We propose a variable stiffness prosthetic hand and present its surface electromyography (sEMG) interface for tele-impedance control. Such an interface, together with variable stiffness actuation (VSA), enables the amputee to modulate the impedance of the prosthetic limb to properly match the requirements of the task at hand, while performing activities of daily living. Both the desired position and stiffness of the hand are estimated through sEMG signals and these estimates are employed to control the VSA hand prosthesis. In particular, regulation of finger impedance is managed through the impedance measurements of the intact forearm; this control takes place naturally and automatically as the amputee interacts with the environment, while position of the hand prosthesis is regulated intentionally by the amputee through the estimated position of the thumb, extracted from sEMG signals of the dysfunctional agonistic and antagonistic thumb muscles that are embedded in the forearm. The proposed approach is advantageous, since the impedance regulation takes place naturally from task to task or during execution of a single task without requiring amputees' attention and diminishing their functional capability. Consequently, the proposed interface does not require long training periods or interfere with control of intact body segments, and provides amputee with easiness in use. The performance of the tele-impedance control of the VSA hand prosthesis is experimentally evaluated. Experimental results indicate that both position and stiffness can be adequately estimated using sEMG signals and regulated through VSA.
A finger exoskeleton has been developed to aid treatment of tendon injuries. The exoskeleton is designed to assist flexion/extension motions of a finger within its full range, in a natural and coordinated manner, while keeping the tendon tension within acceptable limits to avoid gap formation or rupture of the suture. In addition to offering robot assisted operation modes for tendon therapies, the exoskeleton can provide quantitative measures of recovery that can help guide the physical therapy program. Usability studies have been conducted and efficacy of exoskeleton driven exercises to reduce muscle requitement levels has been demonstrated.
We present ASSISTON-FINGER, a novel under-actuated active exoskeleton for robot-assisted tendon therapy of human fingers. The primary use for the exoskeleton is to assist flexion/extension motions of a finger within its full range, while decreasing voluntary muscle contractions helping to keep the tendon tension levels to stay within acceptable limits, avoiding gap formation or rupture of the suture. The device can also be employed to administer range of motion (RoM)/strengthening exercises. ASSISTON-FINGER is designed to be passively back-driveable, can cover the whole RoM of patients, and can do so in a natural and coordinated manner. In particular, the device employs human finger as an integral part of its kinematics and when coupled to a human operator, the parallel kinematic structure of exoskeleton supports three independent degrees of freedom, dictated by the kinematics of the human finger. Automatically aligning its joint axes to match finger joint axes, ASSISTON-FINGER can guarantee ergonomy and comfort throughout the therapy. The self-aligning feature also significantly shortens the setup time required to attach the patient to the exoskeleton. We present the kinematic type selection for the exoskeleton to satisfy the design requirements for tendon therapy applications, detail optimal dimensional synthesis of the device considering trade-offs between multiple design criteria and discuss implementation details of the exoskeleton. We also present feasibility studies conducted on healthy volunteers and provide statistical evidence on the efficacy of exoskeleton driven exercises in keeping the average muscle recruitment and the maximum tendon tension levels as low as human guided therapies.
In this work, to guarantee the Pisa/IIT SoftHand's grasp robustness against slippage, three reflex control modes, namely Current, Pose and Impedance, are implemented and experimentally evaluated. Towards this objective, ThimbleSense fingertip sensors are designed and integrated into the thumb and middle fingers of the SoftHand for real-time detection and control of the slippage. Current reflex regulates the restoring grasp forces of the hand by modulating the motor's current profile according to an update law. Pose and Impedance reflex modes instead replicate this behaviour by implementing an impedance control scheme. The difference between the two latter is that the stiffness gain in Impedance reflex mode is being varied in addition to the hand pose, as a function of the slippage on the fingertips. Experimental setup also includes a seven degrees-of-freedom robotic arm to realize consistent trajectories (e.g. lifting) among three control modes for the sake of comparison. Different test objects are considered to evaluate the efficacy of the proposed reflex modes in our experimental setup. Results suggest that task-appropriate restoring forces can be achieved using Impedance reflex due to its capability in demonstrating instantaneous and rather smooth reflexive behaviour during slippage. Preliminary experiments on five healthy human subjects provide evidence on the similarity of the control concepts exploited by the humans and the one realized by the Impedance reflex, highlighting its potential in prosthetic applications
We present the design, implementation, and experimental evaluation of a low-cost, customizable, easy-to-use transradial hand prosthesis capable of adapting its compliance. Variable stiffness actuation (VSA) of the prosthesis is based on antagonistically arranged tendons coupled to nonlinear springs driven through a Bowden cable based power transmission. Bowden cable based antagonistic VSA can, not only regulate the stiffness and the position of the prosthetic hand but also enables a light-weight and low-cost design, by the opportunistic placement of motors, batteries, and controllers on any convenient location on the human body, while nonlinear springs are conveniently integrated inside the forearm. The transradial hand prosthesis also features tendon driven underactuated compliant fingers that allow natural adaption of the hand shape to wrap around a wide variety of object geometries, while the modulation of the stiffness of their drive tendons enables the prosthesis to perform various tasks with high dexterity. The compliant fingers of the prosthesis add inherent robustness and flexibility, even under impacts. The control of the variable stiffness transradial hand prosthesis is achieved by an sEMG based natural human-machine interface.
