Real-time and offline performance of pattern recognition myoelectric control using a generic electrode grid with targeted muscle reinnervation patients

The Role of Fascial Tissue Layer in Electric Signal Transmission from the Forearm Musculature to the Cutaneous Layer as a Possibility for Increased Signal Strength in Myoelectric Forearm Exoprosthesis Development

Myoelectric exoprostheses serve to aid in the everyday activities of patients with forearm or hand amputations. While electrical signals are known key factors controlling exoprosthesis, little is known about how we can improve their transmission strength from the forearm muscles as to obtain better sEMG. The purpose of this study is to evaluate the role of the forearm fascial layer in transmitting myoelectrical current. We examined the sEMG signals in three individual muscles, each from six healthy forearms (Group 1) and six amputation stumps (Group 2), along with their complete biometric characteristics. Following the tests, one patient underwent a circumferential osteoneuromuscular stump revision surgery (CONM) that also involved partial removal of fascia and subcutaneous fat in the amputation stump, with re-testing after complete healing. In group 1, we obtained a stronger sEMG signal than in Group 2. In the CONM case, after surgery, the patient’s data suggest that the removal of fascia, alongside the fibrotic and subcutaneous fat tissue, generates a stronger sEMG signal. Therefore, a reduction in the fascial layer, especially if accompanied by a reduction of the subcutaneous fat layer may prove significant for improving the strength of sEMG signals used in the control of modern exoprosthetics.

Surface EMG Statistical and Performance Analysis of Targeted-Muscle-Reinnervated (TMR) Transhumeral Prosthesis Users in Home and Laboratory Settings

A pattern-recognition (PR)-based myoelectric control system is the trend of future prostheses development. Compared with conventional prosthetic control systems, PR-based control systems provide high dexterity, with many studies achieving >95% accuracy in the last two decades. However, most research studies have been conducted in the laboratory. There is limited research investigating how EMG signals are acquired when users operate PR-based systems in their home and community environments. This study compares the statistical properties of surface electromyography (sEMG) signals used to calibrate prostheses and quantifies the quality of calibration sEMG data through separability indices, repeatability indices, and correlation coefficients in home and laboratory settings. The results demonstrate no significant differences in classification performance between home and laboratory environments in within-calibration classification error (home: 6.33 ± 2.13%, laboratory: 7.57 ± 3.44%). However, between-calibration classification errors (home: 40.61 ± 9.19%, laboratory: 44.98 ± 12.15%) were statistically different. Furthermore, the difference in all statistical properties of sEMG signals is significant (p < 0.05). Separability indices reveal that motion classes are more diverse in the home setting. In summary, differences in sEMG signals generated between home and laboratory only affect between-calibration performance.

Review on electromyography based intention for upper limb control using pattern recognition for human-machine interaction

Upper limb myoelectric prosthetic control is an essential topic in the field of rehabilitation. The technique controls prostheses using surface electromyogram (sEMG) and intramuscular EMG (iEMG) signals. EMG signals are extensively used in controlling prosthetic upper and lower limbs, virtual reality entertainment, and human-machine interface (HMI). EMG signals are vital parameters for machine learning and deep learning algorithms and help to give an insight into the human brain’s function and mechanisms. Pattern recognition techniques pertaining to support vector machine (SVM), k-nearest neighbor (KNN) and Bayesian classifiers have been utilized to classify EMG signals. This paper presents a review on current EMG signal techniques, including electrode array utilization, signal acquisition, signal preprocessing and post-processing, feature selection and extraction, data dimensionality reduction, classification, and ultimate application to the community. The paper also discusses using alternatives to EMG signals, such as force sensors, to measure muscle activity with reliable results. Future implications for EMG classification include employing deep learning techniques such as artificial neural networks (ANN) and recurrent neural networks (RNN) for achieving robust results.

