Myoelectric Prostheses Research Articles

A machine learning‐assisted multifunctional tactile sensor for smart prosthetics

AbstractThe absence of tactile perception limits the dexterity of a prosthetic hand and its acceptance by amputees. Recreating the sensing properties of the skin using a flexible tactile sensor could have profound implications for prosthetics, whereas existing tactile sensors often have limited functionality with cross‐interference. In this study, we propose a machine‐learning‐assisted multifunctional tactile sensor for smart prosthetics, providing a human‐like tactile sensing approach for amputations. This flexible sensor is based on a poly(3,4‐ethylenedioxythiophene): poly(styrene sulfonate) (PEDOT:PSS)–melamine sponge, which enables the detection of force and temperature with low cross‐coupling owing to two separate sensing mechanisms: the open‐circuit voltage of the sensor as a force‐insensitive intrinsic variable to measure the absolute temperature and the resistance as a temperature‐insensitive extrinsic variable to measure force. Furthermore, by analyzing the unsteady heat conduction and characterizing it using real‐time thermal imaging, we demonstrated that the process of open‐circuit voltage variation resulting from the unsteady heat conduction is closely correlated with the heat‐conducting capabilities of materials, which can be utilized to discriminate between substances. Assisted by the decision tree algorithm, the device is endowed with thermal conductivity sensing ability, which allows it to identify 10 types of substances with an accuracy of 94.7%. Furthermore, an individual wearing an advanced myoelectric prosthesis equipped with the above sensor can sense pressure, temperature, and recognize different materials. We demonstrated that our multifunctional tactile sensor provides a new strategy to help amputees feel force, temperature and identify the material of objects without the aid of vision. image

Chipless RFID Sensors for Bioimplants: A Comprehensive Review

RFID technology has boomed in the area of biomedical applications, particularly in wearable sensors and bioimplants. The placement of the tag externally or internally generates numerous challenges, such as the design procedure, fabrication method, and real-time testing, along with unique challenges specific to diagnosis and other on-body applications. This article presents a review of bioimplants for several on-body applications associated with RFID technologies and their impact on human tissue. Several fabrication methods are also discussed, with a focus on understanding flexible, conformal, and strong RFID devices. In light of the future of upcoming applications, this article also reviews transformative RFID-based solutions for wireless body area network and implantable uses. In addition, the review highlights the design and implant of prototype devices. In sum, the main focus of this article is all aspects of chipless RFID utilized in multiple places in the human body, beginning with limb sensing and tracking, which basically involves a prosthetic structural tag (PST) responsible for providing information about a prosthetic limb in space. Similarly, the ultrahigh-frequency (UHF) passive RFID implant gives information about the tag, which is placed in the limb, and the communication between the readers takes place without any physical contact. An alternative touch probe method that resonates at about 13.56 MHz improves the accuracy of the implant. Stent tags are discussed for cardiovascular systems. Other specific topics covered are body implants based on an organically modified ceramic technology (ORMOCER) substrate, myoelectric prostheses, fractal-based RFID implants for crack sensing, and the thermal effects of implants.

Nonlinear Mapping From EMG to Prosthesis Closing Velocity Improves Force Control With EMG Biofeedback.

When using EMG biofeedback to control the grasping force of a myoelectric prosthesis, subjects need to activate their muscles and maintain the myoelectric signal within an appropriate interval. However, their performance decreases for higher forces, because the myoelectric signal is more variable for stronger contractions. Therefore, the present study proposes to implement EMG biofeedback using nonlinear mapping, in which EMG intervals of increasing size are mapped to equal-sized intervals of the prosthesis velocity. To validate this approach, 20 non-disabled subjects performed force-matching tasks using Michelangelo prosthesis with and without EMG biofeedback with linear and nonlinear mapping. Additionally, four transradial amputees performed a functional task in the same feedback and mapping conditions. The success rate in producing desired force was significantly higher with feedback (65.4±15.9%) compared to no feedback (46.2±14.9%) as well as when using nonlinear (62.4±16.8%) versus linear mapping (49.2±17.2%). Overall, in non-disabled subjects, the highest success rate was obtained when EMG biofeedback was combined with nonlinear mapping (72%), and the opposite for linear mapping with no feedback (39.6%). The same trend was registered also in four amputee subjects. Therefore, EMG biofeedback improved prosthesis force control, especially when combined with nonlinear mapping, which showed to be an effective approach to counteract increasing variability of myoelectric signal for stronger contractions.

