Biceps brachii synergy and its contribution to target reaching tasks within a virtual cube

Ces dernières années, des travaux importants ont été observés dans le développement du contrôle prothétique afin d'aider les personnes amputées du membre supérieur à améliorer leur qualité de vie au quotidien. Certaines prothèses myoélectriques modernes des membres supérieurs disponibles dans le commerce ont de nombreux degrés de liberté et nécessitent de nombreux signaux de contrôle pour réaliser plusieurs tâches fréquemment utilisées dans la vie quotidienne. Pour obtenir plusieurs signaux de contrôle, de nombreux muscles sont requis mais pour les personnes ayant subi une amputation du membre supérieur, le nombre de muscles disponibles est plus ou moins réduit selon le niveau de l’amputation. Pour accroître le nombre de signaux de contrôle, nous nous sommes intéressés au biceps brachial, vu qu’anatomiquement il est formé de 2 chefs et que de la présence de compartiments a été observée sur sa face interne. Physiologiquement, il a été trouvé que les unités motrices du biceps sont activées à différents endroits du muscle lors de la production de diverses tâches fonctionnelles. De plus, il semblerait que le système nerveux central puisse se servir de la synergie musculaire pour arriver à facilement produire plusieurs mouvements. Dans un premier temps on a donc identifié que la synergie musculaire était présente chez le biceps de sujets normaux et on a montré que les caractéristiques de cette synergie permettaient d’identifier la posture statique de la main lorsque les signaux du biceps avaient été enregistrés. Dans un deuxième temps, on a réussi à démontrer qu’il était possible, dans un cube présenté sur écran, à contrôler la position d’une sphère en vue d’atteindre diverses cibles en utilisant la synergie musculaire du biceps. Les techniques de classification utilisées pourraient servir à faciliter le contrôle des prothèses myoélectriques.In recent years, important work has been done in the development of prosthetic control to help upper limb amputees improve their quality of life on a daily basis. Some modern commercially available upper limb myoelectric prostheses have many degrees of freedom and require many control signals to perform several tasks commonly used in everyday life. To obtain several control signals, many muscles are required, but for people with upper limb amputation, the number of muscles available is more or less reduced, depending on the level of amputation. To increase the number of control signals, we were interested in the biceps brachii, since it is anatomically composed of 2 heads and the presence of compartments was observed on its internal face. Physiologically, it has been found that the motor units of the biceps are activated at different places of the muscle during production of various functional tasks. In addition, it appears that the central nervous system can use muscle synergy to easily produce multiple movements. In this research, muscle synergy was first identified to be present in the biceps of normal subjects, and it was shown that the characteristics of this synergy allowed the identification of static posture of the hand when the biceps signals had been recorded. In a second investigation, we demonstrated that it was possible in a virtual cube presented on a screen to control online the position of a sphere to reach various targets by using muscle synergy of the biceps. Classification techniques have been used to improve the classification of muscular synergy features, and these classification techniques can be integrated with control algorithm that produces dynamic movement of myoelectric prostheses to facilitate the training of prosthetic control

Assessment of an automatic prosthetic elbow control strategy using residual limb motion for transhumeral amputated individuals with socket or osseointegrated prostheses

International audienceMost transhumeral amputated individuals deplore the lack of functionality of their prosthesis due to control-related limitations. Commercialized prosthetic elbows are controlled via myoelectric signals, yielding complex control schemes when users have to control an entire prosthetic limb. Limited control yields the development of compensatory strategies. An alternative control strategy associates residual limb motions to automatize the prosthetic elbow motion using a model of physiological shoulder/elbow synergies. Preliminary studies have shown that elbow motion could be predicted from residual limb kinematic measurements, but results with transhumeral amputated individuals were lacking. This study focuses on the experimental assessment of automatic prosthetic elbow control during a reaching task, compared to conventional myoelectric control, with six transhumeral amputated individuals, among whom, three had an osseointegrated device. Part of the recruited participants had an osseointegrated prosthetic device. The task was achieved within physiological precision errors with both control modes. Automatic elbow control reduced trunk compensations, and restored a physiologically-like shoulder/elbow movement synchronization. However, the kinematic assessment showed that amputation and prosthesis wear modifies the shoulder movements in comparison with physiological shoulder kinematics. Overall, participants described the automatic elbow control strategy as intuitive, and this work highlights the interest of automatized prosthetic elbow motion

