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.

Gait classification for growing children with Duchenne muscular dystrophy.

Classifying gait patterns into homogeneous groups could enhance communication among healthcare providers, clinical decision making and clinical trial designs in boys with Duchenne muscular dystrophy (DMD). Sutherland's classification has been developed 40 years ago. Ever since, the state-of-the-art medical care has improved and boys with DMD are now longer ambulatory. Therefore, the gait classification requires an update. The overall aim was to develop an up-to-date, valid DMD gait classification. A total of 137 three-dimensional gait analysis sessions were collected in 30 boys with DMD, aged 4.6-17 years. Three classes were distinguished, which only partly aligned with increasing severity of gait deviations. Apart from the mildly affected pattern, two more severely affected gait patterns were found, namely the tiptoeing pattern and the flexion pattern with distinct anterior pelvic tilt and posterior trunk leaning, which showed most severe deviations at the ankle or at the proximal segments/joints, respectively. The agreement between Sutherland's and the current classification was low, suggesting that gait pathology with the current state-of-the-art medical care has changed. However, overlap between classes, especially between the two more affected classes, highlights the complexity of the continuous gait changes. Therefore, caution is required when classifying individual boys with DMD into classes.

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.

Are Gait Patterns during In-Lab Running Representative of Gait Patterns during Real-World Training? An Experimental Study.

Biomechanical assessments of running typically take place inside motion capture laboratories. However, it is unclear whether data from these in-lab gait assessments are representative of gait during real-world running. This study sought to test how well real-world gait patterns are represented by in-lab gait data in two cohorts of runners equipped with consumer-grade wearable sensors measuring speed, step length, vertical oscillation, stance time, and leg stiffness. Cohort 1 (N = 49) completed an in-lab treadmill run plus five real-world runs of self-selected distances on self-selected courses. Cohort 2 (N = 19) completed a 2.4 km outdoor run on a known course plus five real-world runs of self-selected distances on self-selected courses. The degree to which in-lab gait reflected real-world gait was quantified using univariate overlap and multivariate depth overlap statistics, both for all real-world running and for real-world running on flat, straight segments only. When comparing in-lab and real-world data from the same subject, univariate overlap ranged from 65.7% (leg stiffness) to 95.2% (speed). When considering all gait metrics together, only 32.5% of real-world data were well-represented by in-lab data from the same subject. Pooling in-lab gait data across multiple subjects led to greater distributional overlap between in-lab and real-world data (depth overlap 89.3-90.3%) due to the broader variability in gait seen across (as opposed to within) subjects. Stratifying real-world running to only include flat, straight segments did not meaningfully increase the overlap between in-lab and real-world running (changes of <1%). Individual gait patterns during real-world running, as characterized by consumer-grade wearable sensors, are not well-represented by the same runner's in-lab data. Researchers and clinicians should consider "borrowing" information from a pool of many runners to predict individual gait behavior when using biomechanical data to make clinical or sports performance decisions.

Gait Pattern Analysis: Integration of a Highly Sensitive Flexible Pressure Sensor on a Wireless Instrumented Insole.

Gait phase monitoring wearable sensors play a crucial role in assessing both health and athletic performance, offering valuable insights into an individual's gait pattern. In this study, we introduced a simple and cost-effective capacitive gait sensor manufacturing approach, utilizing a micropatterned polydimethylsiloxane dielectric layer placed between screen-printed silver electrodes. The sensor demonstrated inherent stretchability and durability, even when the electrode was bent at a 45-degree angle, it maintained an electrode resistance of approximately 3 Ω. This feature is particularly advantageous for gait monitoring applications. Furthermore, the fabricated flexible capacitive pressure sensor exhibited higher sensitivity and linearity at both low and high pressure and displayed very good stability. Notably, the sensors demonstrated rapid response and recovery times for both under low and high pressure. To further explore the capabilities of these new sensors, they were successfully tested as insole-type pressure sensors for real-time gait signal monitoring. The sensors displayed a well-balanced combination of sensitivity and response time, making them well-suited for gait analysis. Beyond gait analysis, the proposed sensor holds the potential for a wide range of applications within biomedical, sports, and commercial systems where soft and conformable sensors are preferred.

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.

Integrated Fibrous Iontronic Pressure Sensors with High Sensitivity and Reliability for Human Plantar Pressure and Gait Analysis.

