Control of Upper Limb Prostheses: Terminology and Proportional Myoelectric Control—A Review

IntroductionThe human history has been accompanied by accidental trauma, war, and congenital anomalies.Consequently, amputation and deformity have been dealt with, one way or another, throughout the ages.More than one million individuals in the United States today are living with limb amputations (Adams et al., 1999), in which there are approximately 100,000 patients with an upper limb amputation.The wars in Iraq and Afghanistan have added to this number.According to the survey results of the Second China National Sample Survey on Disables (SCNSSD 2006) led by the National Statistics Bureau in 2006, approximately 8% of physical disables, or 2.26 million people, live with limb amputations in China alone.Natural disasters and accidents have been making this number increase.The massive earthquakes that occurred in May 2008, Sichuan Province, China, recently increased about 20 thousand of new limb amputees.Expectations for control of upper limb prostheses have always been high because of the standard established by able-bodied dexterity.Most commercially available upper limb prostheses are either body-powered or electrical motor powered.The body-powered prostheses are operated by certain movements of the amputees' body through a system of cables, harnesses, and sometimes, manual control.In order to operate a body-powered prosthesis, the upper limb amputees have to possess significant strength and control over various body parts, including the shoulders, chest, and residual limb which must have sufficient residual limb length, musculature, and range of motion.Exaggerated movements of the body are captured by harness systems and are transferred through cables to operate the hand, wrist, or elbow movements of a prosthesis.With some advantages such as low cost, high reliability, and some kinesthetic feedback provided by the harness system, body-powered prostheses are still widely accepted by the upper limb amputees worldwide, especially in some developing countries.However, with this inappropriate control approach, body-powered upper limb prostheses are limited in utility, frustratingly slow to operate, awkward to maintain, and can operate only one joint at a time.Myoelectric signals detected with electrodes placed on the skin surface overlying the muscles, well-known as electromyography (EMG), have been used in control of motorized upper-limb prostheses for several decades (Kay & Newman, 1975;Parker & Scott, 1986).The www.intechopen.com

The myoelectric control of hand prosthesis commercially available is simple and limits the user to very basic operations. Although in the academic research for prosthesis control a large variety of advanced control methods has been developed, none of them has replaced the current industrial state of the art, yet. In this PhD project I have investigated and developed an approach towards intuitive prostheses control, based on new signal-processing and regression algorithms. By introducing a novel adaptive pre-processing algorithm (ACAR) for the surface EMG signals and designing a regression system based on a non-negative matrix factorization, I have developed a myocontrol system capable of online control of upper limb prosthesis for two degrees of freedom, simultaneously and proportionally. Additionally, I have developed a virtual evaluation paradigm, which can assess the control performance of important hand movements necessary for daily life activities. This online assessment goes beyond the state of the art of myoelectric control research, which is done offline. That is without the interaction with the subject. The resulting myocontrol system and virtual evaluation paradigm have been tested in both intact-limb subjects and subjects with limb deficiencies. In these studies, the benefits of the developed algorithms have been confirmed. The scientific results and developments of this project have been the basis for additional publications and scientific achievements by the Department of Neurorehabilitation Engineering and its scientific partners. This underlines the impact of this work in the field of myoelectric control for upper limb prostheses.

BackgroundPattern recognition control of prosthetic hands take inputs from one or more myoelectric sensors and controls one or more degrees of freedom. However, most systems created allow only sequential control of one motion class at a time. Additionally, only recently have researchers demonstrated proportional myoelectric control in such systems, an option that is believed to make fine control easier for the user. Recent developments suggest improved reliability if the user follows a so-called prosthesis guided training (PGT) scheme.MethodsIn this study, a system for simultaneous proportional myoelectric control has been developed for a hand prosthesis with two motor functions (hand open/close, and wrist pro-/supination). The prosthesis has been used with a prosthesis socket equivalent designed for normally-limbed subjects. An extended version of PGT was developed for use with proportional control. The control system’s performance was tested for two subjects in the Clothespin Relocation Task and the Southampton Hand Assessment Procedure (SHAP). Simultaneous proportional control was compared with three other control strategies implemented on the same prosthesis: mutex proportional control (the same system but with simultaneous control disabled), mutex on-off control, and a more traditional, sequential proportional control system with co-contractions for state switching.ResultsThe practical tests indicate that the simultaneous proportional control strategy and the two mutex-based pattern recognition strategies performed equally well, and superiorly to the more traditional sequential strategy according to the chosen outcome measures.ConclusionsThis is the first simultaneous proportional myoelectric control system demonstrated on a prosthesis affixed to the forearm of a subject. The study illustrates that PGT is a promising system training method for proportional control. Due to the limited number of subjects in this study, no definite conclusions can be drawn.