A parallel classification strategy to simultaneous control elbow, wrist, and hand movements

Background

In the field of myoelectric control systems, pattern recognition (PR) algorithms have become always more interesting for predicting complex electromyography patterns involving movements with more than 2 Degrees of Freedom (DoFs). The majority of classification strategies, used for the prosthetic control, are based on single, hierarchical and parallel linear discriminant analysis (LDA) classifiers able to discriminate up to 19 wrist/hand gestures (in the 3-DoFs case), considering both combined and discrete motions. However, these strategies were introduced to simultaneously classify only 2 DoFs and their use is limited by the lack of online performance measures. This study introduces a novel classification strategy based on the Logistic Regression (LR) algorithm with regularization parameter to provide simultaneous classification of 3 DoFs motion classes.

Methods

The parallel PR-based strategy was tested on 15 healthy subjects, by using only six surface EMG sensors. Twenty-seven discrete and complex elbow, hand and wrist motions were classified by keeping the number of electromyographic (EMG) electrodes to a bare minimum and the classification error rate under 10 %. To this purpose, the parallel classification strategy was implemented by using three classifiers one for each DoF: the “Elbow classifier”, the “Wrist classifier”, and the “Hand classifier” provided the simultaneous control of the elbow, hand, and wrist joints, respectively.

Results

Both the offline and real-time performance metrics were evaluated and compared with the LDA parallel classification results. The real-time recognition results were statistically better with the LR classifier with respect to the LDA classifier, for all motion classes (elbow, hand and wrist).

Conclusions

In this paper, a novel parallel PR-based strategy was proposed for classifying up to 3 DoFs: three joint classifiers were employed simultaneously for classifying 27 motion classes related to the elbow, wrist, and hand and promising results were obtained.

Control Strategies and Performance Assessment of Upper-Limb TMR Prostheses: A Review

The evolution of technological and surgical techniques has made it possible to obtain an even more intuitive control of multiple joints using advanced prosthetic systems. Targeted Muscle Reinnervation (TMR) is considered to be an innovative and relevant surgical technique for improving the prosthetic control for people with different amputation levels of the limb. Indeed, TMR surgery makes it possible to obtain reinnervated areas that act as biological amplifiers of the motor control. On the technological side, a great deal of research has been conducted in order to evaluate various types of myoelectric prosthetic control strategies, whether direct control or pattern recognition-based control. In the literature, different control performance metrics, which have been evaluated on TMR subjects, have been introduced, but no accepted reference standard defines the better strategy for evaluating the prosthetic control. Indeed, the presence of several evaluation tests that are based on different metrics makes it difficult the definition of standard guidelines for comprehending the potentiality of the proposed control systems. Additionally, there is a lack of evidence about the comparison of different evaluation approaches or the presence of guidelines on the most suitable test to proceed for a TMR patients case study. Thus, this review aims at identifying these limitations by examining the several studies in the literature on TMR subjects, with different amputation levels, and proposing a standard method for evaluating the control performance metrics.

Current rates of prosthetic usage in upper-limb amputees - have innovations had an impact on device acceptance?

PURPOSE

There is a large body of evidence demonstrating high rates of prosthesis abandonment in the upper extremity. However, these surveys were conducted years ago, thus the influence of recent refinements in prosthetic technology on acceptance is unknown. This study aims to gather current data on prosthetic usage, to assess the effects of these advancements.

MATERIALS AND METHODS

A questionnaire was sent to 68 traumatic upper limb amputees treated within the Austrian Trauma Insurance Agency between the years 1996 and 2016. Responses were grouped by the year of amputation to assess the effect of time.

RESULTS

The rejection rate at all levels of amputation was 44%. There was no significant difference in acceptance between responders amputated before or after 2006 (p = 0.939). Among users, 92.86% (n = 13) used a myoelectric, while only one amputee (7.14%, n = 1) used a body-powered device. Most responders complained about the comfort (60.87%, n = 14) as well as the weight of the device (52.17%, n = 12).

