Phase-Based Gait Prediction after Botulinum Toxin Treatment Using Deep Learning.

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.

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

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

Assessment of gait quality and efficiency after undergoing a single-event multilevel surgery in children with cerebral palsy presenting an intoeing gait pattern.

PURPOSE: The biomechanical impact of undergoing a single-event multilevel surgery (SEMLS) for children with cerebral palsy (CP) presenting an intoeing gait pattern has been widely documented. However, past studies mostly focused on gait quality rather than efficiency. Thus, there is a need to determine the impact of undergoing a SEMLS on gait quality and efficiency in children with CP presenting an intoeing gait pattern. METHODS: Data from 16 children with CP presenting an intoeing gait pattern who underwent a SEMLS were retrospectively selected. Gait kinematics was quantified before (baseline) and at least 1 year after the surgery (follow-up). Gait quality was investigated with the Gait Profile Score (GPS), hip internal rotation angle and foot progression angle (FPA). Gait efficiency was analysed using clinically accessible variables, namely the normalised gait speed and medio-lateral and vertical centre of mass excursions (COMp). Dependent variables were compared between sessions with paired t-tests. RESULTS: At the follow-up, children with CP exhibited a more outward FPA and GPS as well as a decreased hip internal rotation angle. No changes in normalised gait speed and vertical COMp excursion were observed, and medio-lateral COMp excursion was slightly decreased. CONCLUSION: Children with CP presenting an intoeing gait pattern who underwent a SEMLS exhibited an increased gait quality, but gait efficiency was only minimally improved at the follow-up compared to baseline. Further studies are needed to identify contributors of gait efficiency in children with CP, and the best treatment modalities to optimise both their gait quality and efficiency.

Three-Dimensional Lower-Limb Kinematics from Accelerometers and Gyroscopes with Simple and Minimal Functional Calibration Tasks: Validation on Asymptomatic Participants.

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.

Flexible Machine Learning Algorithms for Clinical Gait Assessment Tools.

The current gold standard of gait diagnostics is dependent on large, expensive motion-capture laboratories and highly trained clinical and technical staff. Wearable sensor systems combined with machine learning may help to improve the accessibility of objective gait assessments in a broad clinical context. However, current algorithms lack flexibility and require large training datasets with tedious manual labelling of data. The current study tests the validity of a novel machine learning algorithm for automated gait partitioning of laboratory-based and sensor-based gait data. The developed artificial intelligence tool was used in patients with a central neurological lesion and severe gait impairments. To build the novel algorithm, 2% and 3% of the entire dataset (567 and 368 steps in total, respectively) were required for assessments with laboratory equipment and inertial measurement units. The mean errors of machine learning-based gait partitions were 0.021 s for the laboratory-based datasets and 0.034 s for the sensor-based datasets. Combining reinforcement learning with a deep neural network allows significant reduction in the size of the training datasets to <5%. The low number of required training data provides end-users with a high degree of flexibility. Non-experts can easily adjust the developed algorithm and modify the training library depending on the measurement system and clinical population.

Treatment Outcome Prediction Using Multi-Task Learning: Application to Botulinum Toxin in Gait Rehabilitation.

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.

Long-term follow-up after multilevel surgery in cerebral palsy.

INTRODUCTION: Single-event multilevel surgery (SEMLS) is frequently used to correct pathological gait patterns in children with bilateral spastic cerebral palsy (BSCP) in a single session surgery. However, in-depth long-term evaluation reports of gait outcomes are limited. Therefore, we investigated if SEMLS is able to correct lower extremity joint and pelvic angles during gait towards typically developing gait patterns (TDC) in children with BSCP, and if so, if this effect is durable over a 10-year period. MATERIALS AND METHODS: In total 13 children with BSCP GMFCS level II at time of index-surgery between the ages of 7.7-18.2 years at the time of SEMLS were retrospectively recruited. Three-dimensional gait data were captured preoperatively, as well as at short-, mid-, and long-term post-operatively, and used to analyze: movement analysis profile (MAP), gait profile score (GPS), and lower extremity joint and pelvic angles over the course of a gait cycle using statistical parametric mapping. RESULTS: In agreement with previous studies, MAP and GPS improved towards TDCs after surgery, as did knee extension during the stance phase (ɳ2 = 0.67; p < 0.001), while knee flexion in the swing phase (ɳ2 = 0.67; p < 0.001) and pelvic tilt over the complete gait cycle (ɳ2 = 0.36; p < 0.001) deteriorated; no differences were observed between follow-ups. However, further surgical interventions were required in 8 out of 13 of the participants to maintain improvements 10 years post-surgery. CONCLUSIONS: While the overall gait pattern improved, our results showed specific aspects of the gait cycle actually deteriorated post-SEMLS and that a majority of the participants needed additional surgery, supporting previous statements for the use of multilevel surgery rather than SEMLS. The results highlight that the field should not only focus on the overall gait scores when evaluating treatment outcomes but should offer additional long-term follow-up of lower extremity function.