Electromyogram (EMG) signals or myoelectric signals (MESs) have two prominent areas in the field of biomedical instrumentation. EMG signals are primarily used to analyse the neuromuscular diseases such as myopathy and neuropathy. In addition, the EMG signal can be utilised in myoelectric control systems - where the external devices like upper limb prostheses, intelligent wheelchairs, and assistive robots can be controlled by acquiring surface EMG signals. The aim of present work is to obtain classification accuracy first by using linear discriminant analysis (LDA) classifier where principal component analysis (PCA) and uncorrelated linear discriminant analysis (ULDA) feature reduction techniques are used for upper limb prostheses control application. Next, the multilayer perceptron (MLP) neural network with back propagation algorithm is used to calculate the classification accuracy for upper limb prostheses control.

Purpose: The DEKA Arm, a pre-commercial upper limb prosthesis, funded by the DARPA Revolutionizing Prosthetics Program, offers increased degrees of freedom while requiring a large number of user control inputs to operate. To address this challenge, DEKA developed prototype foot controls. Although the concept of utilizing foot controls to operate an upper limb prosthesis has been discussed for decades, only small-sized studies have been performed and no commercial product exists. The purpose of this paper is to report amputee user perspectives on using three different iterations of foot controls to operate the DEKA Arm. Method: Qualitative data was collected from 36 subjects as part of the Department of Veterans Affairs (VA) Study to Optimize the DEKA Arm through surveys, interviews, audio memos, and videotaped sessions. Three major, interrelated themes were identified using the constant comparative method: attitudes towards foot controls, psychomotor learning and physical experience of using foot controls. Results: Feedback about foot controls was generally positive for all iterations. The final version of foot controls was viewed most favorably. Conclusions: Our findings indicate that foot controls are a viable control option that can enable control of a multifunction upper limb prosthesis (the DEKA Arm).Implications for RehabilitationMultifunction upper limb prostheses require many user control inputs to operate. Foot controls offer additional control input options for such advanced devices, yet have had minimal study.This study found that foot controls were a viable option for controlling multifunction upper limb prostheses. Most of the 36 subjects in this study were willing to adopt foot controls to control the multiple degrees of freedom of the DEKA Arm.With training and practice, all users were able to develop the psychomotor skills needed to successfully operate food controls. Some had initial difficulty, but acclimated over time.

Objective. Recent studies have shown the possibility of simultaneous and proportional control of electrically powered upper-limb prostheses, but there has been little investigation on optimal channel selection. The objective of this study is to find a robust channel selection method and the channel subsets most suitable for simultaneous and proportional myoelectric prosthesis control of multiple degrees-of-freedom (DoFs). Approach. Ten able-bodied subjects and one person with congenital upper-limb deficiency took part in this study, and performed wrist movements with various combinations of two DoFs (flexion/extension and radial/ulnar deviation). During the experiment, high density electromyographic (EMG) signals and the actual wrist angles were recorded with an 8 × 24 electrode array and a motion tracking system, respectively. The wrist angles were estimated from EMG features with ridge regression using the subsets of channels chosen by three different channel selection methods: (1) least absolute shrinkage and selection operator (LASSO), (2) sequential feature selection (SFS), and (3) uniform selection (UNI). Main results. SFS generally showed higher estimation accuracy than LASSO and UNI, but LASSO always outperformed SFS in terms of robustness, such as noise addition, channel shift and training data reduction. It was also confirmed that about 95% of the original performance obtained using all channels can be retained with only 12 bipolar channels individually selected by LASSO and SFS. Significance. From the analysis results, it can be concluded that LASSO is a promising channel selection method for accurate simultaneous and proportional prosthesis control. We expect that our results will provide a useful guideline to select optimal channel subsets when developing clinical myoelectric prosthesis control systems based on continuous movements with multiple DoFs.