Enhancing Biomechanical Function through Development and Testing of Assistive Devices for Shoulder Impairment and Total Limb Amputation

Assistive devices serve as a potential for restoring sensorimotor function to impaired individuals. My research focuses on two assistive devices: a passive shoulder exoskeleton and a muscle-driven endoprosthesis (MDE). Previous passive shoulder exoskeletons have focused on testing during static loading conditions in the shoulder. However, activities of daily living are based on dynamic tasks. My research for passive shoulder exoskeletons analyzes the effect that a continuous passive assistance has on shoulder biomechanics. In my research I showed that passive assistance decreases the muscular activation in muscles responsible for positive shoulder exoskeleton. An MDE has the potential to have accurate and precise control of movement as well as restore a sense of proprioception to the user. Such a transformative and invasive device has never previously been tested. Therefore, my research focused on analyzing fundamental principles of the MDE in an in-vivo rabbit model. The two concepts I tested in my research were the feasibility of implanting an orthopedic device underneath the skin at the distal end of a limb following amputation and the locomotor restorative capabilities of an artificial tendon used for muscle-device connection. In my work I proved the feasibility of implanting fully-footed rigid endoprostheses underneath the skin and isolated the primary factors for a successful surgery and recovery. In addition, my research showed that although artificial tendons have the potential to restore locomotor function, proper in-situ tendon lengths must be achieved for optimal movement. This research informed the design and testing of a fully jointed muscle-driven endoprosthesis prototype

Towards Natural Control of Artificial Limbs

The use of implantable electrodes has been long thought as the solution for a more natural control of artificial limbs, as these offer access to long-term stable and physiologically appropriate sources of control, as well as the possibility to elicit appropriate sensory feedback via neurostimulation. Although these ideas have been explored since the 1960’s, the lack of a long-term stable human-machine interface has prevented the utilization of even the simplest implanted electrodes in clinically viable limb prostheses.In this thesis, a novel human-machine interface for bidirectional communication between implanted electrodes and the artificial limb was developed and clinically implemented. The long-term stability was achieved via osseointegration, which has been shown to provide stable skeletal attachment. By enhancing this technology as a communication gateway, the longest clinical implementation of prosthetic control sourced by implanted electrodes has been achieved, as well as the first in modern times. The first recipient has used it uninterruptedly in daily and professional activities for over one year. Prosthetic control was found to improve in resolution while requiring less muscular effort, as well as to be resilient to motion artifacts, limb position, and environmental conditions.In order to support this work, the literature was reviewed in search of reliable and safe neuromuscular electrodes that could be immediately used in humans. Additional work was conducted to improve the signal-to-noise ratio and increase the amount of information retrievable from extraneural recordings. Different signal processing and pattern recognition algorithms were investigated and further developed towards real-time and simultaneous prediction of limb movements. These algorithms were used to demonstrate that higher functionality could be restored by intuitive control of distal joints, and that such control remains viable over time when using epimysial electrodes. Lastly, the long-term viability of direct nerve stimulation to produce intuitive sensory feedback was also demonstrated.The possibility to permanently and reliably access implanted electrodes, thus making them viable for prosthetic control, is potentially the main contribution of this work. Furthermore, the opportunity to chronically record and stimulate the neuromuscular system offers new venues for the prediction of complex limb motions and increased understanding of somatosensory perception. Therefore, the technology developed here, combining stable attachment with permanent and reliable human-machine communication, is considered by the author as a critical step towards more functional artificial limbs

Predicting Wrist Joint Angles from the Kinematics of the Arm: Application to the Control of Upper Limb Prostheses

