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La evaluación de la marcha en cinta caminadora puede resultar relevante para la toma de decisiones clínicas. No obstante, factores demográficos como la edad y el IMC pueden alterar la interpretación de los resultados. Nuestro objetivo fue obtener variables espacio- temporales, energéticas y costo de transporte durante la velocidad autoseleccionada en cinta caminadora para una muestra representativa de adultos uruguayos (n=28) y evaluar si diferentes rangos de edades e IMC pueden ser factores a tener en cuenta en pruebas clínicas donde se consideren dichas variables. Participaron 17 hombres y 11 mujeres (39,3 ± 14,8 años, 75,9 ± 12,5 kg, 1,74 ± 0,09 m, IMC 25,2 ± 4,06). Se realizó una reconstrucción 3D del movimiento en forma sincronizada con el consumo energético. Se obtuvieron valores de referencia y luego de agrupar los participantes según su IMC y rango de edad se compararon los datos mediante test de t (p≤0.05). Los resultados revelaron discrepancias significativas en las medidas espacio-temporales y energéticas de los adultos uruguayos al caminar en cinta con respecto a la literatura. La marcha difiere entre adultos jóvenes y de mediana edad en su velocidad autoseleccionada (p=0,03), longitud de zancada (p=0,01), trabajo mecánico externo (<0,001) y recuperación de energía mecánica (0,009), destacando la importancia de considerar la edad en evaluaciones clínicas. El IMC no influyó significativamente en estas variables. Estos hallazgos subrayan la necesidad de ajustar las interpretaciones de las pruebas clínicas de la marcha sobre cinta caminadora en adultos uruguayos de mediana edad (45 a 65 años).

Treadmill gait assessment can be relevant for clinical decision-making. However, demographic factors such as age and BMI may alter result interpretation. Our aim was to obtain spatiotemporal, energetic, and cost of transport variables during self-selected treadmill walking speed for a representative sample of Uruguayan adults (n=28) and to assess if different age ranges and BMI could be factors to consider in clinical tests involving these variables. Seventeen men and eleven women participated (39.3 ± 14.8 years, 75.9 ± 12.5 kg, 1.74 ± 0.09 m, BMI 25.2 ± 4.06). A synchronized 3D motion reconstruction was performed with energy consumption. Reference values were obtained and data were compared using t-tests (p≤0.05), after grouping participants by BMI and age range. Results revealed significant discrepancies in spatiotemporal and energetic measures of Uruguayan adults walking on the treadmill, compared to the literature. Gait differed between young and middle-aged adults in their self-selected speed (p=0.03), stride length (p=0.01), external mechanical work (p<0.001), and mechanical energy recovery (0.009), emphasizing the importance of considering age in clinical evaluations. BMI did not significantly influence these variables. These findings underscore the need to adjust interpretations of treadmill gait clinical tests in middle-aged Uruguayan adults (45 to 65 years).

A avaliação da marcha na esteira pode ser relevante para a tomada de decisões clínicas. No entanto, fatores demográficos como idade e IMC podem alterar a interpretação dos resultados. Nosso objetivo foi obter variáveis espaço-temporais, energéticas e custo de transporte durante a velocidade de caminhada autoselecionada na esteira para uma amostra representativa de adultos uruguaios (n = 28) e avaliar se diferentes faixas etárias e IMC podem ser fatores a serem considerados em testes clínicos que envolvam essas variáveis. Dezessete homens e onze mulheres participaram (39,3 ± 14,8 anos, 75,9 ± 12,5 kg, 1,74 ± 0,09 m, IMC 25,2 ± 4,06). Foi realizada uma reconstrução tridimensional do movimento sincronizada com o consumo de energia. Foram obtidos valores de referência e os dados foram comparados usando testes t (p≤0,05), após agrupar os participantes por IMC e faixa etária. Os resultados revelaram discrepâncias significativas nas medidas espaço-temporais e energéticas dos adultos uruguaios ao caminhar na esteira, em comparação com a literatura. A marcha diferiu entre adultos jovens e de meia-idade em sua velocidade autoselecionada (p=0,03), comprimento da passada (p=0,01), trabalho mecânico externo (<0,001) e recuperação de energia mecânica (0,009), destacando a importância de considerar a idade em avaliações clínicas. O IMC não influenciou significativamente essas variáveis. Esses achados destacam a necessidade de ajustar as interpretações dos testes clínicos de marcha na esteira em adultos uruguaios de meia- idade (45 a 65 anos).