We propose, implement, and evaluate a natural human-machine control interface for a variable stiffness transradial hand prosthesis that achieves tele-impedance control through surface electromyography (sEMG) signals. This interface, together with variable stiffness actuation (VSA), enables an amputee to modulate the impedance of the prosthetic limb to properly match the requirements of a task while performing activities of daily living (ADL). Both the desired position and stiffness references are estimated through sEMG signals and used to control the VSA hand prosthesis. In particular, regulation of hand impedance is managed through the impedance measurements of the intact upper arm; this control takes place naturally and automatically as the amputee interacts with the environment, while the position of the hand prosthesis is regulated intentionally by the amputee through the estimated position of the shoulder. The proposed approach is advantageous since the impedance regulation takes place naturally without requiring amputees' attention and diminishing their functional capability. Consequently, the proposed interface is easy to use, does not require long training periods or interferes with the control of intact body segments. This control approach is evaluated through human subject experiments conducted over able volunteers where adequate estimation of references and independent control of position and stiffness are demonstrated.
Ozetçe -Bu çalışmada, gönüllülerin deneydeki görev zorluklarına baglı olarak gösterdikleri istek düzeylerinin tespitini yapan elektroensefologram (EEG) tabanlı bir beyin-bilgisayar arayüzü (BBA) yaklaşımı sunuyoruz. Bunun için farklı agırlıklardaki yükler sag elde kaldırılırken dirsek bükmesi ve takiben gevşetilmesi hareketinin yapılması sırasında oluşan olgu ile ilgili eşzamanlanma ve eşzamanlanma bozulumu desenleri dogrusal ayırtaç analizi (DAA) ile sınıflandırılmaktadır. Sonuçlarımız degişen zorluklardaki görevlerin EEG sinyallerine dayalı olarak sınıflandırılabildigini göstermektedir. Ayrıca, EEG ve yüzey elektromiyogram (yEMG) sinyallerinden tespit edilen istek düzeyleri arasında bir ilinti analizi sunulmaktadır ve iki sinyal arasında tespit edilen belirli seviyedeki dogrusal ilişki sonuçlarımızı desteklemektedir. Fiziklel rehabilitasyon tedavileri sırasında hastaların istek seviyelerini belirleyebilmek hastanın terapideki görevine daha aktif katılımını saglamakta ve robotik rehabilitasyon sistemlerinin etkinligini arttırmaktadır. Bu nedenle,önerdigimiz tipte bir istek düzeyi belirleme yaklaşımının bu tür fiziksel rehabilitasyon süreçlerinde faydalı olma potansiyeli mevcuttur.Anahtar Kelimeler-EEG, BBA, yEMG, istek düzeyi, rehabilitasyon Abstract-In this study, an approach which detects the level of intention in response to the difficulty of the task executed by the subjects in an electroencephalogram (EEG) based braincomputer interface (BCI), is proposed. For this purpose, event related synchronization and desynchronization patterns which occur in the process of lifting different weights by the right hand by executing elbow flexion and extension movements, are classified by the linear discriminant analysis (LDA). Our results show that the varying difficulty of the task can be classified based on the EEG signals. In addition, a correlation analysis between the intention levels detected from EEG and surface electromyogram (sEMG) signals is presented and the detected level of correlation between these two signals supports our previous inference. Determining the level of intention of the patients during the physical rehabilitation treatment, ensures the patients' active participation in their therapy task and increases the effectiveness of the robotic rehabilitation system. Accordingly, the type of intention level detection approach we propose here has the potential to be useful in such physical rehabilitation processes.
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