Within-socket Myoelectric Prediction of Continuous Ankle Kinematics for Control of a Powered Transtibial Prosthesis

Objective. Powered robotic prostheses create a need for natural-feeling user interfaces and robust control schemes. Here, we examined the ability of a nonlinear autoregressive model to continuously map the kinematics of a transtibial prosthesis and electromyographic (EMG) activity recorded within socket to the future estimates of the prosthetic ankle angle in three transtibial amputees. Approach. Model performance was examined across subjects during level treadmill ambulation as a function of the size of the EMG sampling window and the temporal \u27prediction\u27 interval between the EMG/kinematic input and the model\u27s estimate of future ankle angle to characterize the trade-off between model error, sampling window and prediction interval. Main results. Across subjects, deviations in the estimated ankle angle from the actual movement were robust to variations in the EMG sampling window and increased systematically with prediction interval. For prediction intervals up to 150 ms, the average error in the model estimate of ankle angle across the gait cycle was less than 6°. EMG contributions to the model prediction varied across subjects but were consistently localized to the transitions to/from single to double limb support and captured variations from the typical ankle kinematics during level walking. Significance. The use of an autoregressive modeling approach to continuously predict joint kinematics using natural residual muscle activity provides opportunities for direct (transparent) control of a prosthetic joint by the user. The model\u27s predictive capability could prove particularly useful for overcoming delays in signal processing and actuation of the prosthesis, providing a more biomimetic ankle response

Engineering Platform and Experimental Protocol for Design and Evaluation of a Neurally-controlled Powered Transfemoral Prosthesis

To enable intuitive operation of powered artificial legs, an interface between user and prosthesis that can recognize the user's movement intent is desired. A novel neural-machine interface (NMI) based on neuromuscular-mechanical fusion developed in our previous study has demonstrated a great potential to accurately identify the intended movement of transfemoral amputees. However, this interface has not yet been integrated with a powered prosthetic leg for true neural control. This study aimed to report (1) a flexible platform to implement and optimize neural control of powered lower limb prosthesis and (2) an experimental setup and protocol to evaluate neural prosthesis control on patients with lower limb amputations. First a platform based on a PC and a visual programming environment were developed to implement the prosthesis control algorithms, including NMI training algorithm, NMI online testing algorithm, and intrinsic control algorithm. To demonstrate the function of this platform, in this study the NMI based on neuromuscular-mechanical fusion was hierarchically integrated with intrinsic control of a prototypical transfemoral prosthesis. One patient with a unilateral transfemoral amputation was recruited to evaluate our implemented neural controller when performing activities, such as standing, level-ground walking, ramp ascent, and ramp descent continuously in the laboratory. A novel experimental setup and protocol were developed in order to test the new prosthesis control safely and efficiently. The presented proof-of-concept platform and experimental setup and protocol could aid the future development and application of neurally-controlled powered artificial legs

Technology Efficacy in Active Prosthetic Knees for Transfemoral Amputees: A Quantitative Evaluation

Several studies have presented technological ensembles of active knee systems for transfemoral prosthesis. Other studies have examined the amputees’ gait performance while wearing a specific active prosthesis. This paper combined both insights, that is, a technical examination of the components used, with an evaluation of how these improved the gait of respective users. This study aims to offer a quantitative understanding of the potential enhancement derived from strategic integration of core elements in developing an effective device. The study systematically discussed the current technology in active transfemoral prosthesis with respect to its functional walking performance amongst above-knee amputee users, to evaluate the system’s efficacy in producing close-to-normal user performance. The performances of its actuator, sensory system, and control technique that are incorporated in each reported system were evaluated separately and numerical comparisons were conducted based on the percentage of amputees’ gait deviation from normal gait profile points. The results identified particular components that contributed closest to normal gait parameters. However, the conclusion is limitedly extendable due to the small number of studies. Thus, more clinical validation of the active prosthetic knee technology is needed to better understand the extent of contribution of each component to the most functional development

Technology efficacy in active prosthetic knees for transfemoral amputees: A quantitative evaluation