CONCLUSIONS

The advancements of the last decade in the arena of upper limb prosthetics have not yet achieved a significant change in prosthetic abandonment within this study cohort. Although academic solutions have been presented to tackle patient's complaints, clinical reality still shows high rejection rates of cost-intensive prosthetic devices.Implications for rehabilitationAbandonment rates in prosthetic rehabilitation after upper limb amputation have shown to be 50% and higher.The advancements of the last decade in the arena of upper limb prosthetics have not yet achieved a significant change in prosthetic abandonment.Well-structured and patient-tailored prosthetic training as well as ensuring the amputee's active participation in the decision making process will most likely improve prosthetic acceptance.

Real-Time EMG Based Pattern Recognition Control for Hand Prostheses: A Review on Existing Methods, Challenges and Future Implementation

Upper limb amputation is a condition that significantly restricts the amputees from performing their daily activities. The myoelectric prosthesis, using signals from residual stump muscles, is aimed at restoring the function of such lost limbs seamlessly. Unfortunately, the acquisition and use of such myosignals are cumbersome and complicated. Furthermore, once acquired, it usually requires heavy computational power to turn it into a user control signal. Its transition to a practical prosthesis solution is still being challenged by various factors particularly those related to the fact that each amputee has different mobility, muscle contraction forces, limb positional variations and electrode placements. Thus, a solution that can adapt or otherwise tailor itself to each individual is required for maximum utility across amputees. Modified machine learning schemes for pattern recognition have the potential to significantly reduce the factors (movement of users and contraction of the muscle) affecting the traditional electromyography (EMG)-pattern recognition methods. Although recent developments of intelligent pattern recognition techniques could discriminate multiple degrees of freedom with high-level accuracy, their efficiency level was less accessible and revealed in real-world (amputee) applications. This review paper examined the suitability of upper limb prosthesis (ULP) inventions in the healthcare sector from their technical control perspective. More focus was given to the review of real-world applications and the use of pattern recognition control on amputees. We first reviewed the overall structure of pattern recognition schemes for myo-control prosthetic systems and then discussed their real-time use on amputee upper limbs. Finally, we concluded the paper with a discussion of the existing challenges and future research recommendations.

Online adaptive neural control of a robotic lower limb prosthesis

OBJECTIVE

The purpose of this study was to develop and evaluate an adaptive intent recognition algorithm that continuously learns to incorporate a lower limb amputee's neural information (acquired via electromyography (EMG)) as they ambulate with a robotic leg prosthesis.

APPROACH

We present a powered lower limb prosthesis that was configured to acquire the user's neural information and kinetic/kinematic information from embedded mechanical sensors, and identify and respond to the user's intent. We conducted an experiment with eight transfemoral amputees over multiple days. EMG and mechanical sensor data were collected while subjects using a powered knee/ankle prosthesis completed various ambulation activities such as walking on level ground, stairs, and ramps. Our adaptive intent recognition algorithm automatically transitioned the prosthesis into the different locomotion modes and continuously updated the user's model of neural data during ambulation.

MAIN RESULTS

Our proposed algorithm accurately and consistently identified the user's intent over multiple days, despite changing neural signals. The algorithm incorporated 96.31% [0.91%] (mean, [standard error]) of neural information across multiple experimental sessions, and outperformed non-adaptive versions of our algorithm-with a 6.66% [3.16%] relative decrease in error rate.

SIGNIFICANCE

This study demonstrates that our adaptive intent recognition algorithm enables incorporation of neural information over long periods of use, allowing assistive robotic devices to accurately respond to the user's intent with low error rates.

Long-term implant of intramuscular sensors and nerve transfers for wireless control of robotic arms in above-elbow amputees

Targeted muscle reinnervation (TMR) amplifies the electrical activity of nerves at the stump of amputees by redirecting them in remnant muscles above the amputation. The electrical activity of the reinnervated muscles can be used to extract natural control signals. Nonetheless, current control systems, mainly based on noninvasive muscle recordings, fail to provide accurate and reliable control over time. This is one of the major reasons for prosthetic abandonment. This prospective interventional study includes three unilateral above-elbow amputees and reports the long-term (2.5 years) implant of wireless myoelectric sensors in the reinnervation sites after TMR and their use for control of robotic arms in daily life. It therefore demonstrates the clinical viability of chronically implanted myoelectric interfaces that amplify nerve activity through TMR. The patients showed substantial functional improvements using the implanted system compared with control based on surface electrodes. The combination of TMR and chronically implanted sensors may drastically improve robotic limb replacement in above-elbow amputees.