One of the main challenges in upper limb prosthesis control to date is to provide devices intuitive to use and capable to reproduce the natural movements of the arm and hand. One approach to solve this challenge is to use the same control signals for prosthesis control that our nervous system uses to control its muscles. This thesis aims to investigate the possibility of natural, intuitive prosthesis control using neural information obtained with available surface EMG decomposition methods. In order to explore all aspects of such a novel approach, a series of five studies were performed with the final goal of implementing a proof of concept and comparing its performance with state of the art myoelectric control. The performed investigations revealed important insights in motor unit physiology after targeted muscle reinnervation, EMG decomposition in dynamic voluntary contractions of the forearm, and the properties and challenges of neural information based prosthesis control. The main outcome of the thesis is that neural information based prosthesis control is capable to outperform myoelectric approaches in pattern recognition, linear regression and nonlinear regression, as determined by offline performance comparisons. The final proof of concept for this novel approach was a robust regression method based on neuromusculoskeletal modeling. The kinematics estimation of the proposed approach outperformed EMG-based nonlinear regression in both able-bodied subjects and patients with limb deficiency, indicating that using neural information is a promising avenue for advanced myoelectric control.

Feed-forward control paradigms currently domi-nate upper limb prosthesis research. Electromyographic (EMG) signals measured from muscles in a user's residual limb are filtered and processed before becoming the input to a machine learning algorithm. The output of this algorithm is sent directly as a command to a prosthetic. Despite advances in feed-forward methods, upper limb prostheses remain difficult for amputee users to control, and abandonment rates are high [1]. In this paper, we present a novel shared control paradigm that uses a hybrid gaze/EMG interface to control prosthetic hand and wrist movements. Six subjects used a virtual prosthesis to perform a pick-and-place task in an augmented reality (AR) environment using both a semi-autonomous (SA) controller and a feedforward (FF) controller representative of the current state of the art. Results show a 75% increase in average successful task completion rate when using the SA controller instead of the FF controller. The SA controller was found to be more robust against variation in object type and orientation, scored significantly higher on subjective usability metrics (p ≤ 0.05), and resulted in a dramatic decrease in user frustration during the task (p <. 01).

A two degree-of-freedom microprocessor based controller that uses the principle of Extended Physiological Proprio- ception (EPP), was designed for the simultaneous multifunctional control of upper-limb prostheses. In an EPP system, the output is related to the input by a mechanically unbeatable position servomechanism. Use of embedded microprocessor systems in the control of upper-limb prostheses provides a high degree of control algorithm flexibility allowing different control algorithms to be downloaded and executed in the same controller circuit. In addition, control parameters can be easily adjusted and tailored to different user capabilities. In a trans-humeral or shoulder disarticulation prosthesis we envision this controller enabling EPP control of both elbow flexion-extension, and humeral rotation. In a wrist disarticulation or trans-radial prosthesis this controller, in conjunction with other similar controllers, could provide EPP control of individual digits in a multifunctional hand prosthesis.

BackgroundAlthough electromyogram (EMG) pattern recognition (PR) for multifunctional upper limb prosthesis control has been reported for decades, the clinical benefits have rarely been examined. The study purposes were to: 1) compare self-report and performance outcomes of a transradial amputee immediately after training and one week after training of direct myoelectric control and EMG pattern recognition (PR) for a two-degree-of-freedom (DOF) prosthesis, and 2) examine the change in outcomes one week after pattern recognition training and the rate of skill acquisition in two subjects with transradial amputations.MethodsIn this cross-over study, participants were randomized to receive either PR control or direct control (DC) training of a 2 DOF myoelectric prosthesis first. Participants were 2 persons with traumatic transradial (TR) amputations who were 1 DOF myoelectric users. Outcomes, including measures of dexterity with and without cognitive load, activity performance, self-reported function, and prosthetic satisfaction were administered immediately and 1 week after training. Speed of skill acquisition was assessed hourly. One subject completed training under both PR control and DC conditions. Both subjects completed PR training and testing. Outcomes of test metrics were analyzed descriptively.ResultsComparison of the two control strategies in one subject who completed training in both conditions showed better scores in 2 (18%) dexterity measures, 1 (50%) dexterity measure with cognitive load, and 1 (50%) self-report functional measure using DC, as compared to PR. Scores of all other metrics were comparable. Both subjects showed decline in dexterity after training. Findings related to rate of skill acquisition varied considerably by subject.ConclusionsOutcomes of PR and DC for operating a 2-DOF prosthesis in a single subject cross-over study were similar for 74% of metrics, and favored DC in 26% of metrics. The two subjects who completed PR training showed decline in dexterity one week after training ended. Findings related to rate of skill acquisition varied considerably by subject. This study, despite its small sample size, highlights a need for additional research quantifying the functional and clinical benefits of PR control for upper limb prostheses.