Several studies have presented technological ensembles of active knee systems for transfemoral prosthesis. Other studies have examined the amputees' gait performance while wearing a specific active prosthesis. This paper combined both insights, that is, a technical examination of the components used, with an evaluation of how these improved the gait of respective users. This study aims to offer a quantitative understanding of the potential enhancement derived from strategic integration of core elements in developing an effective device. The study systematically discussed the current technology in active transfemoral prosthesis with respect to its functional walking performance amongst above-knee amputee users, to evaluate the system's efficacy in producing close-to-normal user performance. The performances of its actuator, sensory system, and control technique that are incorporated in each reported system were evaluated separately and numerical comparisons were conducted based on the percentage of amputees' gait deviation from normal gait profile points. The results identified particular components that contributed closest to normal gait parameters. However, the conclusion is limitedly extendable due to the small number of studies. Thus, more clinical validation of the active prosthetic knee technology is needed to better understand the extent of contribution of each component to the most functional development

Towards Bidirectional Lower Limb Prostheses: Restoring Proprioception Using EMG Based Vibrotactile Feedback

As a result, they do not effectively replace the lost limb. Electromyography (EMG) control has been widely implemented in upper limb prostheses but is still underdeveloped in lower limb prostheses. The aim of this thesis is to design, develop, and evaluate a novel vibrotactile feedback system in combination with an EMG-controlled powered knee or ankle prosthesis to restore proprioception. This thesis demonstrates that discrete localised vibrations enable proprioceptive sensing for the user through the described sensory feedback system. Three subjects with a major lower limb amputation performed level ground and inclined walking tests under various conditions. The experiments reported in the thesis compare the effects of EMG control with and without sensory feedback on temporal gait symmetry and psychosocial metrics, i.e. cognitive workload assessment, prosthesis embodiment, and confidence. The key results from this thesis are the following: temporal gait symmetry and psychosocial measures tended to improve within and between session, though the results varied widely between subjects. Interference in the rest EMG signal was found when the vibrotactors were activated. Further, subjects were able to distinguish between sensory feedback levels. EMG control initially reduced gait symmetry, but gait symmetry was later increased with sensory feedback. Higher symmetry scores were measured after sensory feedback was turned off, demonstrating learning retention. Similar trends were measured in psychosocial metrics, indicating that the sensory feedback system contributed to perceived improvements of the prosthesis. In summary, results show promising effects of using vibrotactile feedback in combination with EMG control in lower limb prostheses, despite the need to improve system robustness. Longer training with EMG and sensory feedback might improve quality of life of prosthesis users even more

EMG-driven control in lower limb prostheses: a topic-based systematic review

Background The inability of users to directly and intuitively control their state-of-the-art commercial prosthesis contributes to a low device acceptance rate. Since Electromyography (EMG)-based control has the potential to address those inabilities, research has flourished on investigating its incorporation in microprocessor-controlled lower limb prostheses (MLLPs). However, despite the proposed benefits of doing so, there is no clear explanation regarding the absence of a commercial product, in contrast to their upper limb counterparts. Objective and methodologies This manuscript aims to provide a comparative overview of EMG-driven control methods for MLLPs, to identify their prospects and limitations, and to formulate suggestions on future research and development. This is done by systematically reviewing academical studies on EMG MLLPs. In particular, this review is structured by considering four major topics: (1) type of neuro-control, which discusses methods that allow the nervous system to control prosthetic devices through the muscles; (2) type of EMG-driven controllers, which defines the different classes of EMG controllers proposed in the literature; (3) type of neural input and processing, which describes how EMG-driven controllers are implemented; (4) type of performance assessment, which reports the performance of the current state of the art controllers. Results and conclusions The obtained results show that the lack of quantitative and standardized measures hinders the possibility to analytically compare the performances of different EMG-driven controllers. In relation to this issue, the real efficacy of EMG-driven controllers for MLLPs have yet to be validated. Nevertheless, in anticipation of the development of a standardized approach for validating EMG MLLPs, the literature suggests that combining multiple neuro-controller types has the potential to develop a more seamless and reliable EMG-driven control. This solution has the promise to retain the high performance of the currently employed non-EMG-driven controllers for rhythmic activities such as walking, whilst improving the performance of volitional activities such as task switching or non-repetitive movements. Although EMG-driven controllers suffer from many drawbacks, such as high sensitivity to noise, recent progress in invasive neural interfaces for prosthetic control (bionics) will allow to build a more reliable connection between the user and the MLLPs. Therefore, advancements in powered MLLPs with integrated EMG-driven control have the potential to strongly reduce the effects of psychosomatic conditions and musculoskeletal degenerative pathologies that are currently affecting lower limb amputees