Upper Limb Prosthesis Control for High-Level Amputees via Myoelectric Recognition of Leg Gestures

Recognition of motion intent via surface electromyography (EMG) has become increasingly practical for prosthesis control, but lacking residual muscle sites remains a major obstacle to its use by high-level amputees. Currently, there are few approaches to upper limb prosthesis control for individuals with amputations proximal to the elbow, all of which suffer from one or more of three primary problems: invasiveness, the need for intensive training, and lacking functionality. Using surface EMG sensors placed on the lower leg and a natural mapping between degrees of freedom of the leg and the arm, we tested a noninvasive control approach by which high-level amputees could control prosthetic elbow, wrist, and hand movements with minimal training. In this paper, we used able-bodied subjects to facilitate a direct comparison between control using intact arm and leg muscles. First, we found that foot gestures could be classified offline using time domain features and linear discriminant analysis with accuracy comparable to an equivalent system for recognizing arm movements. Second, we used the target achievement control test to evaluate real-time control performance in three and four degrees of freedom. After approximately 20 min of training, subjects tended to perform the task as well with the leg as with intact arm muscles, and performance overall was comparable to other control methods.

A Modular, Smart, and Wearable System for High Density sEMG Detection

OBJECTIVE

The use of linear or bi-dimensional electrode arrays for surface EMG detection (HD-sEMG) is gaining attention as it increases the amount and reliability of information extracted from the surface EMG. However, the complexity of the setup and the encumbrance of HD-sEMG hardware currently limits its use in dynamic conditions. The aim of this paper was to develop a miniaturized, wireless, and modular HD-sEMG acquisition system for applications requiring high portability and robustness to movement artifacts.

METHODS

A system with modular architecture was designed. Its core is a miniaturized 32-channel amplifier (Sensor Unit - SU) sampling at 2048 sps/ch with 16 bit resolution and wirelessly transmitting data to a PC or a mobile device. Each SU is a node of a Body Sensor Network for the synchronous signal acquisition from different muscles.

RESULTS

A prototype with two SUs was developed and tested. Each SU is small (3.4 cm × 3 cm × 1.5 cm), light (16.7 g), and can be connected directly to the electrodes; thus, avoiding the need for customary, wired setup. It allows to detect HD-sEMG signals with an average noise of 1.8 μV_RMS and high performance in terms of rejection of power-line interference and motion artefacts. Tests performed on two SUs showed no data loss in a 22 m range and a ±500 μs maximum synchronization delay.

CONCLUSIONS

Data collected in a wide spectrum of experimental conditions confirmed the functionality of the designed architecture and the quality of the acquired signals.

SIGNIFICANCE

By simplifying the experimental setup, reducing the hardware encumbrance, and improving signal quality during dynamic contractions, the developed system opens new perspectives in the use of HD-sEMG in applied and clinical settings.

Phantom-Mobility-Based Prosthesis Control in Transhumeral Amputees Without Surgical Reinnervation: A Preliminary Study