Electromyography-based gesture classification methods for control of advanced upper limb prostheses are limited either to individuals with amputations distal to the elbow or to those willing to undergo targeted muscle reinnervation surgery. Based on the natural similarity between gestures of the lower leg and the arm and on established methods in electromyography-based gesture classification, we propose a noninvasive system with which users control an upper limb prosthesis via homologous movements of the leg and foot. Eight inexperienced able-bodied subjects controlled a simulated robotic arm in a target achievement control (TAC) task with command of up to four degrees of freedom toward targets requiring one motion class. All subjects performed the task with analogous electromyography recording configurations on both the leg and the arm (as a benchmark), achieving slightly better performance with leg control overall. Only a brief demonstration of the arm-leg gesture mapping was necessary for subjects to perform the task, establishing the minimal training time required to begin using the control scheme. Our findings indicate that electromyography-based recognition of leg gestures may be a viable noninvasive prosthesis control option for high-level amputees.

Although significant technological advances have been made in the last forty years, natural and effortless control of upper limb prostheses is still an open issue. Commercially available myoelectric prostheses present limited Degrees of Freedom (DoF) mainly because of the lack of available and reliable independent control signals from the human body. Thus, despite the crucial role that an actuated wrist could play in a transradial prosthesis in terms of avoiding compensatory movements, commercial hand prostheses present only manually adjustable passive wrists or actuated rotators controlled by (unnatural) sequential control strategies. In the present study we investigated the synergies between the humeral orientation with respect to the trunk and the forearm pronation/supination angles during the execution of a wide range of activities of daily living, in healthy subjects. Our results showed consistent postural synergies between the two selected body segments for almost the totality of the activities of daily living under investigation. This is a promising result because these postural synergies could be exploited to automatically control the wrist rotator unit in transradial prostheses improving the fluency and the dexterity of the amputee.

Upper limb prostheses are commonly propelled by pneumatic artificial muscles organized in an antagonistic arrangement. Nonetheless, the control of upper limb prostheses under changing/unknown situations is difficult and necessary for a variety of real-world applications. Adaptive control, learning-based control, and robust control have been studied to deal with such challenges. However, their adaptability is insufficient for prostheses used in daily life, which are exposed to variable task levels, user motor characteristics, and prosthetic features. This paper introduces a highly adaptive controller for the first time based on Generative Adversarial Nets and proportional–integral–derivative controller (G-PID controller). G-PID controller comprises a generator for generating compensation actions to enhance PID responsiveness when controlling the unknown/changing system. Moreover, it incorporates a discriminator that receives responses from both a user-preselected reference system and the compensated changing/unknown system, and simultaneously determines the source of these responses. Through continuous updates, the compensator modifies the response of unknown/changing system to align with the reference system, thereby facilitating adaptive control. The G-PID controller’s effectiveness is evaluated through 1-degree of freedom (DoF) joint and 2-DoF shoulder prostheses in simulation experiments, and further validated in prototype experiments focusing on online learning for unknown and time-varying payload. The results demonstrate its ability to deal with diverse types of unknowns/changes, marking a significant advancement towards incorporating prostheses seamlessly into daily life.

Myoelectric pattern recognition (MPR) based strategies have been well investigated and applied for the control of upper limb prostheses. Despite their wide adoption, EMG-PR based upper limb prosthesis is not yet available in the clinics and commercial stores for above-elbow amputees. On the one hand, above-elbow amputees do not have enough residual muscles to generate a rich set of signals needed to effectively control the device. On the other hand, the limited acquire signal from these category of amputee is often contaminated by several kinds of noises that make it difficult to decode their movement intent which serves as a control input. Hence, there is a need to improve the quality of signals generated by such amputees through an efficient approach, to enhance the decoding outcomes of their limb motions. Therefore, this study systematically investigated the capability of a linear (Wiener filtering (WF)) and non-linear (1 Dimensional median filtering: 1 D-Median) techniques in denoising EMG signals obtained from above-elbow amputees towards improving its overall quality. The performance of both filtering techniques was examined using high-density surface electromyogram (HD-sEMG) recordings obtained from four transhumeral amputees who performed five distinct classes of targeted limb motions across three time-domain features while a linear discriminant analysis (LDA) classifier was adopted. Experimental results showed that WF technique could better denoise the signals by achieving an increment of up to 5.0% across limb motions in decoding accuracy compared to the 1D-Median filtering technique. This result suggests that WF can help to improve the overall performance of the feature extraction, and then this may be one reason why it has found application across the domain.

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.