An efficient attention-driven deep neural network approach for continuous estimation of knee joint kinematics via sEMG signals during running

The smooth interaction and coordination between lower-limb amputees and their prosthetics is crucial when performing complex tasks such as running. To address this, simultaneous and proportional control (SPC) based on continuous estimation of joint angles through surface electromyogram (sEMG) signals offers a more seamless interaction between users and their prosthetic limbs than conventional pattern recognition-based control, which relies on recognizing pre-defined movement classes from specific sEMG patterns using classification algorithms. This study proposes a deep learning-based model (AM-BiLSTM) that integrates the attention mechanism (AM) and bidirectional long short-term memory (BiLSTM) network to provide an accurate and robust SPC for estimating knee joint angle during running from a minimal number of sEMG sensors. The sEMG signals of four muscles recorded from 14 subjects during treadmill running at various speeds were utilized by the AM-BiLSTM model to decode knee joint kinematics. To comprehensively investigate the generalizability of the proposed model, it was tested in intra-subject and speed, intra-subject and inter-speed, and inter-subject and speed scenarios and compared with BiLSTM, standard LSTM, and multi-layer perceptron (MLP) approaches. Normalized root-mean-square error and correlation coefficient were used as performance metrics. According to nonparametric statistical tests, the proposed AM-BiLSTM model significantly outperformed the BiLSTM network as well as classical techniques including LSTM and MLP networks in all three experiments (p-value < 0.05) and achieved state-of-the-art performance. Based on our findings, the AM-BiLSTM model may facilitate the deployment of intuitive user-driven deep learning-based control schemes for a wide variety of miniaturized robotic lower-limb devices, including myoelectric prostheses.

A tool for measuring mental workload during prosthesis use: The Prosthesis Task Load Index (PROS-TLX).

When using a upper-limb prosthesis, mental, emotional, and physical effort is often experienced. These have been linked to high rates of device dissatisfaction and rejection. Therefore, understanding and quantifying the complex nature of workload experienced when using, or learning to use, a upper-limb prosthesis has practical and clinical importance for researchers and applied professionals. The aim of this paper was to design and validate a self-report measure of mental workload specific to prosthesis use (The Prosthesis Task Load Index; PROS-TLX) that encapsulates the array of mental, physical, and emotional demands often experienced by users of these devices. We first surveyed upper-limb prosthetic limb users who confirmed the importance of eight workload constructs taken from published literature and previous workload measures. These constructs were mental demands, physical demands, visual demands, conscious processing, frustration, situational stress, time pressure and device uncertainty. To validate the importance of these constructs during initial prosthesis learning, we then asked able-bodied participants to complete a coin-placement task using their anatomical hand and then using a myoelectric prosthesis simulator under low and high mental workload. As expected, using a prosthetic hand resulted in slower movements, more errors, and a greater tendency to visually fixate the hand (indexed using eye-tracking equipment). These changes in performance were accompanied by significant increases in PROS-TLX workload subscales. The scale was also found to have good convergent and divergent validity. Further work is required to validate whether the PROS-TLX can provide meaningful clinical insights to the workload experienced by clinical users of prosthetic devices.

Alternative muscle synergy patterns of upper limb amputees.

Myoelectric hand prostheses are effective tools for upper limb amputees to regain hand functions. Much progress has been made with pattern recognition algorithms to recognize surface electromyography (sEMG) patterns, but few attentions was placed on the amputees' motor learning process. Many potential myoelectric prostheses users could not fully master the control or had declined performance over time. It is possible that learning to produce distinct and consistent muscle activation patterns with the residual limb could help amputees better control the myoelectric prosthesis. In this study, we observed longitudinal effect of motor skill learning with 2 amputees who have developed alternative muscle activation patterns in response to the same set of target prosthetic actions. During a 10-week program, amputee participants were trained to produce distinct and constant muscle activations with visual feedback of live sEMG and without interaction with prosthesis. At the end, their sEMG patterns were different from each other and from non-amputee control groups. For certain intended hand motion, gradually reducing root mean square (RMS) variance was observed. The learning effect was also assessed with a CNN-LSTM mixture classifier designed for mobile sEMG pattern recognition. The classification accuracy had a rising trend over time, implicating potential performance improvement of myoelectric prosthesis control. A follow-up session took place 6 months after the program and showed lasting effect of the motor skill learning in terms of sEMG pattern classification accuracy. The results indicated that with proper feedback training, amputees could learn unique muscle activation patterns that allow them to trigger intended prosthesis functions, and the original motor control scheme is updated. The effect of such motor skill learning could help to improve myoelectric prosthetic control performance.