Transhumeral amputees face substantial difficulties in efficiently controlling their prosthetic limb, leading to a high rate of rejection of these devices. Actual myoelectric control approaches make their use slow, sequential and unnatural, especially for these patients with a high level of amputation who need a prosthesis with numerous active degrees of freedom (powered elbow, wrist, and hand). While surgical muscle-reinnervation is becoming a generic solution for amputees to increase their control capabilities over a prosthesis, research is still being conducted on the possibility of using the surface myoelectric patterns specifically associated to voluntary Phantom Limb Mobilization (PLM), appearing naturally in most upper-limb amputees without requiring specific surgery. The objective of this study was to evaluate the possibility for transhumeral amputees to use a PLM-based control approach to perform more realistic functional grasping tasks. Two transhumeral amputated participants were asked to repetitively grasp one out of three different objects with an unworn eight-active-DoF prosthetic arm and release it in a dedicated drawer. The prosthesis control was based on phantom limb mobilization and myoelectric pattern recognition techniques, using only two repetitions of each PLM to train the classification architecture. The results show that the task could be successfully achieved with rather optimal strategies and joint trajectories, even if the completion time was increased in comparison with the performances obtained by a control group using a simple GUI control, and the control strategies required numerous corrections. While numerous limitations related to robustness of pattern recognition techniques and to the perturbations generated by actual wearing of the prosthesis remain to be solved, these preliminary results encourage further exploration and deeper understanding of the phenomenon of natural residual myoelectric activity related to PLM, since it could possibly be a viable option in some transhumeral amputees to extend their control abilities of functional upper limb prosthetics with multiple active joints without undergoing muscular reinnervation surgery.

User experience of controlling the DEKA Arm with EMG pattern recognition

Introduction

A commercially available EMG Pattern Recognition (EMG-PR) control system was adapted to interface with the multi-degree of freedom (DOF) DEKA Arm.

Purpose

To describe users’ experience of controlling the DEKA Arm using EMG-PR.

Methods

Sample: Twelve persons with upper limb amputation participated, 10 with transradial (TR), 2 with transhumeral (TH) level amputation. Ten were male, and 11 were users of a prosthesis at baselines. Design: This was a two-part study consisting of in-laboratory prosthetic training (Part A) and up to 12 weeks of home use of the prosthesis (Part B). Data collection: Qualitative data were collected through open-ended survey questions and semi-structured interviews. Data Analysis: The study used a qualitative case series design with a constant comparative approach to identify common categories of experience. Coding categories were iteratively refined until saturation of categories was achieved. The data were organized in a primary category, major categories of experience, factors impacting experience, and broader contextual factors.

Results

Users had mixed perspectives on the desirability of the EMG-PR control system in combination with the DEKA Arm. Major aspects of user experience related to the system complexity, process of calibrating, and functional benefits. Factors influencing user experience included training and acclimation, fatigue, prosthesis design, technical issues and control changes. Broader contextual factors, both personal and environmental, also impacted users’ experience.

Discussion/Conclusion

This study provided an in-depth description of user experience operating the DEKA Arm using EMG-PR control. The majority of participants expressed a preference for the controls of their personal prosthesis and controls rather than the iteration of EMG-PR controlled DEKA Arm used in this study. Most were positive about the future potential of EMG-PR as a control method. An understanding of patient experience will assist clinicians and patients choosing prosthetic options.

Control within a virtual environment is correlated to functional outcomes when using a physical prosthesis

Background

Advances such as targeted muscle reinnervation and pattern recognition control may provide improved control of upper limb myoelectric prostheses, but evaluating user function remains challenging. Virtual environments are cost-effective and immersive tools that are increasingly used to provide practice and evaluate prosthesis control, but the relationship between virtual and physical outcomes—i.e., whether practice in a virtual environment translates to improved physical performance—is not understood.

Methods

Nine people with transhumeral amputations who previously had targeted muscle reinnervation surgery were fitted with a myoelectric prosthesis comprising a commercially available elbow, wrist, terminal device, and pattern recognition control system. Virtual and physical outcome measures were obtained before and after a 6-week home trial of the prosthesis.

Results

After the home trial, subjects showed statistically significant improvements (p < 0.05) in offline classification error, the virtual Target Achievement Control test, and the physical Southampton Hand Assessment Procedure and Box and Blocks Test. A trend toward improvement was also observed in the physical Clothespin Relocation task and Jebsen-Taylor test; however, these changes were not statistically significant. The median completion time in the virtual test correlated strongly and significantly with the Southampton Hand Assessment Procedure (p = 0.05, R = − 0.86), Box and Blocks Test (p = 0.007, R = − 0.82), Jebsen-Taylor Test (p = 0.003, R = 0.87), and the Assessment of Capacity for Myoelectric Control (p = 0.005,R = − 0.85). The classification error performance only had a significant correlation with the Clothespin Relocation Test (p = 0.018, R = .76).