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Aim: To investigate the dynamics of the motor control system during walking by examining the complexity, stability, and causal relationships of leg motions. Specifically, the study focuses on gait under both bilateral and unilateral constraints induced by a passive exoskeleton designed to replicate gastrocnemius contractures. Methods: Kinematic data was collected as 10 healthy participants walked at a self-selected speed. A new Complexity-Instability Index (CII) of the leg motions was defined as a function of the Correlation Dimension and the Largest Lyapunov Exponent. Causal interactions between the leg motions are explored using Convergent Cross Mapping. Results: Normal walking is characterized by a high mutual drive of each leg to the other, where CII is lowest for both legs (complexity of each leg motion is low and stability high). The effect of the bilateral emulated contractures is a reduced drive of each leg to the other and an increased CII for both legs. With unilateral emulated contracture, the mechanically constrained leg strongly drives the unconstrained leg, and CII was significantly higher for the constrained leg compared to normal walking. Conclusion: Redundancy in limb motions is used to support causal interactions, reducing complexity and increasing stability in our leg dynamics during walking. The role of redundancy is to allow adaptability above being able to satisfy the overall biomechanical problem; and to allow the system to interact optimally. From an applied perspective, important characteristics of functional movement patterns might be captured by these nonlinear and causal variables, as well as the biomechanical aspects typically studied.

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BACKGROUND: Covid-19 has dramatically increased the number of admissions in intensive care units due to respiratory complications. In some cases, the arousal of neurological impairments, such as peripheral neuropathies, have been revealed. The purpose of this research was to characterize the gait pattern and muscle activity changes in Covid-19 survivors compared to physiological gait. METHODS: Twelve post-Covid-19 participants admitted to intensive care units and twelve non-disabled controls were considered. Kinematics, kinetics and surface electromyographic data were collected for each participant during walking. Post Covid-19 participants were further divided into two sub-groups, according to the number of days spent in the intensive care units. Lower limb joint angles, moments and powers were extracted as well as the muscle activity of four muscles bilaterally, the spatial, temporal and spatiotemporal parameters of gait and the ground reaction forces. The extracted variables were compared through OneWay-ANOVA or Kruskal-Wallis tests where appropriate (p < 0.05). FINDINGS: Overall, the considered parameters revealed statistically significant reduction in gait speed, cadence, range of motion in the sagittal plane, anteroposterior and vertical ground reaction forces between pathological and control participants. Larger alterations of the gait patterns were highlighted in the post-Covid-19 group hospitalized in intensive care units longer than 35 days, where a reduced muscle activity was observed on all the analyzed muscles. INTERPRETATION: Results suggested that the severity of gait impairments in post-Covid-19 participants might be correlated with intensive care units-bedding period. Gait biomechanics assessment could be adopted in the clinical decision-making process to improve treatment protocols in post-Covid-19 survivors.

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Gait disorders in neurological diseases are frequently associated with spasticity. Intramuscular injection of Botulinum Toxin Type A (BTX-A) can be used to treat spasticity. Providing optimal treatment with the highest possible benefit-risk ratio is a crucial consideration. This paper presents a novel approach for predicting knee and ankle kinematics after BTX-A treatment based on pre-treatment kinematics and treatment information. The proposed method is based on a Bidirectional Long Short-Term Memory (Bi-LSTM) deep learning architecture. Our study's objective is to investigate this approach's effectiveness in accurately predicting the kinematics of each phase of the gait cycle separately after BTX-A treatment. Two deep learning models are designed to incorporate categorical medical treatment data corresponding to the injected muscles: (1) within the hidden layers of the Bi-LSTM network, (2) through a gating mechanism. Since several muscles can be injected during the same session, the proposed architectures aim to model the interactions between the different treatment combinations. In this study, we conduct a comparative analysis of our prediction results with the current state of the art. The best results are obtained with the incorporation of the gating mechanism. The average prediction root mean squared error is 2.99° (R2 = 0.85) and 2.21° (R2 = 0.84) for the knee and the ankle kinematics, respectively. Our findings indicate that our approach outperforms the existing methods, yielding a significantly improved prediction accuracy.

