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

Abstract

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.

Intent-aware control in kinematically redundant systems: Towards collaborative wearable robots

“…This approach provides a reliable interface through which the human can indirectly control the robotic joint. Compensation cancellation control [37], however, leverages the fact that impaired users utilize their kinematic redundancies to create their desired motion at the end-effector; e.g., utilizing hip and shoulder to reach forward which compromises the ergonomics [38,39]. Our previous works [40][41][42] show the effectiveness of movement-based approach (over Myoelectric control) in providing an ergonomic posture to the human user.…”

Assessment of an Automatic Prosthetic Elbow Control Strategy Using Residual Limb Motion for Transhumeral Amputated Individuals With Socket or Osseointegrated Prostheses

“…Only reaching phases were used to train the models; the return phases were not included. Further information on physiological data acquisition and design of the regression model is provided in [30].…”

Playing the piano with a robotic third thumb: Assessing constraints of human augmentation

“…we use an existing limb to operate an additional one. While many active upper limb prosthetics with non-invasive interfaces rely on EMG from residual muscles [56][57][58] , there is still interest in movement-based control due to challenges with EMG interfaces 59,60 . Foot motions in particular, detected through various sensing modalities, have been used as prosthetic interfaces [61][62][63][64] .…”