Conclusions

In-home practice with a pattern recognition-controlled prosthesis improves functional control, as measured by both virtual and physical outcome measures. However, virtual measures need to be validated and standardized to ensure reliability in a clinical or research setting.

Trial registration

This is a registered clinical trial: NCT03097978.

Multiday Evaluation of Techniques for EMG-Based Classification of Hand Motions

Currently, most of the adopted myoelectric schemes for upper limb prostheses do not provide users with intuitive control. Higher accuracies have been reported using different classification algorithms but investigation on the reliability over time for these methods is very limited. In this study, we compared for the first time the longitudinal performance of selected state-of-the-art techniques for electromyography (EMG) based classification of hand motions. Experiments were conducted on ten able-bodied and six transradial amputees for seven continuous days. Linear discriminant analysis (LDA), artificial neural network (ANN), support vector machine (SVM), K-nearest neighbor (KNN), and decision trees (TREE) were compared. Comparative analysis showed that the ANN attained highest classification accuracy followed by LDA. Three-way repeated ANOVA test showed a significant difference (P < 0.001) between EMG types (surface, intramuscular, and combined), days (1-7), classifiers, and their interactions. Performance on the last day was significantly better (P < 0.05) than the first day for all classifiers and EMG types. Within-day, classification error (WCE) across all subject and days in ANN was: surface (9.12 ± 7.38%), intramuscular (11.86 ± 7.84%), and combined (6.11 ± 7.46%). The between-day analysis in a leave-one-day-out fashion showed that the ANN was the optimal classifier (surface (21.88 ± 4.14%), intramuscular (29.33 ± 2.58%), and combined (14.37 ± 3.10%). Results indicate that within day performances of classifiers may be similar but over time, it may lead to a substantially different outcome. Furthermore, training ANN on multiple days might allow capturing time-dependent variability in the EMG signals and thus minimizing the necessity for daily system recalibration.

A Prototype of a Neural, Powered, Transtibial Prosthesis for the Cat: Benchtop Characterization

We developed a prototype of a neural, powered, transtibial prosthesis for the use in a feline model of prosthetic gait. The prosthesis was designed for attachment to a percutaneous porous titanium implant integrated with bone, skin, and residual nerves and muscles. In the benchtop testing, the prosthesis was fixed in a testing rig and subjected to rhythmic vertical displacements and interactions with the ground at a cadence corresponding to cat walking. Several prosthesis functions were evaluated. They included sensing ground contact, control of transitions between the finite states of prosthesis loading, and a closed-loop modulation of the linear actuator gain in each loading cycle. The prosthetic design parameters (prosthesis length = 55 mm, mass = 63 g, peak extension moment = 1 Nm) corresponded closely to those of the cat foot-ankle with distal shank and the peak ankle extension moment during level walking. The linear actuator operated the prosthetic ankle joint using inputs emulating myoelectric activity of residual muscles. The linear actuator gain was modulated in each cycle to minimize the difference between the peak of ground reaction forces (GRF) recorded by a ground force sensor and a target force value. The benchtop test results demonstrated a close agreement between the GRF peaks and patterns produced by the prosthesis and by cats during level walking.

Two ways to improve myoelectric control for a transhumeral amputee after targeted muscle reinnervation: a case study

BACKGROUND

Myoelectric control of multifunctional prostheses is challenging for individuals with high-level amputations due to insufficient surface electromyography (sEMG) signals. A surgical technique called targeted muscle reinnervation (TMR) has achieved impressive improvements in myoelectric control by providing more sEMG control signals. In this case, some channels of sEMG signals are coupled after TMR, which limits the performance of conventional amplitude-based control for upper-limb prostheses.