Force/moment tracking performance during constant-pose, force-varying, bilaterally symmetric, hand-wrist tasks.

Bilateral movement is widely used for calibration of myoelectric prosthesis controllers, and is also relevant as rehabilitation therapy for patients with motor impairment and for athletic training. Target tracking and/or force matching tasks can be used to elicit such bilateral movement. Limited descriptive accuracy data exist in able-bodied subjects for bilateral target tracking or dominant vs non-dominant dynamic force matching tasks requiring more than one degree of freedom (DoF). We examined dynamic trajectory (0.75Hz band-limited, white, uniform random) constant-posture, hand open-close, wrist pronation-supination target tracking and matching tasks. Tasks were normalized to maximum voluntary contraction (MVC), spanning a±30% MVC force range, in four 1-DoF and 2-DoF tasks: (1, 2) unilateral dominant limb tracking with/without visual feedback, and (3, 4) bilateral dominant/non-dominant limb tracking with mirror visual feedback. In 12 able-bodied subjects, unilateral tracking error with visual feedback averaged 10-15%MVC, but up to 30%MVC without visual feedback. Bilateral matching error averaged∼10%MVC and was affected little by visual feedback type, so long as feedback was provided. In 1-DoF bilateral tracking, the dominant side had statistically lower error than the non-dominant side. In 2-DoF bilateral tracking, the side providing mirror visual feedback exhibited lower error than the opposite side. In 2-DoF tasks (assumed to be more challenging than their constituent 1-DoF tracking tasks), hand grip force errors grew disproportionately larger than those of each wrist DoF. In unilateral 1-DoF tasks, both hand vs target and wrist vs target latency averaged 250-350ms. In unilateral 2-DoF tasks, wrist vs target latency also averaged 250-350ms, while hand vs target latency averaged>500ms. These results provide guidance on bilateral 2-DoF hand-wrist performance in target tracking, and dominant vs non-dominant force matching tasks.

A Consensus Approach for Targeted Muscle Reinnervation in Amputees

Amputations have been performed with few modifications since the dawn of surgery. Blood vessels are ligated, bones are shortened, and nerves are cut. In a percentage of people, this can result in severe neuropathic, residual limb, and phantom limb pain. Targeted muscle reinnervation is a surgical procedure initially conceived to optimize function for myoelectric prostheses in amputees. Recently, it has been adopted more widely by surgeons for the prevention and treatment of neuropathic pain. Perhaps as a function of its relatively recent development, many authors perform this operation differently, and there has been no overall agreement regarding the principles, indications, technical specifics, and postoperative management guidelines. This article is written as a consensus statement by surgeons focused on the treatment of neuropathic pain and those with extensive experience performing targeted muscle reinnervation. It is designed to serve as a roadmap and template for extremity surgeons to consider when performing targeted muscle reinnervation.

Multivariable grasping force control of myoelectric multi-fingered hand prosthesis

Multivariable grasping force control of myoelectric multi-fingered hand prosthesis

The Starfish Procedure for Independent Digital Control of a Myoelectric Prosthesis.

Management of partial hand amputations is a notable clinical challenge. Historically, myoelectric prostheses have not allowed for independent digital control, resulting in unsatisfactory function and high rejection rates among upper extremity amputees. The Starfish Procedure was developed for patients who sustained loss of multiple digits through the level of the base of the proximal phalanx or distal metacarpal. The procedure involves the pedicled transfer of 1 or more dorsal interosseous muscles to a subcutaneous location. This allows for a myoelectric sensor to capture the signals generated by these transferred muscles, thereby enabling intuitive, independent, digital prosthetic flexion and extension. In this article, we detail the relevant anatomy, indications, and technique for performing the Starfish Procedure. Given our patients' promising outcomes to date, we hope this technique paper will encourage upper extremity surgeons of all training backgrounds to perform this relatively straightforward procedure, thereby allowing patients with life-altering finger amputations to regain meaningful function by enhancing control of digital prostheses.