Automation of wrist rotations in upper limb prostheses allows simplification of the human–machine interface, reducing the user’s mental load and avoiding compensatory movements. This study explored the possibility of predicting wrist rotations in pick-and-place tasks based on kinematic information from the other arm joints. To do this, the position and orientation of the hand, forearm, arm, and back were recorded from five subjects during transport of a cylindrical and a spherical object between four different locations on a vertical shelf. The rotation angles in the arm joints were obtained from the records and used to train feed-forward neural networks (FFNNs) and time-delay neural networks (TDNNs) in order to predict wrist rotations (flexion/extension, abduction/adduction, and pronation/supination) based on the angles at the elbow and shoulder. Correlation coefficients between actual and predicted angles of 0.88 for the FFNN and 0.94 for the TDNN were obtained. These correlations improved when object information was added to the network or when it was trained separately for each object (0.94 for the FFNN, 0.96 for the TDNN). Similarly, it improved when the network was trained specifically for each subject. These results suggest that it would be feasible to reduce compensatory movements in prosthetic hands for specific tasks by using motorized wrists and automating their rotation based on kinematic information obtained with sensors appropriately positioned in the prosthesis and the subject’s body.This publication is part of the R&D project PID2020-118021RB-I00 funded by MICIN/AEI/ 10.13039/501100011033

Movement-Based Control for Upper-Limb Prosthetics: Is the Regression Technique the Key to a Robust and Accurate Control?

Due to the limitations of myoelectric control (such as dependence on muscular fatigue and on electrodes shift, difficulty in decoding complex patterns or in dealing with simultaneous movements), there is a renewal of interest in the movement-based control approaches for prosthetics. The latter use residual limb movements rather than muscular activity as command inputs, in order to develop more natural and intuitive control techniques. Among those, several research works rely on the interjoint coordinations that naturally exist in human upper limb movements. These relationships are modeled to control the distal joints (e.g., elbow) based on the motions of proximal ones (e.g., shoulder). The regression techniques, used to model the coordinations, are various [Artificial Neural Networks, Principal Components Analysis (PCA), etc.] and yet, analysis of their performance and impact on the prosthesis control is missing in the literature. Is there one technique really more efficient than the others to model interjoint coordinations? To answer this question, we conducted an experimental campaign to compare the performance of three common regression techniques in the control of the elbow joint on a transhumeral prosthesis. Ten non-disabled subjects performed a reaching task, while wearing an elbow prosthesis which was driven by several interjoint coordination models obtained through different regression techniques. The models of the shoulder-elbow kinematic relationship were built from the recordings of fifteen different non-disabled subjects that performed a similar reaching task with their healthy arm. Among Radial Basis Function Networks (RBFN), Locally Weighted Regression (LWR), and PCA, RBFN was found to be the most robust, based on the analysis of several criteria including the quality of generated movements but also the compensatory strategies exhibited by users. Yet, RBFN does not significantly outperform LWR and PCA. The regression technique seems not to be the most significant factor for improvement of interjoint coordinations-based control. By characterizing the impact of the modeling techniques through closed-loop experiments with human users instead of purely offline simulations, this work could also help in improving movement-based control approaches and in bringing them closer to a real use by patients

The future of upper extremity rehabilitation robotics: research and practice

The loss of upper limb motor function can have a devastating effect on people’s lives. To restore upper limb control and functionality, researchers and clinicians have developed interfaces to interact directly with the human body’s motor system. In this invited review, we aim to provide details on the peripheral nerve interfaces and brain‐machine interfaces that have been developed in the past 30 years for upper extremity control, and we highlight the challenges that still remain to transition the technology into the clinical market. The findings show that peripheral nerve interfaces and brain‐machine interfaces have many similar characteristics that enable them to be concurrently developed. Decoding neural information from both interfaces may lead to novel physiological models that may one day fully restore upper limb motor function for a growing patient population.Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/155489/1/mus26860_am.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/155489/2/mus26860.pd

Advancing Medical Technology for Motor Impairment Rehabilitation: Tools, Protocols, and Devices