METHODS

In this paper, two different ways (training and algorithms) were attempted to solve the problem in a transhumeral amputee after TMR. Firstly, effect of rehabilitation training on generating independent sEMG signals was investigated. The results indicated that some sEMG signals recorded were still coupled over the targeted muscles after rehabilitation training for about two months. Secondly, pattern recognition (PR) algorithm was then applied to classify the sEMG signals. In the second way, to further improve the real-time performance of prosthetic control, a post-processing method named as mean absolute value-based (MAV-based) threshold switches was utilized.

RESULTS

Using the improved algorithms, substantial improvement was shown in a subset of the modified Action Research Arm Test (ARAT). Compared with common PR control without post-processing method, the total scores increased more than 18% with majority vote and more than 58% with MAV-based threshold switches. The amputee was able to finish all the tasks within the allotted time with the standard MAV-based threshold switches. Subjectively the amputee preferred the PR control with MAV-based threshold switches and reported it to be more accurate and much smoother both in experiment and practical use.

CONCLUSIONS

Although the sEMG signals were still coupled after rehabilitation training on the TMR patient, the online performance of the prosthetic operation was improved through application of PR control with combination of the MAV-based threshold switches.

TRIAL REGISTRATION

Retrospectively registered http://www.chictr.org.cn/showproj.aspx?proj=22058 .

Robust extraction of basis functions for simultaneous and proportional myoelectric control via sparse non-negative matrix factorization

OBJECTIVE

This paper proposes a novel simultaneous and proportional multiple degree of freedom (DOF) myoelectric control method for active prostheses.

APPROACH

The approach is based on non-negative matrix factorization (NMF) of surface EMG signals with the inclusion of sparseness constraints. By applying a sparseness constraint to the control signal matrix, it is possible to extract the basis information from arbitrary movements (quasi-unsupervised approach) for multiple DOFs concurrently.

MAIN RESULTS

In online testing based on target hitting, able-bodied subjects reached a greater throughput (TP) when using sparse NMF (SNMF) than with classic NMF or with linear regression (LR). Accordingly, the completion time (CT) was shorter for SNMF than NMF or LR. The same observations were made in two patients with unilateral limb deficiencies.

SIGNIFICANCE

The addition of sparseness constraints to NMF allows for a quasi-unsupervised approach to myoelectric control with superior results with respect to previous methods for the simultaneous and proportional control of multi-DOF. The proposed factorization algorithm allows robust simultaneous and proportional control, is superior to previous supervised algorithms, and, because of minimal supervision, paves the way to online adaptation in myoelectric control.

Myoelectric Pattern Recognition Outperforms Direct Control for Transhumeral Amputees with Targeted Muscle Reinnervation: A Randomized Clinical Trial

Recently commercialized powered prosthetic arm systems hold great potential in restoring function for people with upper-limb loss. However, effective use of such devices remains limited by conventional (direct) control methods, which rely on electromyographic signals produced from a limited set of muscles. Targeted Muscle Reinnervation (TMR) is a nerve transfer procedure that creates additional recording sites for myoelectric prosthesis control. The effects of TMR may be enhanced when paired with pattern recognition technology. We sought to compare pattern recognition and direct control in eight transhumeral amputees who had TMR in a balanced randomized cross-over study. Subjects performed a 6–8 week home trial using direct and pattern recognition control with a custom prostheses made from commercially available parts. Subjects showed statistically better performance in the Southampton Hand Assessment Procedure (p = 0.04) and the Clothespin relocation task (p = 0.02). Notably, these tests required movements along 3 degrees of freedom. Seven of 8 subjects preferred pattern recognition control over direct control. This study was the first home trial large enough to establish clinical and statistical significance in comparing pattern recognition with direct control. Results demonstrate that pattern recognition is a viable option and has functional advantages over direct control.