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BACKGROUND: Gait abnormality detection is a challenging task in clinical practice. The majority of the current frameworks for gait abnormality detection involve the individual processes of segmentation, feature estimation, feature learning, and similarity assessment. Since each component of these modules is fixed and they are mutually independent, their performance under difficult circumstances is not ideal. We combine those processes into a single framework, a gait abnormality detection system with an end-to-end network. METHODS: It is made up of convolutional neural networks and Deep-Q-learning methods: one for coordinate estimation and the other for classification. In a single joint learning technique that may be trained together, the two networks are modeled. This method is significantly more efficient for use in real life since it drastically simplifies the conventional step-by-step approach. RESULTS: The proposed model is experimented on MATLAB R2020a. While considering into consideration the stability factor, our proposed model attained an average case accuracy of 95.3%, a sensitivity of 96.4%, and a specificity of 94.1%. SIGNIFICANCE: Our paradigm for quantifying gait analysis using commodity equipment will improve access to quantitative gait analysis in medical facilities and rehabilitation centers while also allowing academics to conduct large-scale investigations for gait-related disorders. Numerous experimental findings demonstrate the effectiveness of the proposed strategy and its ability to provide cutting-edge outcomes.

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Motion analysis has seen minimal adoption for orthopaedic clinical assessments. Markerless motion capture solutions, namely Theia3D, address limitations of previous methods and provide gait outcomes that are robust to clothing choice and repeatable in healthy adults. Repeatability in orthopaedic populations has not been investigated and is important for clinical utility and adoption. The purpose of this study was to evaluate the repeatability of Theia3D for gait analysis in a knee osteoarthritis population. Ten orthopaedic patients with knee osteoarthritis underwent gait analysis on three visits, with an average of 8 days between. Participants were recorded during one-minute overground walking trials at self-selected typical and fast speeds by 8 synchronized video cameras. Video data were processed using Theia3D. Intraclass correlations were used to examine the repeatability of temporal distance metrics as well as segment lengths of the underlying kinematic model. Inter-trial and inter-session variability of lower extremity joint angles were estimated for each point of the gait cycle. Intraclass correlations were greater than 0.98 for all temporal distance metrics for both speeds. Lower body segment lengths had intraclass correlations above 0.90. Participant average joint angle waveforms displayed consistent patterns between visits. The average inter-trial and inter-session variability in joint angles across speeds were 1.17 and 1.45 degrees, respectively. The variability in joint angles between visits was less than typically reported for marker-based methods. Gait outcomes measured with Theia3D were highly repeatable in patients with knee osteoarthritis providing further validation for its use in clinical assessment and longitudinal studies.

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BACKGROUND: Clinical gait analysis (CGA) is a systematic approach to comprehensively evaluate gait patterns, quantify impairments, plan targeted interventions, and evaluate the impact of interventions. However, international standards for CGA are currently lacking, resulting in various national initiatives. Standards are important to ensure safe and effective healthcare practices and to enable evidence-based clinical decision-making, facilitating interoperability, and reimbursement under national healthcare policies. Collaborative clinical and research work between European countries would benefit from common standards. RESEARCH OBJECTIVE: This study aimed to review the current laboratory practices for CGA in Europe. METHODS: A comprehensive survey was conducted by the European Society for Movement Analysis in Adults and Children (ESMAC), in close collaboration with the European national societies. The survey involved 97 gait laboratories across 16 countries. The survey assessed several aspects related to CGA, including equipment used, data collection, processing, and reporting methods. RESULTS: There was a consensus between laboratories concerning the data collected during CGA. The Conventional Gait Model (CGM) was the most used biomechanical model for calculating kinematics and kinetics. Respondents also reported the use of video recording, 3D motion capture systems, force plates, and surface electromyography. While there was a consensus on the reporting of CGA data, variations were reported in training, documentation, data preprocessing and equipment maintenance practices. SIGNIFICANCE: The findings of this study will serve as a foundation for the development of standardized guidelines for CGA in Europe.