Excellent motor control skills are necessary to live a high-quality life. Activities such as walking, getting dressed, and feeding yourself may seem mundane, but injuries to the neuromuscular system can render these tasks difficult or even impossible to accomplish without assistance. Statistics indicate that well over 100 million people are affected by diseases or injuries, such as stroke, Parkinson’s Disease, Multiple Sclerosis, Cerebral Palsy, peripheral nerve injury, spinal cord injury, and amputation, that negatively impact their motor abilities. This wide array of injuries presents a challenge to the medical field as optimal treatment paradigms are often difficult to implement due to a lack of availability of appropriate assessment tools, the inability for people to access the appropriate medical centers for treatment, or altogether gaps in technology for treating the underlying impairments causing the disability. Addressing each of these challenges will improve the treatment of movement impairments, provide more customized and continuous treatment to a larger number of patients, and advance rehabilitative and assistive device technology. In my research, the key approach was to develop tools to assess and treat upper extremity movement impairment. In Chapter 2.1, I challenged a common biomechanical[GV1] modeling technique of the forearm. Comparing joint torque values through inverse dynamics simulation between two modeling platforms, I discovered that representing the forearm as a single cylindrical body was unable to capture the inertial parameters of a physiological forearm which is made up of two segments, the radius and ulna. I split the forearm segment into a proximal and distal segment, with the rationale being that the inertial parameters of the proximal segment could be tuned to those of the ulna and the inertial parameters of the distal segment could be tuned to those of the radius. Results showed a marked increase in joint torque calculation accuracy for those degrees of freedom that are affected by the inertial parameters of the radius and ulna. In Chapter 2.2, an inverse kinematic upper extremity model was developed for joint angle calculations from experimental motion capture data, with the rationale being that this would create an easy-to-use tool for clinicians and researchers to process their data. The results show accurate angle calculations when compared to algebraic solutions. Together, these chapters provide easy-to-use models and tools for processing movement assessment data. In Chapter 3.1, I developed a protocol to collect high-quality movement data in a virtual reality task that is used to assess hand function as part of a Box and Block Test. The goal of this chapter is to suggest a method to not only collect quality data in a research setting but can also be adapted for telehealth and at home movement assessment and rehabilitation. Results indicate that the data collected in this protocol are good and the virtual nature of this approach can make it a useful tool for continuous, data driven care in clinic or at home. In Chapter 3.2 I developed a high-density electromyography device for collecting motor unit action potentials of the arm. Traditional surface electromyography is limited by its ability to obtain signals from deep muscles and can also be time consuming to selectively place over appropriate muscles. With this high-density approach, muscle coverage is increased, placement time is decreased, and deep muscle activity can potentially be collected due to the high-density nature of the device[GV2] . Furthermore, the high-density electromyography device is built as a precursor to a high-density electromyography-electrical stimulation device for functional electrical stimulation. The customizable nature of the prototype in Chapter 3.2 allows for the implementation both recording and stimulating electrodes. Furthermore, signal results show that the electromyography data obtained from the device are of high quality and are correlated with gold standard surface electromyography sensors. One key factor in a device that can record and then stimulate based on the information from the recorded signals is an accurate movement intent decoder. High-quality movement decoders have been designed by closed-loop device controllers in the past, but they still struggle when the user interacts with objects of varying weight due to underlying alterations in muscle signals. In Chapter 4, I investigate this phenomenon by administering an experiment where participants perform a Box and Block Task with objects of 3 different weights, 0 kg, 0.02 kg, and 0.1 kg. Electromyography signals of the participants right arm were collected and co-contraction levels between antagonistic muscles were analyzed to uncover alterations in muscle forces and joint dynamics. Results indicated contraction differences between the conditions and also between movement stages (contraction levels before grabbing the block vs after touching the block) for each condition. This work builds a foundation for incorporating object weight estimates into closed-loop electromyography device movement decoders. Overall, we believe the chapters in this thesis provide a basis for increasing availability to movement assessment tools, increasing access to effective movement assessment and rehabilitation, and advance the medical device and technology field