Multi-Classifier Fusion Strategy for Activity and Intent Recognition of Torso Movements

As assistive, wearable robotic devices are being developed to physically assist their users, it has become crucial to develop safe, reliable methods to coordinate the device with the intentions and motions of the wearer. This dissertation investigates the recognition of user intent during flexion and extension of the human torso in the sagittal plane to be used for control of an assistive exoskeleton for the human torso. A multi-sensor intent recognition approach is developed that combines information from surface electromyogram (sEMG) signals from the user’s muscles and inertial sensors mounted on the user’s body. Intent recognition is implemented by following a pattern classification approach, wherein a linear discriminant analysis (LDA) based method of pattern classification is utilized. This method of classification builds on a traditional LDA by utilizing multiple classifiers from multiple sensors that are combined together using a majority voting based classifier fusion scheme, to deliver improved classification performance. Additionally, there is a focus on identification of suitable features for classification. Extraction of features in the time, frequency and time-frequency domains is discussed. Wavelet transform methods are employed for targeted extraction of nonlinear time-frequency domain features, and the effectiveness of these features in improving classification performance is emphasized. Experimental results using sEMG and inertial signals recorded from human subjects, to evaluate the pattern classification and feature extraction methods are presented. Results show that a combined sensor approach that utilizes both inertial and sEMG data leads to a 70% improvement in classification performance. Results also show that the use of multiple time-frequency domain features in conjunction with majority voting based classifier-fusion leads to an additional 75% improvement in classification performance, with a best case of up to 97% accuracy in recognizing user intent. This research has provided an effective demonstration of leveraging nonlinear time-frequency domain features with linear methods of classification to deliver accurate and computationally efficient intent recognition. In addition, the research effort has also developed a library of features that can serve as a starting point for future efforts in classifying torso motions

Programmation différentielle dynamique pour la commande optimale d’actionneurs biomimétiques