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Gait abnormalities are frequent in children and can be caused by different pathologies, such as cerebral palsy, neuromuscular disease, toe walker syndrome, etc. Analysis of the "gait pattern" (i.e., the way the person walks) using 3D analysis provides highly relevant clinical information. This information is used to guide therapeutic choices; however, it is underused in diagnostic processes, probably because of the lack of standardization of data collection methods. Therefore, 3D gait analysis is currently used as an assessment rather than a diagnostic tool. In this work, we aimed to determine if deep learning could be combined with 3D gait analysis data to diagnose gait disorders in children. We tested the diagnostic accuracy of deep learning methods combined with 3D gait analysis data from 371 children (148 with unilateral cerebral palsy, 60 with neuromuscular disease, 19 toe walkers, 60 with bilateral cerebral palsy, 25 stroke, and 59 typically developing children), with a total of 6400 gait cycles. We evaluated the accuracy, sensitivity, specificity, F1 score, Area Under the Curve (AUC) score, and confusion matrix of the predictions by ResNet, LSTM, and InceptionTime deep learning architectures for time series data. The deep learning-based models had good to excellent diagnostic accuracy (ranging from 0.77 to 0.99) for discrimination between healthy and pathological gait, discrimination between different etiologies of pathological gait (binary and multi-classification); and determining stroke onset time. LSTM performed best overall. This study revealed that the gait pattern contains specific, pathology-related information. These results open the way for an extension of 3D gait analysis from evaluation to diagnosis. Furthermore, the method we propose is a data-driven diagnostic model that can be trained and used without human intervention or expert knowledge. Furthermore, the method could be used to distinguish gait-related pathologies and their onset times beyond those studied in this research.

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Introduction: Motion analysis can be used to gain information needed for disease diagnosis as well as for the design and evaluation of intervention strategies in patients with hip osteoarthritis (HOA). Thereby, joint kinematics might be of great interest due to their discriminative capacity and accessibility, especially with regard to the growing usage of wearable sensors for motion analysis. So far, no comprehensive literature review on lower limb joint kinematics of patients with HOA exists. Thus, the aim of this systematic review and meta-analysis was to synthesise existing literature on lower body joint kinematics of persons with HOA compared to those of healthy controls during locomotion tasks. Methods: Three databases were searched for studies on pelvis, hip, knee and ankle kinematics in subjects with HOA compared to healthy controls during locomotion tasks. Standardised mean differences were calculated and pooled using a random-effects model. Where possible, subgroup analyses were conducted. Risk of bias was assessed with the Downs and Black checklist. Results and Discussion: A total of 47 reports from 35 individual studies were included in this review. Most studies analysed walking and only a few studies analysed stair walking or turning while walking. Most group differences were found in ipsi- and contralateral three-dimensional hip and sagittal knee angles with reduced ranges of motion in HOA subjects. Differences between subjects with mild to moderate and severe HOA were found, with larger effects in severe HOA subjects. Additionally, stair walking and turning while walking might be promising extensions in clinical gait analysis due to their elevated requirements for joint mobility. Large between-study heterogeneity was observed, and future studies have to clarify the effects of OA severity, laterality, age, gender, study design and movement execution on lower limb joint kinematics. Systematic Review Registration: PROSPERO (CRD42021238237).

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Background: Paediatric movement disorders such as cerebral palsy often negatively impact walking behaviour. Although clinical gait analysis is usually performed to guide therapy decisions, not all respond positively to their assigned treatment. Identifying these individuals based on their pre-treatment characteristics could guide clinicians towards more appropriate and personalized interventions. Using routinely collected pre-treatment gait and anthropometric features, we aimed to assess whether standard machine learning approaches can be effective in identifying patients at risk of negative treatment outcomes. Methods: Observational data of 119 patients with movement disorders were retrospectively extracted from a local clinical database, comprising sagittal joint angles and spatiotemporal parameters, derived from motion capture data pre- and post-treatment (physiotherapy, orthosis, botulin toxin injections, or surgery). Participants were labelled based on their change in gait profile score (GPS, non-responders with a decline in GPS of <1.6° vs. responders). Their pre-treatment features (sagittal joint angles, spatiotemporal parameters, anthropometrics) were used to train a support vector machine classifier with 5-fold cross-validation and Bayesian optimization within a MATLAB-based Classification Learner App. Results: An average accuracy of 88.2 ± 0.5 % was achieved for identifying participants whose gait will not respond to treatment, with 64 % true negative rate and an area under the curve of 88 %. Conclusion: Overall, a classical machine learning model was able to identify patients at risk of not responding to treatment, based on gait features and anthropometrics collected prior to treatment. The output of such a model could function as a warning signal, notifying clinicians that a certain individual might not respond well to the standard of care and that a more personalized intervention might be needed.