Flexible sensing systems (FSSs) designed to measure plantar pressure can deliver instantaneous feedback on human movement and posture. This feedback is crucial not only for preventing and controlling diseases associated with abnormal plantar pressures but also for optimizing athletes' postures to minimize injuries. The development of an optimal plantar pressure sensor hinges on key metrics such as a wide sensing range, high sensitivity, and long-term stability. However, the effectiveness of current flexible sensors is impeded by numerous challenges, including limitations in structural deformability, mechanical incompatibility between multifunctional layers, and instability under complex stress conditions. Addressing these limitations, we have engineered an integrated pressure sensing system with high sensitivity and reliability for human plantar pressure and gait analysis. It features a high-modulus, porous laminated ionic fiber structure with robust self-bonded interfaces, utilizing a unified polyimide material system. This system showcases a high sensitivity (156.6 kPa-1), an extensive sensing range (up to 4000 kPa), and augmented interfacial toughness and durability (over 150,000 cycles). Additionally, our FSS is capable of real-time monitoring of plantar pressure distribution across various sports activities. Leveraging deep learning, the flexible sensing system achieves a high-precision, intelligent recognition of different plantar types with a 99.8% accuracy rate. This approach provides a strategic advancement in the field of flexible pressure sensors, ensuring prolonged stability and accuracy even amidst complex pressure dynamics and providing a feasible solution for long-term gait monitoring and analysis.

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.

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.

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.

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.

Ubiquitous Gait Analysis through Footstep-Induced Floor Vibrations.

Quantitative analysis of human gait is critical for the early discovery, progressive tracking, and rehabilitation of neurological and musculoskeletal disorders, such as Parkinson's disease, stroke, and cerebral palsy. Gait analysis typically involves estimating gait characteristics, such as spatiotemporal gait parameters and gait health indicators (e.g., step time, length, symmetry, and balance). Traditional methods of gait analysis involve the use of cameras, wearables, and force plates but are limited in operational requirements when applied in daily life, such as direct line-of-sight, carrying devices, and dense deployment. This paper introduces a novel approach for gait analysis by passively sensing floor vibrations generated by human footsteps using vibration sensors mounted on the floor surface. Our approach is low-cost, non-intrusive, and perceived as privacy-friendly, making it suitable for continuous gait health monitoring in daily life. Our algorithm estimates various gait parameters that are used as standard metrics in medical practices, including temporal parameters (step time, stride time, stance time, swing time, double-support time, and single-support time), spatial parameters (step length, width, angle, and stride length), and extracts gait health indicators (cadence/walking speed, left-right symmetry, gait balance, and initial contact types). The main challenge we addressed in this paper is the effect of different floor types on the resultant vibrations. We develop floor-adaptive algorithms to extract features that are generalizable to various practical settings, including homes, hospitals, and eldercare facilities. We evaluate our approach through real-world walking experiments with 20 adults with 12,231 labeled gait cycles across concrete and wooden floors. Our results show 90.5% (RMSE 0.08s), 71.3% (RMSE 0.38m), and 92.3% (RMSPE 7.7%) accuracy in estimating temporal, spatial parameters, and gait health indicators, respectively.

Validity Verification of Human Pose-Tracking Algorithms for Gait Analysis Capability.

Two-dimensional (2D) clinical gait analysis systems are more affordable and portable than contemporary three-dimensional (3D) clinical models. Using the Vicon 3D motion capture system as the standard, we evaluated the internal statistics of the Imasen and open-source OpenPose gait measurement systems, both designed for 2D input, to validate their output based on the similarity of results and the legitimacy of their inner statistical processes. We measured time factors, distance factors, and joint angles of the hip and knee joints in the sagittal plane while varying speeds and gaits during level walking in three in-person walking experiments under normal, maximum-speed, and tandem scenarios. The intraclass correlation coefficients of the 2D models were greater than 0.769 for all gait parameters compared with those of Vicon, except for some knee joint angles. The relative agreement was excellent for the time-distance gait parameter and moderate-to-excellent for each gait motion contraction range, except for hip joint angles. The time-distance gait parameter was high for Cronbach's alpha coefficients of 0.899-0.993 but low for 0.298-0.971. Correlation coefficients were greater than 0.571 for time-distance gait parameters but lower for joint angle parameters, particularly hip joint angles. Our study elucidates areas in which to improve 2D models for their widespread clinical application.

