to characterize muscular activity, especially in clinics. RESEARCH QUESTION: This study is designed to evaluate LEP accuracy in identifying muscular activation and assessing activation timing during walking. METHODS: Surface EMG signals from gastrocnemius lateralis (GL) and tibialis anterior (TA) were analyzed in 100 strides per subject (31 healthy subjects) during ground walking. Signals were full-wave rectified and low-pass filtered (cut-off frequency=5 Hz) to extract the linear envelope. LEP accuracy in identifying muscle activations and the associated error in peak detection were assessed by direct comparison with a reference method based on wavelet transform. LEP accuracy in identifying the timing of higher signalenergy levels was also assessed, increasing the reference-algorithm selectivity. RESULTS: The detection error (percentage number of times when LEP falls outside the correspondent reference activation interval) is close to zero. Detection error increases up to 70% for intervals including only signal energy higher than 90% of energy peak. Mean absolute error (MAE, the absolute value of the distance between LEP timing and the correspondent actual timing of the sEMG-signal peak computed by reference algorithm) is 54.1±20.0 ms. Detection error and MAE are significantly higher (p<0.05) in TA data compared to GL signals. Differences among MAE values detected adopting different values for LE cut-off frequency are not statistically significant. SIGNIFICANCE: LEP was found to be accurate in identifying the number of muscle activations during walking. However, the use of LEP to assess the timing of highest sEMG-signal energy (signal peak) should be considered carefully. Indeed, it could introduce a relevant inaccuracy in muscle-activation identification and peak-timing quantification. The type of muscle to analyze could also influence LEP performances, while the cut-off frequency chosen for envelope extraction appears to have a limited impact.

Robotic-arm assisted versus manual total knee arthroplasty: Functional gait analysis from a randomised controlled trial.

The primary aim of this study was to assess whether measures of functional gait assessment were improved with robotic total knee arthroplasty (rTKA) when compared to manual TKA (mTKA). Gait analysis was performed as part of a randomised controlled trial. Walking and relaxed standing assessments were performed using an instrumented mat system. Spatiotemporal variables included gait cycle parameters, anteroposterior and lateral sway, and plantar pressure ratios. Measurements were recorded at pre-operative baseline and 12 months post-operatively. 100 patients were randomised, 50 to each group. Complete gait cycle data were available for 26 rTKA and 23 mTKA patients. Cadence and walking velocity showed overall improvements following surgery, with no difference between the two groups. In the operated limb, overall step and stride times decreased, while step and stride lengths increased. Subgroup analysis showed reduced propulsion time with rTKA, and decreased foot flat and mid stance times with mTKA. Lateral sway was decreased in the rTKA group. Plantar pressure ratios showed an overall increase in hindfoot loading on the operated limb, with no difference between the two groups. No other significant differences were identified between rTKA and mTKA at 12 months, and limitations may include statistical error. A small sample of the study cohort was followed up; analysis may represent the results of satisfied patients with well-functioning TKA. Further study could incorporate proprioceptive and 3D gait analysis techniques to analyse knee kinetics and kinematics with robotic surgery. Pressure mapping could further subdivide the plantar surfaces to explore any nuances in differential loading.

Ambient floor vibration sensing advances the accessibility of functional gait assessments for children with muscular dystrophies.

Muscular dystrophies (MD) are a group of genetic neuromuscular disorders that cause progressive weakness and loss of muscles over time, influencing 1 in 3500-5000 children worldwide. New and exciting treatment options have led to a critical need for a clinical post-marketing surveillance tool to confirm the efficacy and safety of these treatments after individuals receive them in a commercial setting. For MDs, functional gait assessment is a common approach to evaluate the efficacy of the treatments because muscle weakness is reflected in individuals' walking patterns. However, there is little incentive for the family to continue to travel for such assessments due to the lack of access to specialty centers. While various existing sensing devices, such as cameras, force plates, and wearables can assess gait at home, they are limited by privacy concerns, area of coverage, and discomfort in carrying devices, which is not practical for long-term, continuous monitoring in daily settings. In this study, we introduce a novel functional gait assessment system using ambient floor vibrations, which is non-invasive and scalable, requiring only low-cost and sparsely deployed geophone sensors attached to the floor surface, suitable for in-home usage. Our system captures floor vibrations generated by footsteps from patients while they walk around and analyzes such vibrations to extract essential gait health information. To enhance interpretability and reliability under various sensing scenarios, we translate the signal patterns of floor vibration to pathological gait patterns related to MD, and develop a hierarchical learning algorithm that aggregates insights from individual footsteps to estimate a person's overall gait performance. When evaluated through real-world experiments with 36 subjects (including 15 patients with MD), our floor vibration sensing system achieves a 94.8% accuracy in predicting functional gait stages for patients with MD. Our approach enables accurate, accessible, and scalable functional gait assessment, bringing MD progressive tracking into real life.

Gait analysis of patients with knee osteoarthiritis who can run versus cannot run.

BACKGROUND: Many middle-aged and older adults participate in running to maintain their health and fitness; however, some have to stop running due to osteoarthritis-attributed knee pain. It was unclear whether gait biomechanics and knee physical findings differ between those who can and cannot run. RESEARCH QUESTION: What are the gait and knee physical findings of patients with knee osteoarthritis who remain capable of running in comparison to those who are not capable of running? METHODS: This was a cross-sectional study, which recruited 23 patients over the age of 40 who had been diagnosed with knee osteoarthritis. Their knee joint ranges of motion and muscle strength, knee pain, and the maximum gait speed (walk as fast as possible) were measured. Knee alignment was calculated from X-ray images, and the knee joint extension angle and adduction moment during a self-selected gait speed were determined using motion analysis. Participants were divided into two groups-those able to run (n=11) and those unable to run (n=12). The measured and calculated outcomes were compared between groups, and logistic regression analyses of significantly different outcomes were performed. RESULTS: There were significant group differences in the maximum knee extension angle during stance phase (p = 0.027), maximum gait speed during the 10-m walk test (p = 0.014), knee pain during gait (p = 0.039) and medial proximal tibial angle by X-ray (p = 0.035). Logistic regression analyses revealed that the maximum knee extension angle during stance phase (OR: 1.44, 95%CI: 1.06¬1.94, p = 0.02) was a significant factor. SIGNIFICANCE: The ability to extend the knee during gait is an important contributing factor in whether participants with knee osteoarthritis are capable of running.