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BACKGROUND: Although model personalization is critical when assessing individuals with morphological or neurological abnormalities, or even non-disabled subjects, its translation into routine clinical settings is hampered by the cumbersomeness of experimental data acquisition and lack of resources, which are linked to high costs and long processing pipelines. Quantifying the impact of neglecting subject-specific information in simulations that aim to estimate muscle forces with surface electromyography informed modeling approaches, can address their potential in relevant clinical questions. The present study investigates how different methods to fine-tune subject-specific neuromuscular parameters, reducing the number of electromyography input data, could affect the estimation of the unmeasured excitations and the musculotendon forces. METHODS: Three-dimensional motion analysis was performed on 8 non-disabled adult subjects and 13 electromyographic signals captured. Four neuromusculoskeletal models were created for 8 participants: a reference model driven by a large set of sEMG signals; two models informed by four electromyographic signals but calibrated in different fashions; a model based on static optimization. FINDINGS: The electromyography-informed models better predicted experimental excitations, including the unmeasured ones. The model based on static optimization obtained less reliable predictions of the experimental data. When comparing the different reduced models, no major differences were observed, suggesting that the less complex model may suffice for predicting muscle forces with a small set of input in clinical gait analysis tasks. INTERPRETATION: Quantitative model performance evaluation in different conditions provides an objective indication of which method yields the most accurate prediction when a small set of electromyographic recordings is available.

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BACKGROUND: Primary causes of surgical revision after total hip arthroplasty are polyethylene wear and implant loosening. These factors are particularly related to joint friction and thus patients' physical activity. Assessing implant wear over time according to patients' morphology and physical activity level is key to improve follow-up and patients' quality of life. METHODS: An approach initially proposed for tibiofemoral prosthetic wear estimation was adapted to compute two wear factors (force-velocity, directional wear intensity) using a musculoskeletal model. It was applied on 17 participants with total hip arthroplasty to compute joint angular velocity, contact force, sliding velocity, and wear factors during common daily living activities. FINDINGS: Differences were observed between gait, sitting down, and standing up tasks. An incremental increase of both global wear factors (time-integral) was observed during gait from slow to fast speeds (p ≤ 0.01). Interestingly, these two wear factors did not result in same trend for sitting down and standing up tasks. Compared to gait, one cycle of sitting down or standing up tends to induce higher friction-related wear but lower cross-shear-related wear. Depending on the wear factor, significant differences can be found between sitting down and gait at slow speed (p ≤ 0.05), and between sitting down (p ≤ 0.05) or standing up (p ≤ 0.05) and gait at fast speed. Furthermore, depending on the activity, wear can be fostered by joint contact force and/or sliding velocity. INTERPRETATION: This study demonstrated the potential of wear estimation to highlight activities inducing a higher risk of implant wear after total hip arthroplasty from motion capture data.

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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.

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Purpose: The purpose of this study was to describe gait evolution in patients with unilateral spastic cerebral palsy (USCP) using modified Gait Profile Score (mGPS without hip rotation), Gait Variable Score (GVS), walking speed, and the observed effects of single-level surgery (SLS) after 10 years. Methods: Fifty-two patients with USCP (Gross Motor Function Classification System I) and data from two Clinical Gait Analyses (CGAs) were included. The evolution of patients' mGPS, GVS, and walking speed were calculated. Two "no surgery" and "single-level surgery" patient categories were analyzed. Paired t-tests were used to compare the data between CGAs and as a function of treatment category. Pearson's correlations were used to examine relationships between baseline values and evolutions in mGPS and walking speed. Results: Mean ages (SD) at first and last CGAs were 9.3 (3.2) and 19.7 (6.0) years old, respectively, with an average follow-up of 10.5 (5.6) years. Mean mGPS for the patients' affected side was significantly lower at the last CGA for the full cohort: baseline = 8.5° (2.1) versus follow-up = 7.2° (1.6), effect size = 0.73, p < 0.001. Significant improvements in mGPS and GVS for ankle and foot progression were found for the SLS group. The mGPS change and mGPS at baseline (r = -0.79, p < 0.001) were negatively correlated. Conclusions: SLS patients demonstrated a positive long-term change in gait pattern over time. The group that had undergone surgery had worse gait scores at baseline than the group that had not, but the SLS group's last CGA scores were relatively closer to those of the "no surgery" group. Level of evidence: This was a retrospective comparative therapeutic study (level III).