A 2-Degree-of-Freedom Quasi-Passive Prosthetic Wrist With Two Levels of Compliance

Restoring the function of a missing hand is still a grand challenge for bioengineers. We witnessed significant recent advances in the development of myoelectric hand prostheses and their controllers. Conversely, the wrist joint is generally overlooked in prosthetics, despite playing a fundamental role in orienting the hand in space. Indeed, it may account for several degrees of freedom of the hand in reducing compensatory movements. We acknowledge that an active, three-degree-of-freedom prosthetic wrist is not a viable option for a self-contained prosthesis, therefore we merged in one design two opposed passive behaviors. The proposed wrist can automatically transition between a compliant mode, which exhibits relatively low stiffness allowing for passive motions around two rotational axes (wrist flexion/extension and radial/ulnar deviation), and a stiff mode, which grants stability during manipulation. To switch mode, no additional control input - hence cognitive burden - from the user is needed: it occurs synchronously with the prosthetic hand opening and closing motion, such that the wrist is compliant during reaching and stiff during manipulation. Our device proved reliable on the test bench and useful in a pilot test with an amputee volunteer, motivating further developments and more extensive testing to prove its effectiveness.

Biomechanical Analysis of Body Movements of Myoelectric Prosthesis Users During Standardized Clinical Tests.

The objective clinical evaluation of user's capabilities to handle their prosthesis is done using various tests which primarily focus on the task completion speed and do not explicitly account for the potential presence of compensatory motions. Given that the excessive body compensation is a common indicator of inadequate prosthesis control, tests which include subjective observations on the quality of performed motions have been introduced. However, these metrics are then influenced by the examiner's opinions, skills, and training making them harder to standardize across patient pools and compare across different prosthetic technologies. Here we aim to objectively quantify the severity of body compensations present in myoelectric prosthetic hand users and evaluate the extent to which traditional objective clinical scores are still able to capture them. We have instructed 9 below-elbow prosthesis users and 9 able-bodied participants to complete three established objective clinical tests: Box-and-Blocks-Test, Clothespin-Relocation-Test, and Southampton-Hand-Assessment-Procedure. During all tests, upper-body kinematics has been recorded. While the analysis showed that there are some correlations between the achieved clinical scores and the individual body segment travel distances and average speeds, there were only weak correlations between the clinical scores and the observed ranges of motion. At the same time, the compensations were observed in all prosthesis users and, for the most part, they were substantial across the tests. The sole reliance on the currently available objective clinical assessment methods seems inadequate as the compensatory movements are prominent in prosthesis users and yet not sufficiently accounted for.

Development and testing of a virtual simulator for a myoelectric prosthesis prototype – the PRISMA Hand II – to improve its usability and acceptability

Artificial limbs can help people missing body parts to restore some of their daily-life activities. However, the user should spend up to a few months to intuitively control the new device. During this period, she/he may suffer pain due to wearing or using the prosthesis inappropriately. This research presents a virtual simulator that allows the user to carry out training sessions for controlling the prosthesis. A set of Surface Electromyographic (sEMG) sensors are used to acquire the signals from user’s muscles and send them to a recognition algorithm that interprets the patient’s intentions. Simultaneously, the patient observes the response of her/his device on the simulator. Two studies are presented: the first study evaluate the performance of three different recognition algorithms i.e., Linear Discriminant Analysis (LDA), Support Vector Machine (SVM), and Multi-Layer Perceptron (MLP), based on the successful recognition of the patient’s intentions. The second study investigates the least number of sEMG sensors to be used, as having less components improves the patient’s wearability and decreases the processing time. The developed simulator represents a real prosthetic device, PRISMA hand II.The results showed the superiority of the MLP with 80% of successful recognition when 6-sEMG sensors are used. If a reduced set of gestures is considered (frequently needed by the patient), 90% of successful recognition could be achieved. Less sEMG sensors significantly degraded the performance of the recognition algorithm as only 53.8% of successful recognition could be achieved. All experiments were conducted with the help of a patient with below-elbow amputation.

Demystifying Targeted Muscle Reinnervation: A Systematic Review of Nerve Transfers for the Lower Extremity.