Effects on gait kinematics, pedobarography, functional and subjective results after isolated chopart injury.

BACKGROUND: This study analysed changes in gait and pedobarography and subjective and functional outcomes after isolated Chopart joint injury. METHODS: The results of 14 patients were reviewed. Kinematic 3D gait analysis, comparative bilateral electromyography (EMG) and pedobarography were performed. RESULTS: On the injured side, the 3D gait analysis showed a significantly increased internal rotation and decreased external rotation of the hip and significantly decreased adduction and decreased range of motion (ROM) for the ankle. On the healthy side, the pedobarography revealed a significantly increased mean force in the forefoot, an increased peak maximum force and an increased maximum pressure in the metatarsal. When standing, significantly more weight was placed on the healthy side. The EMG measurements showed no significant differences between the healthy and injured legs. CONCLUSIONS: After isolated Chopart injuries, significant changes in gait and pedobarography can be seen over the long term.

Which method should we use to determine the hip joint center location in individuals with a high amount of soft tissue?

BACKGROUND: This study investigated the most accurate method for estimating the hip joint center position in clinical 3D gait analysis for young individuals with high amounts of soft tissue. We compared position estimates of five regression-based and two functional methods to the hip joint center position obtained through 3D free-hand ultrasound. METHODS: For this purpose, the data of 14 overweight or obese individuals with a mean age of 13.6 (SD 2.1 yrs) and a BMI of 36.5 (SD 7.1 kg/m2, range 26-52 kg/m2) who underwent standard clinical 3D gait analysis were used. The data of each participant were processed with five regression-based and two functional methods and compared to the hip joint center identified via 3D free-hand ultrasound. FINDINGS: The absolute location errors to 3D free-hand ultrasound for each anatomical plane and the Euclidean distances served as outcomes next to their effects on gait variables. The data suggest that regression-based methods are preferable to functional methods in this population, as the latter demonstrated the highest variability in accuracy with large errors for some individuals. INTERPRETATION: Based on our findings we recommend using the regression method presented by Hara et al. due to its superior overall accuracy of <9 mm on average in all planes and the lowest impact on kinematic and kinetic output variables. We do not recommend using the Harrington equations (single and multiple) in populations with high amounts of soft tissue as they require pelvic depth as input, which can be massively biased when a lot of soft tissue is present around the pelvis.

Evaluation of the optimal number of steps to obtain reliable running spatio-temporal parameters and their variability.

BACKGROUND: Spatio-temporal running parameters and their variability help to determine a runner's running style. However, determining whether a change is due to the measurement or to a specific condition such as an injury is a matter of debate, as no recommendation on the number of steps required to obtain reliable assessments exists. RESEARCH QUESTION: What is the optimal number of steps required to measure different spatio-temporal parameters and study their variability at different running speeds? METHODS: Twenty-five runners performed three experimental sessions of three bouts of treadmill running at 8, 10 and 12 km/h separated by 24 h. We measured cadence, stride, step, contact and flight time. We calculated the duty factor and the leg stiffness index (Kleg). Mean spatio-temporal parameters and linear (coefficient of variation, standard deviation) and non-linear (Higuchi fractal index, α1 coefficient of detrended fluctuation analysis) analyses were computed for different numbers of steps. Relative reliability was determined using the intraclass coefficient correlation. The minimal number of steps which present a good reliability level was considered as the optimal number of steps for measurement. Absolute reliability was assessed by calculating minimal detectable change. RESULTS: To assess the mean values of spatio-temporal running parameters, between 16 and 150 steps were required. We were unable to obtain an optimal number of steps for cadence, stride and step-time variabilities for all speeds. For the linear analyses, we deduced the optimal number of steps for Kleg and the contact time (around 350 steps). Non-linear analyses measurements required between 350 and 540 steps, depending on the parameter. SIGNIFICANCE: Researchers and clinicians should optimize experimental conditions (number of steps and running speed) depending on the parameter or the variability analysis targeted. Future studies must use absolute reliability metrics to report changes in response to a specific condition with no bias due to measurement error.

Machine learning applied to gait analysis data in cerebral palsy and stroke: A systematic review.