RÉSUMÉ Les prothèses sont des dispositifs permettant de remplacer la fonction d’un organe ou d’un membre. Elles doivent donc être le plus biofidèle possible pour rendre aux patients l’intégralité de leurs capacités perdues. Dans le cas d’un membre, il est donc nécessaire de reproduire le comportement des muscles et des os lors des mouvements. Or, ce dernier est bien plus complexe qu’il n’y paraît, et ce notamment à cause de la cocontraction des muscles agonistes et antagonistes qui assure stabilité et protection des articulations et qui permet d’en moduler la rigidité. Ce dernier point est très important, pour deux raisons principales : il permet d’optimiser la consommation d’énergie et d’interagir avec l’environnement en toute sécurité. Afin de concevoir des prothèses biofidèles, il est donc pertinent de chercher à reproduire cette rigidité variable. Plusieurs approches ont été imaginées, celle que nous étudions ici consiste à employer des actionneurs à impédance variable. Ces actionneurs possèdent des ressorts dans leur mécanisme, que l’on peut régler de façon à changer la rigidité du système au cours du temps. Le problème principal lié à ces actionneurs est leur commande. Il est possible de la découpler, i.e. de contrôler séparément la rigidité et la position de l’actionneur, mais on n’exploite pas dans ce cas le plein potentiel de la rigidité variable. Notre problématique se résume donc ainsi : comment calculer la commande optimale non découplée d’un actionneur à impédance variable ? Dans un premier temps, nous avons modélisé un actionneur à impédance variable et linéarisé sa fonction d’évolution. Puis nous avons testé plusieurs algorithmes (la méthode par collocation et le régulateur linéaire quadratique) parmi les plus classiques en contrôle optimal pour le contrôler à impédance fixe. Si la méthode de collocation exigeait un trop long temps de calcul, les résultats avec le régulateur linéaire quadratique étaient très satisfaisants, tant en convergence qu’en stabilité. Toutefois, cette méthode n’est pas envisageable à impédance variable. En effet, la linéarisation des équations n’est plus possible. L’algorithme Differential Dynamic Programming (DDP) permet de résoudre le problème de contrôle optimal pour des systèmes non-linéaires. Son fonctionnement se rapproche de celui d’un descente de Newton dans laquelle on tiendrait compte des dérivées secondes, ce qui permet justement d’étudier des systèmes pour lesquels la linéarisation est impossible. Nous avons testé et validé cet algorithme en simulation, notamment sur un saut réalisé par un robot humanoïde équipé d’actionneurs à impédance variable. Néanmoins, la complexité de cet algorithme est très élevée et limite son utilisation en temps réel. Nous avons donc cherché à l’optimiser dans un second temps. Afin de limiter le nombre d’opérations effectuées au cours d’un calcul par DDP, nous avons employé l’hyptohèse de Gauss-Newton pour approcher les dérivées secondes. Nous avons alors reformulé l’algorithme en utilisant les racines carrées des matrices en jeu et diminué la complexité : de 11n3 opérations à chaque cycle à 3n3 opérations, où n est la taille de notre fenêtre de prévision. En conclusion, on a montré qu’il est possible de contrôler de manière non découplée un actionneur à impédance variable, grâce à l’algorithme DDP, en simulation. La suite future de ce travail en serait la validation expérimentale.----------ABSTRACT Prostheses are artificial devices that replace organs or limbs. Therefore, they must be as much biofidelic as possible to provide back to patients close to their lost capacities. In the case of a limb, the complete behavior of the muscles and bones has to be reproduced during movement. However, it is much more complex than it seems, especially because of the cocontraction of agonist and antagonist muscles. This phenomenon ensures stability and protection of the joints, but also the modulation of the stiffness. The later is very important for two main reasons : the optimization of energy consumption, and safe interaction with the environment. To design biofidelic prostheses, it is highly desirable to reproduce this variable stiffness. Several approaches have been proposed, the one we study here is to use of Variable Impedance Actuators (VIA). The mechanical system of these actuators have springs inside and so, the global stiffness can be changed over time. The main problem with VIA is their control. We can decouple it, this means controlling separately position and stiffness, but in this way, the full potential of variable stiffness is not exploited. Our research question can be summed up as follow : How to perform the optimal non decoupled control of VIA ? At first, we modelized a VIA and linearized its evolution function. Then, we tested several algorithms (collocation and Linear Quadratic Regulator (LQR)) among the classical methods to control it with fixed stiffness. The collocation method required a too long computation time, and we hence prefered the LQR. The results using LQR were very satisfactory both in terms of convergence and in robustness. However this method is not efficient with variable stiffness, and we can’t linearize the equations. The algorithm Differential Dyamic Programming (DDP) solves optimal control problem for non linear systems. It is similar to a Newton descent, in which we take into account the second derivatives. This is precisely the key to non linearizable systems. We tested and validated with simulations this algorithm, using a jump performed by a humanoid robot with VIA. However, the complexity of DDP tends to be very high and limits its use in real time. We therefore sought to optimize it in a second time. To limit the number of operations performed during a computation, we used the Gauss Newton hyptohesis to approximate the second derivatives. We then used a square-root formulation of the DDP algorithm. The complexity decreased from 11n3 operations at each cycle to 3n3. To conclude, uncoupled control of VIA is possible using DDP in a simulation environment. The future step of this work would be the experimental validation of the results