1,165 research outputs found

    Predicting Continuous Locomotion Modes via Multidimensional Feature Learning from sEMG

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    Walking-assistive devices require adaptive control methods to ensure smooth transitions between various modes of locomotion. For this purpose, detecting human locomotion modes (e.g., level walking or stair ascent) in advance is crucial for improving the intelligence and transparency of such robotic systems. This study proposes Deep-STF, a unified end-to-end deep learning model designed for integrated feature extraction in spatial, temporal, and frequency dimensions from surface electromyography (sEMG) signals. Our model enables accurate and robust continuous prediction of nine locomotion modes and 15 transitions at varying prediction time intervals, ranging from 100 to 500 ms. In addition, we introduced the concept of 'stable prediction time' as a distinct metric to quantify prediction efficiency. This term refers to the duration during which consistent and accurate predictions of mode transitions are made, measured from the time of the fifth correct prediction to the occurrence of the critical event leading to the task transition. This distinction between stable prediction time and prediction time is vital as it underscores our focus on the precision and reliability of mode transition predictions. Experimental results showcased Deep-STP's cutting-edge prediction performance across diverse locomotion modes and transitions, relying solely on sEMG data. When forecasting 100 ms ahead, Deep-STF surpassed CNN and other machine learning techniques, achieving an outstanding average prediction accuracy of 96.48%. Even with an extended 500 ms prediction horizon, accuracy only marginally decreased to 93.00%. The averaged stable prediction times for detecting next upcoming transitions spanned from 28.15 to 372.21 ms across the 100-500 ms time advances.Comment: 10 pages,7 figure

    Electromyography-Based Control of Lower Limb Prostheses: A Systematic Review

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    Most amputations occur in lower limbs and despite improvements in prosthetic technology, no commercially available prosthetic leg uses electromyography (EMG) information as an input for control. Efforts to integrate EMG signals as part of the control strategy have increased in the last decade. In this systematic review, we summarize the research in the field of lower limb prosthetic control using EMG. Four different online databases were searched until June 2022: Web of Science, Scopus, PubMed, and Science Direct. We included articles that reported systems for controlling a prosthetic leg (with an ankle and/or knee actuator) by decoding gait intent using EMG signals alone or in combination with other sensors. A total of 1,331 papers were initially assessed and 121 were finally included in this systematic review. The literature showed that despite the burgeoning interest in research, controlling a leg prosthesis using EMG signals remains challenging. Specifically, regarding EMG signal quality and stability, electrode placement, prosthetic hardware, and control algorithms, all of which need to be more robust for everyday use. In the studies that were investigated, large variations were found between the control methodologies, type of research participant, recording protocols, assessments, and prosthetic hardware

    A method to determine the optimal features for control of a powered lower-limb prostheses

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    Lower-limb prostheses are rapidly advancing with greater computing power and sensing modalities. This paper is an attempt to begin exploring the trade-off between extrinsic and intrinsic control modalities. In this case, between electromyographic (extrinsic) and several internal sensors that can be used for intrinsic control. We propose a method that will identify the particular features, taken from two trans-femoral amputee and one trans-tibial amputee, during locomotion on varying terrain, that perfectly discriminate between locomotion modes. From this we are able to identify the source of the discriminability from a large-set of features that does not depend on the type of amputation. Also, we comment on the use of this algorithm in selecting the most discriminatory and least encumbering sensor/feature combination for transitions when the ground underneath the foot is unknown for trans-tibial amputees

    Real-time Hybrid Locomotion Mode Recognition for Lower-limb Wearable Robots

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    Real-time recognition of locomotion-related activities is a fundamental skill that the controller of lower-limb wearable robots should possess. Subject-specific training and reliance on electromyographic interfaces are the main limitations of existing approaches. This study presents a novel methodology for real-time locomotion mode recognition of locomotion-related activities in lower-limb wearable robotics. A hybrid classifier can distinguish among seven locomotion-related activities. First, a time-based approach classifies between static and dynamical states based on gait kinematics data. Second, an event-based fuzzy logic method triggered by foot pressure sensors operates in a subject-independent fashion on a minimal set of relevant biomechanical features to classify among dynamical modes. The locomotion mode recognition algorithm is implemented on the controller of a portable powered orthosis for hip assistance. An experimental protocol is designed to evaluate the controller performance in an out-of-lab scenario without the need for a subject-specific training. Experiments are conducted on six healthy volunteers performing locomotion-related activities at slow, normal, and fast speeds under the zero-torque and assistive mode of the orthosis. The overall accuracy rate of the controller is 99.4% over more than 10,000 steps, including seamless transitions between different modes. The experimental results show a successful subject-independent performance of the controller for wearable robots assisting locomotion-related activities

    Towards Intelligent Lower Limb Prostheses with Activity Recognition

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    User’s volitional control of lower limb prostheses is still challenging task despite technological advancements. There is still a need for amputees to impose their will upon the prosthesis to drive in an accurate and interactive fashion. This study represents a brief review on control strategies using different sensor modalities for the purpose of phases/events detection and activity recognition. The preliminary work that is associated with middle-level control shows a simple and reliable method for event detection in real-time using a single inertial measurement unit. The outcome shows promising results

    Effects of overground walking with a robotic exoskeleton on lower limb muscle synergies