A systematic literature review was performed and contained the following inclusion criteria: original cases of primary or secondary lower extremity amputation defects or nerve-related pain that underwent TMR with clearly described target muscles for each nerve transfer. Studies were excluded if the cases had been previously described or contained incomplete data. The primary outcomes were nerves transferred and muscles targeted. Target muscle options were presented in tables specific to anatomic region, and cross-sectional schematics were created for intraoperative assistance. Seventeen studies presenting original cases with clearly described nerve transfers and target muscles in the lower extremity were included in the review. Target muscle selection for all nerve transfers at the transfemoral and transtibial levels were presented in separate tables. Reports of early experience at multiple institutions identify trends in the selection of certain target muscles for nerve transfers in transfemoral and transtibial TMR. Familiarity with these common target muscles and nerve transfers can simplify intraoperative decision-making and enhance integration of lower extremity TMR in amputation care and in the treatment of nerve-related pain.

The multi-grip and standard myoelectric hand prosthesis compared: does the multi-grip hand live up to its promise?

BackgroundMulti-grip myoelectric hand prostheses (MHPs), with five movable and jointed fingers, have been developed to increase functionality. However, literature comparing MHPs with standard myoelectric hand prostheses (SHPs) is limited and inconclusive. To establish whether MHPs increase functionality, we compared MHPs with SHPs on all categories of the International Classification of Functioning, Disability, and Health-model (ICF-model).MethodsMHP users (N = 14, 64.3% male, mean age = 48.6 years) performed physical measurements (i.e., Refined Clothespin Relocation Test (RCRT), Tray-test, Box and Blocks Test, Southampton Hand Assessment Procedure) with their MHP and an SHP to compare the joint angle coordination and functionality related to the ICF-categories ‘Body Function’ and ‘Activities’ (within-group comparisons). SHP users (N = 19, 68.4% male, mean age = 58.1 years) and MHP users completed questionnaires/scales (i.e., Orthotics and Prosthetics Users’ Survey—The Upper Extremity Functional Status Survey /OPUS–UEFS, Trinity Amputation and Prosthesis Experience Scales for upper extremity/TAPES-Upper, Research and Development-36/RAND-36, EQ-5D-5L, visual analogue scale/VAS, the Dutch version of the Quebec User Evaluation of Satisfaction with assistive technology/D-Quest, patient-reported outcome measure to assess the preferred usage features of upper limb prostheses/PUF-ULP) to compare user experiences and quality of life in the ICF-categories ‘Activities’, ‘Participation’, and ‘Environmental Factors’ (between-group comparisons).Results‘Body Function’ and ‘Activities’: nearly all users of MHPs had similar joint angle coordination patterns with an MHP as when they used an SHP. The RCRT in the upward direction was performed slower in the MHP condition compared to the SHP condition. No other differences in functionality were found. ‘Participation’: MHP users had a lower EQ-5D-5L utility score; experienced more pain or limitations due to pain (i.e., measured with the RAND-36). ‘Environmental Factors’: MHPs scored better than SHPs on the VAS-item holding/shaking hands. The SHP scored better than the MHP on five VAS-items (i.e., noise, grip force, vulnerability, putting clothes on, physical effort to control) and the PUF-ULP.ConclusionMHPs did not show relevant differences in outcomes compared to SHPs on any of the ICF-categories. This underlines the importance of carefully considering whether the MHP is the most suitable option for an individual taking into account the additional costs of MHPs.

Simultaneous and Proportional Real-Time Myocontrol of Up to Three Degrees of Freedom of the Wrist and Hand.

Here, we introduce a system for intuitive concurrent hand and wrist control, based on a robust feature-extraction protocol and machine-learning. Using the mean absolute value of high-density EMG, we train a ridge-regressor (RR) on only the sustained portions of the single-DOF contractions and leverage the regressor's inherent ability to provide simultaneous multi-DOF estimates. In this way, we robustly capture the amplitude information of the inputs while harnessing the power of the RR to extrapolate otherwise noisy and often overfitted estimations of dynamic portions of movements. The real-time evaluation of the system on 13 able-bodied participants and an amputee shows that almost all single-DOF tasks could be reached (96% success rate), while at the same time users were able to complete most of the two-DOF (62%) and even some of the very challenging three-DOF tasks (37%). To further investigate the translational potential of the approach, we reduced the original 192-channel setup to a 16-channel configuration and the observed performance did not deteriorate. Notably, the amputee performed similarly well to the other participants, according to all considered metrics. This is the first real-time operated myocontrol system that consistently provides intuitive simultaneous and proportional control over 3-DOFs of wrist and hand, relying on only surface EMG signals from the forearm. Focusing on reduced complexity, a real-time test and the inclusion of an amputee in the study demonstrate the translational potential of the control system for future applications in prosthetic control.