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BACKGROUND: Selective dorsal rhizotomy (SDR) has been shown to improve gait in the short-term in children with cerebral palsy (CP). Further study is needed to look at the trajectory of outcomes over the longer-term. RESEARCH QUESTION: What are the medium-term effects of SDR on gait compared to a matched CP non-SDR group? METHODS: Participants underwent SDR at mean age 6.3 years and completed baseline, 1-year and 5-year follow-up gait analyses. Non-SDR participants were matched at baseline. Differences were assessed within and between groups. Kinematic variables were analysed using Statistical non-Parametric Mapping (SnPM). Other gait and clinical data were analysed using Friedman's one-way repeated measure analysis of variance and a Mann-Whitney U-test. RESULTS: The initial SDR group consisted of 29 participants, reducing to 22 at 5-year follow-up. Of these, 15 (68 %) had orthopaedic surgeries either concurrent with or in the intervening period since the SDR, mean 3.3 procedures per participant. The initial non- SDR group had 18 participants, reducing to 17 at 5-year follow-up. Of these, 13 (76 %) had orthopaedic surgeries, mean 5.7 procedures. At 1-year follow-up the SDR group had significantly improved knee extension, ankle dorsiflexion, foot progression, Gait Deviation Index, and normalised step length compared to baseline, p < 0.05, and outcomes were maintained at 5-years. At 1-year follow-up the non-SDR group kinematic patterns were unchanged, but at 5-year follow-up this group demonstrated significantly improved knee extension, ankle dorsiflexion and foot progression. There were no significant kinematic differences between the SDR and the non-SDR group at medium-term follow-up. SIGNIFICANCE: We have documented the trajectory of gait outcomes post-SDR over 3 assessments and found that short-term gait changes endured in the medium-term. However, kinematic changes were similar to a non-SDR group undergoing routine and orthopaedic care. These outcomes are important to guide surgical decision making and to manage treatment goals and expectations.

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PURPOSE: Since self-paced treadmills enable more natural gait patterns compared to fixed-speed treadmills we examined the use of a self-paced treadmill as a alternative for overground gait analysis in persons after stroke. MATERIAL AND METHODS: Twenty-five persons after stroke (10 males/15 females; 53 ± 12.05 years; 40.72 ± 42.94 months post-stroke) walked at self-selected speed overground (GAITRite, CIR Systems) and on a self-paced treadmill (GRAIL, Motek) in randomized order. Spatiotemporal parameters, variability and symmetry measures were compared using paired-sample t-tests or Wilcoxon Signed Rank tests. Concurrent validity was assessed using intraclass correlation coefficients and Bland-Altman plots. A regression model determined the contribution of the walking velocity to the changes in spatiotemporal parameters. RESULTS: The velocity on the treadmill was significant lower compared to overground (p < 0.001). This difference predicted the significant changes in other spatiotemporal parameters to varying degrees (27.7%-83.8%). Bland-Altman plots showed large percentage of bias and limits of agreement. Variability and symmetry measures were similar between conditions. CONCLUSIONS: When considering gait analysis in persons after stroke a self-paced treadmill may be a valuable alternative for overground analysis. Although a slower walking velocity, and accompanying changes in other spatiotemporal parameters, should be taken into account compared to overground walking.Implications for rehabilitationConsidering the advantages regarding space and time, instrumented treadmills provide opportunities for gait assessment and training in a stroke population.When using self-paced treadmills for clinical gait analysis in persons after stroke, the slower walking velocity and accompanying changes in other spatiotemporal parameters need to be taken into account.Stroke patients seem to preserve their walking pattern on a self-paced treadmill.