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    Les exosquelettes robotisés de marche (ERM) représentent une intervention prometteuse dans le domaine de la réadaptation locomotrice. Sur le plan clinique, les ERM facilitent la mise en application de principes de neuroplasticité. Jusqu'à présent, la majorité des études analysant les effets de l’ERM a été menée avec des ERM fournissant une assistance robotique complète le long d’une trajectoire de mouvements prédéfinie des membres inférieurs (MI) de façon à reproduire la marche de façon quasi parfaite à très basse vitesse. La nouvelle génération d’ERM, maintenant disponible sur le marché, propose de nouveaux modes de contrôles qui permettent, entre autres, une liberté de mouvement accrue aux MIs (c.-à-d. trajectoire non imposée) et une possibilité d’offrir une assistance ou résistance aux mouvements de différentes intensités surtout pendant la phase d’oscillation du cycle de marche. Cependant, les effets de ces modes de contrôles sur la coordination musculaire des MI pendant la marche au sol avec l’ERM, caractérisé via l’extraction de synergies musculaires (SM), restent méconnus. Cette thèse mesure et compare les caractéristiques des SM (c.-à-d. nombre, profils d’activation, composition musculaire et contribution relative des muscles) pendant la la marche au sol sans ou avec un ERM paramétré avec six différents modes de contrôle chez des individus en bonne santé (articles #1 et #2) et d’autres ayant une lésion médullaire incomplète (LMI) (article #3). Les signaux électromyographiques (EMG) des différents muscles clés des MI, enregistrés lors de la marche, ont été utilisés afin d’extraire les SM avec un algorithme de factorisation matricielle non négative. La similarité des cosinus et les coefficients de corrélation ont caractérisé les similitudes entre les caractéristiques des SM. Les résultats montrent que: 1) les profils d'activation temporelle et le nombre de SM sont modifiés en fonction de la vitesse de marche avec, entre autres une augmentation de la vitesse de marche entrainant une fusion de SM, chez les individus en bonne santé marchant sans ERM ; 2) lorsque ces derniers marchent avec un ERM, les différents modes de contrôle testés ne dupliquent pas adéquatement les SM retrouvées lors de la marche sans ERM. En fait, uniquement le mode de contrôle libérant la contrainte de trajectoire de mouvements des MIs dans le plan sagittal lors de la phase d’oscillation reproduit les principales caractéristiques des SM retrouvées pendant la marche sans ERM ; 3) le nombre et la composition musculaire des SM sont modifiés pendant la marche sans ERM chez les personnes ayant une LMI. Cependant, parmi tous les modes de contrôle étudiés, seul le mode de contrôle libérant le contrôle de la trajectoire de mouvements des MI et assistant l’oscillation du MIs (c.-à-d. HASSIST) permets l’extraction de SM similaire à celles observées chez des individus en santé lors d'une marche sans ERM. Dans l’ensemble, cette thèse a mis en évidence le fait que différentes demandes biomécaniques liées à la marche (c.-à-d. vitesse de marche, modes de contrôle de l’ERM) modifient le nombre et les caractéristiques de SM chez les personnes en santé. Cette thèse a également confirmé que la coordination musculaire, mise en évidence via l’analyse de SM, est altérée chez les personnes ayant une LMI et a tendance à se normaliser lors de la marche avec l’ERM paramétré dans le mode de HASSIST. Les nouvelles preuves appuieront les professionnels de la réadaptation dans le processus de prise de décision concernant la sélection du mode de contrôle des MIs lors de l’entrainement locomoteur utilisant avec un ERM.Wearable robotic exoskeletons (WRE) represent a promising rehabilitation intervention for locomotor rehabilitation training that aligns with activity-based neuroplasticity principles in terms of optimal sensory input, massed repetition, and proper kinematics. Thus far, most studies that investigated the effects of WRE have used WRE that provide full robotic assistance and fixed trajectory guidance to the lower extremity (L/E) to generate close-to-normal walking kinematics, usually at very slow speeds. Based on clinicians’ feedback, current commercially-available WRE have additional control options to be able to integrate these devices into the recovery process of individuals who have maintained some ability to walk after an injury to the central nervous system. In this context, WRE now offer additional degrees of movements for the L/E to move freely and different strategies to assist or resist movement, particularly during the gait cycle’s swing phase. However, the extent that these additional WRE control options affect L/E neuromuscular control during walking, typically characterized using muscle synergies (MSs), remains unknown. This thesis measures and compares MSs characteristics (i.e., number, temporal activation profile, and muscles contributing to a specific synergy [weightings]) during typical overground walking, with and without a WRE, in six different control modes, in abled-bodied individuals (Articles #1 and #2) and individuals with incomplete spinal cord injury (iSCI; Article #3). Surface EMG of key L/E muscles were recorded while walking and used to extract MSs using a non-negative matrix factorization algorithm. Cosine similarity and correlation coefficients characterized, grouped, and indicated similarities between MS characteristics. Results demonstrated that: 1) the number of MSs and MS temporal activation profiles in able-bodied individuals walking without WRE are modified by walking speed and that, as speed increased, specific MSs were fused or merged compared to MSs at slow speeds; 2) In able-bodied individuals walking with WRE, few WRE control modes maintained the typical MSs characteristics that were found during overground walking without WRE. Moreover, freeing the L/E swing trajectory imposed by the WRE best reproduced those MSs characteristics during overground walking without the WRE; and 3) After an iSCI, alterations to the number and the composition of MSs were observed during walking without WRE. However, of all WRE control modes that were investigated, only HASSIST (i.e., freeing WRE control over L/E swing trajectory while assisting the user’s self-selected trajectory) reproduced the number and composition of MSs found in abled-bodied individuals during overground walking without WRE. Altogether, the results of this thesis demonstrated that different walking-related biomechanical demands (i.e., walking speed) and most of the WRE control modes can alter some MSs, and their characteristics, in able-bodied individuals. This research also confirmed that impaired muscle coordination, assessed via MSs, can adapt when walking with a WRE set with specific control options (e.g., HASSIST). These MS adaptations mimicked typical MS characteristics extracted during overground walking. The evidence generated by this thesis will support the decision-making process when selecting specific L/E control options during WRE walking, allowing rehabilitation professionals to refine WRE locomotor training protocols
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