Manelle Merad

Isometric Torque Control for Neuromuscular Electrical Stimulation With Time-Varying Input Delay

Previous results have shown experimental evidence that the muscle response to neuromuscular electrical stimulation (NMES) is delayed; the time lag is often referred to as electromechanical delay. NMES closed-loop control methods have been developed to compensate for a known constant input delay. However, as a muscle fatigues, this delay increases. This paper develops a feedback controller that robustly compensates for the time-varying delay of an uncertain muscle model during isometric contractions. The controller is proven to yield global uniformly ultimately bounded torque tracking error. Experimental results illustrate the effectiveness of the developed controller and the time-varying nature of the delayed response.

The Time-Varying Nature of Electromechanical Delay and Muscle Control Effectiveness in Response to Stimulation-Induced Fatigue

Neuromuscular electrical stimulation (NMES) and Functional Electrical Stimulation (FES) are commonly prescribed rehabilitative therapies. Closed-loop NMES holds the promise to yield more accurate limb control, which could enable new rehabilitative procedures. However, NMES/FES can rapidly fatigue muscle, which limits potential treatments and presents several control challenges. Specifically, the stimulation intensity-force relation changes as the muscle fatigues. Additionally, the delayed response between the application of stimulation and muscle force production, termed electromechanical delay (EMD), may increase with fatigue. This paper quantifies these effects. Specifically, open-loop fatiguing protocols were applied to the quadriceps femoris muscle group of able-bodied individuals under isometric conditions, and the resulting torque was recorded. Short pulse trains were used to measure EMD with a thresholding method while long duration pulse trains were used to induce fatigue, measure EMD with a cross-correlation method, and construct recruitment curves. EMD was found to increase significantly with fatigue, and the control effectiveness (i.e., the linear slope of the recruitment curve) decreased with fatigue. Outcomes of these experiments indicate an opportunity for improved closed-loop NMES/FES control development by considering EMD to be time-varying and by considering the muscle recruitment curve to be a nonlinear, time-varying function of the stimulation input.

Intuitive Control of a Prosthetic Elbow

Many transhumeral amputees deplore the lack of functionality of their prosthesis, mostly caused by a counter-intuitive control strategy. This work is the first implementation of an automatic prosthesis control approach based on natural coordinations between upper limb joints and IMU-based humeral orientation measurement. One healthy individual was able to use the prosthetic elbow, fitted with a prosthetic forearm and attached to the subject’s upper arm, to point at targets with an encouragingly small error.

Towards the implementation of natural prosthetic elbow motion using upper limb joint coordination

The control of an active prosthetic elbow is problematic for most transhumeral amputees and a functional solution providing intuitive control over active multi-joint prosthetic upper limbs is yet to be found. The method in this paper uses IMU-based upper arm kinematics to predict the elbow motion based on upper limb joint coordinations during pointing movements. A RBFN-based regression was performed to model the shoulder/elbow coordination. The prediction results indicate that such an approach is ready to be implemented on current transhumeral prostheses equipped with embedded motion sensors like IMUs. Different algorithm training methods to obtain better prediction performance are also investigated.

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.

Intuitive prosthetic control using upper limb inter-joint coordinations and IMU-based shoulder angles measurement: A pilot study

Commercialized upper limb prostheses do not match the expectations of amputated people, especially transhumeral amputees. Most of them report a lack of functionality, mostly explained by a counter-intuitive control strategy. This paper presents the first implementation of an automatic prosthesis control approach based on natural coordinations between upper limb joints and IMU-based humeral orientation measurement. Two healthy individuals were able to use the prosthetic forearm attached to their upper arm to point at targets in a 3D workspace with a reasonable error. The results demonstrate the potential applications of automatizing the motion of some joints along the upper limb, in the same way as human upper limbs are controlled.