BACKGROUND: Among neurological pathologies, cerebral palsy and stroke are the main contributors to walking disorders. Machine learning methods have been proposed in the recent literature to analyze gait data from these patients. However, machine learning methods still fail to translate effectively into clinical applications. This systematic review addressed the gaps hindering the use of machine learning data analysis in the clinical assessment of cerebral palsy and stroke patients. RESEARCH QUESTION: What are the main challenges in transferring proposed machine learning methods to clinical applications? METHODS: PubMed, Web of Science, Scopus, and IEEE databases were searched for relevant publications on machine learning methods applied to gait analysis data from stroke and cerebral palsy patients until February the 23rd, 2023. Information related to the suitability, feasibility, and reliability of the proposed methods for their effective translation to clinical use was extracted, and quality was assessed based on a set of predefined questions. RESULTS: From 4120 resulting references, 63 met the inclusion criteria. Thirty-one studies used supervised, and 32 used unsupervised machine learning methods. Artificial neural networks and k-means clustering were the most used methods in each category. The lack of rationale for features and algorithm selection, the use of unrepresentative datasets, and the lack of clinical interpretability of the clustering outputs were the main factors hindering the clinical reliability and applicability of these methods. SIGNIFICANCE: The literature offers numerous machine learning methods for clustering gait data from cerebral palsy and stroke patients. However, the clinical significance of the proposed methods is still lacking, limiting their translation to real-world applications. The design of future studies must take into account clinical question, dataset significance, feature and model selection, and interpretability of the results, given their criticality for clinical translation.

Modeling foot rockers via functional calibration for use in clinical gait analysis.

BACKGROUND: Goal of this work is a quantitative description of Jacquelin Perry's rocker concept by locating the position of the heel rocker and the forefoot rocker within segments of the foot via functional calibration. METHODS: Two functional calibration tasks with the foot in ground contact were performed by ten typical developed adults and foot marker motion was captured. After applying a least-square method for constructing foot segments, their motion relative to the floor was analyzed via a functional algorithm. Resulting reference positions - namely the heel rotation center and the metatarsal rotation axis - were calculated. Further, the repeatability of the method and variability of outcome within the cohort was tested. RESULTS: The heel rotation center is located substantially posterior (25 mm) and slightly more inferior (5 mm). to the midpoint of the two markers placed medially and laterally on the calcaneus. Repeated measures reveal a variation of this location around 5 mm. The forefoot center is slightly more medial to the "toe marker" (DMT2) and substantially more inferior (19 mm). The metatarsal rotation axis is slightly tilted in the frontal and transverse plane against the metatarsal line given between markers on MT1 and MT5 with small variation in repeated measures (1-2°). SIGNIFICANCE: The determination of heel rotation center and the metatarsal rotation axis relative to foot segments can be determined with good repeatability and their location meet the intuitive expectation. Since they have a direct biomechanical meaning in the foot roll-over process in gait, they may be used for a more functionally oriented definition of foot segments potentially improving the calculation of foot kinematics and kinetics in future work.

Analysis of gait pattern related to high cerebral small vessel disease burden using quantitative gait data from wearable sensors.

BACKGROUND AND OBJECTIVES: Sensor-based wearable devices help to obtain a wide range of quantitative gait parameters, which provides sufficient data to investigate disease-specific gait patterns. Although cerebral small vessel disease (CSVD) plays a significant role in gait impairment, the specific gait pattern associated with a high burden of CSVD remains to be explored. METHODS: We analyzed the gait pattern related to high CSVD burden from 720 participants (aged 55-65 years, 42.5 % male) free of neurological disease in the Taizhou Imaging Study. All participants underwent detailed quantitative gait assessments (obtained from an insole-like wearable gait tracking device) and brain magnetic resonance imaging examinations. Thirty-three gait parameters were summarized into five gait domains. Sparse sliced inverse regression was developed to extract the gait pattern related to high CSVD burden. RESULTS: The specific gait pattern derived from several gait domains (i.e., angles, phases, variability, and spatio-temporal) was significantly associated with the CSVD burden (OR=1.250, 95 % CI: 1.011-1.546). The gait pattern indicates that people with a high CSVD burden were prone to have smaller gait angles, more stance time, more double support time, larger gait variability, and slower gait velocity. Furthermore, people with this gait pattern had a 25 % higher risk of a high CSVD burden. CONCLUSIONS: We established a more stable and disease-specific quantitative gait pattern related to high CSVD burden, which is prone to facilitate the identification of individuals with high CSVD burden among the community residents or the general population.