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Background: The interpretation of clinical gait data in children with cerebral palsy (CP) is time-consuming, requires extensive expertise and often lacks transparency. Here we aimed to develop a set of look-up tables to support this process, linking typical gait features as present in CP to their potential underlying impairments. Methods: We developed an initial core set of gait features and their potential underlying impairments based on biomechanical reasoning, literature and clinical experience. This core set was further specified through a Delphi process in a multidisciplinary group of experts in gait analysis of children with CP and evaluated on 20 patient cases. The likelihood of the listed gait feature-impairment relationships was scored by the expert panel on a five-point scale. Results: The final core set included 120 relevant gait feature-impairment relations including likelihood scores. This set was presented in the form of look-up tables in both directions, i.e., sorted by gait features with potential underlying impairment, and sorted by impairments with potential related gait features. The average likelihood score for the relations was 3.5 ± 0.6 (range 2.1-4.6). Conclusion: The developed set of look-up tables linking gait features and impairments, can assist gait analysts and clinicians in standardized biomechanical reasoning, to support treatment decision-making for gait impairments in children with CP.

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We propose a framework for optimizing personalized treatment outcomes for patients with neurological diseases. A typical consequence of such diseases is gait disorders, partially explained by command and muscle tone problems associated with spasticity. Intramuscular injection of botulinum toxin type A is a common treatment for spasticity. According to the patient's profile, offering the optimal treatment combined with the highest possible benefit-risk ratio is important. For the prediction of knee and ankle kinematics after botulinum toxin type A (BTX-A) treatment, we propose: (1) a regression strategy based on a multi-task architecture composed of LSTM models; (2) to introduce medical treatment data (MTD) for context modeling; and (3) a gating mechanism to model treatment interaction more efficiently. The proposed models were compared with and without metadata describing treatments and with serial models. Multi-task learning (MTL) achieved the lowest root-mean-squared error (RMSE) (5.60°) for traumatic brain injury (TBI) patients on knee trajectories and the lowest RMSE (3.77°) for cerebral palsy (CP) patients on ankle trajectories, with only a difference of 5.60° between actual and predicted. Overall, the best RMSE ranged from 5.24° to 6.24° for the MTL models. To the best of our knowledge, this is the first time that MTL has been used for post-treatment gait trajectory prediction. The MTL models outperformed the serial models, particularly when introducing treatment metadata. The gating mechanism is efficient in modeling treatment interaction and improving trajectory prediction.

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The use of inertial measurement units (IMUs) to compute gait outputs, such as the 3D lower-limb kinematics is of huge potential, but no consensus on the procedures and algorithms exists. This study aimed at evaluating the validity of a 7-IMUs system against the optoelectronic system. Ten asymptomatic subjects were included. They wore IMUs on their feet, shanks, thighs and pelvis. The IMUs were embedded in clusters with reflective markers. Reference kinematics was computed from anatomical markers. Gait kinematics was obtained from accelerometer and gyroscope data after sensor orientation estimation and sensor-to-segment (S2S) calibration steps. The S2S calibration steps were also applied to the cluster data. IMU-based and cluster-based kinematics were compared to the reference through root mean square errors (RMSEs), centered RMSEs (after mean removal), correlation coefficients (CCs) and differences in amplitude. The mean RMSE and centered RMSE were, respectively, 7.5° and 4.0° for IMU-kinematics, and 7.9° and 3.8° for cluster-kinematics. Very good CCs were found in the sagittal plane for both IMUs and cluster-based kinematics at the hip, knee and ankle levels (CCs > 0.85). The overall mean amplitude difference was about 7°. These results reflected good accordance in our system with the reference, especially in the sagittal plane, but the presence of offsets requires caution for clinical use.

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SPM is a statistical method of analysis of time-varying human movement gait signal, depending on the random field theory (RFT). MovementRx is our inhouse-developed decision-support system that depends on SPM1D Python implementation of the SPM (spm1d.org). We present the potential application of MovementRx in the prediction of increased joint forces with the possibility to predispose to osteoarthritis in a sample of post-surgical Transtibial Amputation (TTA) patients who were ambulant in the community. We captured the three-dimensional movement profile of 12 males with TTA and studied them using MovementRx, employing the SPM1D Python library to quantify the deviation(s) they have from our corresponding reference data, using "Hotelling 2" and "T test 2" statistics for the 3D movement vectors of the 3 main lower limb joints (hip, knee, and ankle) and their nine respective components (3 joints × 3 dimensions), respectively. MovementRx results visually demonstrated a clear distinction in the biomechanical recordings between TTA patients and a reference set of normal people (ABILITY data project), and variability within the TTA patients' group enabled identification of those with an increased risk of developing osteoarthritis in the future. We conclude that MovementRx is a potential tool to detect increased specific joint forces with the ability to identify TTA survivors who may be at risk for osteoarthritis.