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Elderly patients often have more than one disease that affects walking behavior. An objective tool to identify which disease is the main cause of functional limitations may aid clinical decision making. Therefore, we investigated whether gait patterns could be used to identify degenerative diseases using machine learning. Data were extracted from a clinical database that included sagittal joint angles and spatiotemporal parameters measured using seven inertial sensors, and anthropometric data of patients with unilateral knee or hip osteoarthritis, lumbar or cervical spinal stenosis, and healthy controls. Various classification models were explored using the MATLAB Classification Learner app, and the optimizable Support Vector Machine was chosen as the best performing model. The accuracy of discrimination between healthy and pathologic gait was 82.3%, indicating that it is possible to distinguish pathological from healthy gait. The accuracy of discrimination between the different degenerative diseases was 51.4%, indicating the similarities in gait patterns between diseases need to be further explored. Overall, the differences between pathologic and healthy gait are distinct enough to classify using a classical machine learning model; however, routinely recorded gait characteristics and anthropometric data are not sufficient for successful discrimination of the degenerative diseases. [ABSTRACT FROM AUTHOR]

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., Competing Interests: None Declared., (© 2023 The Authors. Published by Elsevier Ltd.)

Background: To analyse and interpret gait patterns in pathological paediatric populations, accurate determination of the timing of specific gait events (e.g. initial contract - IC, or toe-off - TO) is essential. As currently used clinical identification methods are generally subjective, time-consuming, or limited to steps with force platform data, several techniques have been proposed based on processing of marker kinematics. However, until now, validation and standardization of these methods for use in diverse gait patterns remains lacking., Research Questions: 1) What is the accuracy of available kinematics-based identification algorithms in determining the timing of IC and TO for diverse gait signatures? 2) Does automatic identification affect interpretation of spatio-temporal parameters?., Methods: 3D kinematic and kinetic data of 90 children were retrospectively analysed from a clinical gait database. Participants were classified into 3 gait categories: group A (toe-walkers), B (flat IC) and C (heel IC). Five kinematic algorithms (one modified) were implemented for two different foot marker configurations for both IC and TO and compared with clinical (visual and force-plate) identification using Bland-Altman analysis. The best-performing algorithm-marker configuration was used to compute spatio-temporal parameters (STP) of all gait trials. To establish whether the error associated with this configuration would affect clinical interpretation, the bias and limits of agreement were determined and compared against inter-trial variability established using visual identification., Results: Sagittal velocity of the heel (Group C) or toe marker configurations (Group A and B) was the most reliable indicator of IC, while the sagittal velocity of the hallux marker configuration performed best for TO. Biases for walking speed, stride time and stride length were within the respective inter-trial variability values., Significance: Automatic identification of gait events was dependent on algorithm-marker configuration, and best results were obtained when optimized towards specific gait patterns. Our data suggest that correct selection of automatic gait event detection approach will ensure that misinterpretation of STPs is avoided., (Copyright © 2021 The Author(s). Published by Elsevier B.V. All rights reserved.)

Non-surgical treatment of knee osteoarthritis (KOA) is often focused on the motor component of KOA even though there is evidence that sensory dysfunctions play an important role in the impaired control of the affected joint. Excitation of sensory afferents can increase motor function by exploiting the nervous system's ability to adapt to changing environments (i.e., neuronal plasticity). Therefore, the aim of this study was to explore the acute effects of a single session (30 min) of sensory intervention targeting neuronal plasticity using low-frequency (10 Hz) somatosensory electrical stimulation (SES) of the femoral nerve. We evaluated the effects of SES on the position and force control of the affected knee and self-reported pain in KOA patients ( n = 14) in a sham-controlled randomized trial. The results showed that SES did not improve measures of lower-limb motor coordination compared to sham stimulation in KOA patients, nor did it improve self-reported knee function and pain (all p > 0.05). In conclusion, despite sensory involvement in KOA, the sensory intervention used in the present explorative study did not relieve self-reported pain, which may underlie the absence of an effect on measures of motor coordination. In sum, the present explorative study showed that SES alone does not improve motor coordination in KOA patients.

Introduction: The purpose of this study was to investigate the relative activation of vastus medialis (VM) and vastus lateralis (VL) muscles during voluntary and stimulated isometric contractions at different joint angles., Methods: Sixteen healthy men (mean age: 26 years) completed maximal voluntary and stimulated contractions of the knee extensor muscles at 30°, 65°, and 100° of knee flexion. VM/VL ratios were calculated from voluntary electromyographic (EMG) and evoked torque recordings., Results: Both EMG and VM/VL torque ratios were significantly lower at 30° than at 100° of knee flexion (P < 0.05)., Conclusions: These results can be explained by the relatively small contribution of the VM muscle to knee extension torque at short muscle length. Such disadvantage of the VM muscle at extended knee positions does not seem to be compensated by an increased neural drive. Muscle Nerve 56: 968-974, 2017., (© 2016 Wiley Periodicals, Inc.)