Estimation of spatio-temporal parameters of gait from magneto-inertial measurement units: multicenter validation among Parkinson, mildly cognitively impaired and healthy older adults.

BACKGROUND: The use of miniaturized magneto-inertial measurement units (MIMUs) allows for an objective evaluation of gait and a quantitative assessment of clinical outcomes. Spatial and temporal parameters are generally recognized as key metrics for characterizing gait. Although several methods for their estimate have been proposed, a thorough error analysis across different pathologies, multiple clinical centers and on large sample size is still missing. The aim of this study was to apply a previously presented method for the estimate of spatio-temporal parameters, named Trusted Events and Acceleration Direct and Reverse Integration along the direction of Progression (TEADRIP), on a large cohort (236 patients) including Parkinson, mildly cognitively impaired and healthy older adults collected in four clinical centers. Data were collected during straight-line gait, at normal and fast walking speed, by attaching two MIMUs just above the ankles. The parameters stride, step, stance and swing durations, as well as stride length and gait velocity, were estimated for each gait cycle. The TEADRIP performance was validated against data from an instrumented mat. RESULTS: Limits of agreements computed between the TEADRIP estimates and the reference values from the instrumented mat were - 27 to 27 ms for Stride Time, - 68 to 44 ms for Stance Time, - 31 to 31 ms for Step Time and - 67 to 52 mm for Stride Length. For each clinical center, the mean absolute errors averaged across subjects for the estimation of temporal parameters ranged between 1 and 4%, being on average less than 3% (< 30 ms). Stride length mean absolute errors were on average 2% (≈ 25 mm). Error comparisons across centers did not show any significant difference. Significant error differences were found exclusively for stride and step durations between healthy elderly and Parkinsonian subjects, and for the stride length between walking speeds. CONCLUSIONS: The TEADRIP method was effectively validated on a large number of healthy and pathological subjects recorded in four different clinical centers. Results showed that the spatio-temporal parameters estimation errors were consistent with those previously found on smaller population samples in a single center. The combination of robustness and range of applicability suggests the use of the TEADRIP as a suitable MIMU-based method for gait spatio-temporal parameter estimate in the routine clinical use. The present paper was awarded the "SIAMOC Best Methodological Paper 2017".

Benchmarking Foot Trajectory Estimation Methods for Mobile Gait Analysis.

Mobile gait analysis systems based on inertial sensing on the shoe are applied in a wide range of applications. Especially for medical applications, they can give new insights into motor impairment in, e.g., neurodegenerative disease and help objectify patient assessment. One key component in these systems is the reconstruction of the foot trajectories from inertial data. In literature, various methods for this task have been proposed. However, performance is evaluated on a variety of datasets due to the lack of large, generally accepted benchmark datasets. This hinders a fair comparison of methods. In this work, we implement three orientation estimation and three double integration schemes for use in a foot trajectory estimation pipeline. All methods are drawn from literature and evaluated against a marker-based motion capture reference. We provide a fair comparison on the same dataset consisting of 735 strides from 16 healthy subjects. As a result, the implemented methods are ranked and we identify the most suitable processing pipeline for foot trajectory estimation in the context of mobile gait analysis.

Causal interactions and dynamic stability between limbs while walking with imposed leg constraints.

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.

Markerless motion capture provides repeatable gait outcomes in patients with knee osteoarthritis.

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.

Current practices in clinical gait analysis in Europe: A comprehensive survey-based study from the European society for movement analysis in adults and children (ESMAC) standard initiative.

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.

Clinical gait analysis reveals altered walking patterns in critical Covid 19 survivors.

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.

SAGA: Stability-Aware Gait Analysis in constraint-free environments.

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.

Towards a diagnostic tool for neurological gait disorders in childhood combining 3D gait kinematics and deep learning.

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.

Identifying treatment non-responders based on pre-treatment gait characteristics - A machine learning approach.

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.

Comparing spatiotemporal gait parameters between overground walking and self-paced treadmill walking in persons after stroke.

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.

Influence of different calibration methods on surface electromyography-informed musculoskeletal models with few input signals.

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.

Estimation of two wear factors for total hip arthroplasty: A simulation study based on musculoskeletal modelling.

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.

The medium-term effects of selective dorsal rhizotomy on gait compared to a matched cerebral palsy non-SDR group: A follow-up study.

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.