Nonlinear mappings between discrete and simultaneous motions to decrease training burden of simultaneous pattern recognition myoelectric control

Real-time simultaneous pattern recognition (PR) control of multiple degrees of freedom (DOF) has been demonstrated using a set of parallel linear discriminant analysis (LDA) classifiers trained with both discrete (1-DOF) and simultaneous (2-DOF) motion data. However, this training method presents a clinical challenge, requiring large amounts of data necessary to re-train the system. This study presents a parallel classifier training method that aims to reduce the training burden. Artificial neural networks (ANNs) were used to determine a nonlinear mapping between surface EMG features of 2-DOF motions and their 1-DOF motion components. The mapping was then used to transform experimentally collected features of 1-DOF motions into simulated features of 2-DOF motions. A set of parallel LDA classifiers were trained using the novel training method and two previously reported training methods. The training methods evaluated were (1) using experimentally collected 1-DOF data and ANN-simulated 2-DOF data, (2) using only experimentally collected 1-DOF data and (3) using experimentally collected 1- and 2-DOF data. Using the novel training method resulted in significantly lower classification error overall (p<;0.01) and in predicting 2-DOF motions (p<;0.01) compared to training with experimental 1-DOF data only. These findings demonstrate that using a set of ANNs to predict 2-DOF data from 1-DOF data can improve system performance when only discrete training data are available, thus reducing the training burden of simultaneous PR control.

Phantom movements from physiologically inappropriate muscles: A case study with a high transhumeral amputee

Individuals with high-level amputation have a great need for functional prostheses because of their vast functional deficits. Conventional techniques are considered inappropriate for high-level amputees due to the lack of physiologically appropriate muscles. This study investigates how accurate phantom movements (PMs) can be classified from physiologically inappropriate muscles. The study involves a case study of a 42-year-old transhumeral amputee. Suitable PMs and best electrode configuration were identified using the sequential forward selection method and brute-force technique. Using linear discriminant analysis, the best PMs (elbow extension/flexion, wrist supination/pronation) and rest were classified with error ranging from 3% to 0.18% when using 3 to 8 EMG channels respectively. A completion rate of 93 % was obtained during a targeted achievement control test in a virtual reality environment. This case indicates that a proximal transhumeral amputee can generate muscle activation patterns related to distinct PMs; and these PMs can be decoded from physiologically inappropriate muscles.

Targeted muscle reinnervation and advanced prosthetic arms

Targeted muscle reinnervation (TMR) is a surgical procedure used to improve the control of upper limb prostheses. Residual nerves from the amputated limb are transferred to reinnervate new muscle targets that have otherwise lost their function. These reinnervated muscles then serve as biological amplifiers of the amputated nerve motor signals, allowing for more intuitive control of advanced prosthetic arms. Here the authors provide a review of surgical techniques for TMR in patients with either transhumeral or shoulder disarticulation amputations. They also discuss how TMR may act synergistically with recent advances in prosthetic arm technologies to improve prosthesis controllability. Discussion of TMR and prosthesis control is presented in the context of a 41-year-old man with a left-side shoulder disarticulation and a right-side transhumeral amputation. This patient underwent bilateral TMR surgery and was fit with advanced pattern-recognition myoelectric prostheses.

Non-invasive control interfaces for intention detection in active movement-assistive devices

Active movement-assistive devices aim to increase the quality of life for patients with neuromusculoskeletal disorders. This technology requires interaction between the user and the device through a control interface that detects the user’s movement intention. Researchers have explored a wide variety of invasive and non-invasive control interfaces. To summarize the wide spectrum of strategies, this paper presents a comprehensive review focused on non-invasive control interfaces used to operate active movement-assistive devices. A novel systematic classification method is proposed to categorize the control interfaces based on: (I) the source of the physiological signal, (II) the physiological phenomena responsible for generating the signal, and (III) the sensors used to measure the physiological signal. The proposed classification method can successfully categorize all the existing control interfaces providing a comprehensive overview of the state of the art. Each sensing modality is briefly described in the body of the paper following the same structure used in the classification method. Furthermore, we discuss several design considerations, challenges, and future directions of non-invasive control interfaces for active movement-assistive devices.