Neural evidence for functional roles of tactile and visual feedback in the application of myoelectric prosthesis

Objective. The primary purpose of this study was to investigate the electrophysiological mechanism underlying different modalities of sensory feedback and multi-sensory integration in typical prosthesis control tasks. Approach. We recruited 15 subjects and developed a closed-loop setup for three prosthesis control tasks which covered typical activities in the practical prosthesis application, i.e. prosthesis finger position control (PFPC), equivalent grasping force control (GFC) and box and block control (BABC). All the three tasks were conducted under tactile feedback (TF), visual feedback (VF) and tactile-visual feedback (TVF), respectively, with a simultaneous electroencephalography (EEG) recording to assess the electroencephalogram (EEG) response underlying different types of feedback. Behavioral and psychophysical assessments were also administered in each feedback condition. Results. EEG results showed that VF played a predominant role in GFC and BABC tasks. It was reflected by a significantly lower somatosensory alpha event-related desynchronization (ERD) in TVF than in TF and no significant difference in visual alpha ERD between TVF and VF. In PFPC task, there was no significant difference in somatosensory alpha ERD between TF and TVF, while a significantly lower visual alpha ERD was found in TVF than in VF, indicating that TF was essential in situations related to proprioceptive position perception. Tactile-visual integration was found when TF and VF were congruently implemented, showing an obvious activation over the premotor cortex in the three tasks. Behavioral and psychophysical results were consistent with EEG evaluations. Significance. Our findings could provide neural evidence for multi-sensory integration and functional roles of tactile and VF in a practical setting of prosthesis control, shedding a multi-dimensional insight into the functional mechanisms of sensory feedback.

Limb loading enhances skill transfer between augmented and physical reality tasks during limb loss rehabilitation

BackgroundVirtual and augmented reality (AR) have become popular modalities for training myoelectric prosthesis control with upper-limb amputees. While some systems have shown moderate success, it is unclear how well the complex motor skills learned in an AR simulation transfer to completing the same tasks in physical reality. Limb loading is a possible dimension of motor skill execution that is absent in current AR solutions that may help to increase skill transfer between the virtual and physical domains.MethodsWe implemented an immersive AR environment where individuals could operate a myoelectric virtual prosthesis to accomplish a variety of object relocation manipulations. Intact limb participants were separated into three groups, the load control (CGLD; N=4), the AR control (CGAR; N=4), and the experimental group (EG; N=4). Both the CGAR and EG completed a 5-session prosthesis training protocol in AR while the CGLD performed simple muscle training. The EG attempted manipulations in AR while undergoing limb loading. The CGAR attempted the same manipulations without loading. All participants performed the same manipulations in physical reality while operating a real prosthesis pre- and post-training. The main outcome measure was the change in the number of manipulations completed during the physical reality assessments (i.e. completion rate). Secondary outcomes included movement kinematics and visuomotor behavior.ResultsThe EG experienced a greater increase in completion rate post-training than both the CGAR and CGLD. This performance increase was accompanied by a shorter motor learning phase, the EG’s performance saturating in less sessions of AR training than the CGAR.ConclusionThe results demonstrated that limb loading plays an important role in transferring complex motor skills learned in virtual spaces to their physical reality analogs. While participants who did not receive limb loading were able to receive some functional benefit from AR training, participants who received the loading experienced a greater positive change in motor performance with their performance saturating in fewer training sessions.

Myoelectric prosthesis hand grasp control following targeted muscle reinnervation in individuals with transradial amputation.

Despite the growing availability of multifunctional prosthetic hands, users' control and overall functional abilities with these hands remain limited. The combination of pattern recognition control and targeted muscle reinnervation (TMR) surgery, an innovative technique where amputated nerves are transferred to reinnervate new muscle targets in the residual limb, has been used to improve prosthesis control of individuals with more proximal upper limb amputations (i.e., shoulder disarticulation and transhumeral amputation). The goal of this study was to determine if prosthesis hand grasp control improves following transradial TMR surgery. Eight participants were trained to use a multi-articulating hand prosthesis under myoelectric pattern recognition control. All participated in home usage trials pre- and post-TMR surgery. Upper limb outcome measures were collected following each home trial. Three outcome measures (Southampton Hand Assessment Procedure, Jebsen-Taylor Hand Function Test, and Box and Blocks Test) improved 9-12 months post-TMR surgery compared with pre-surgery measures. The Assessment of Capacity for Myoelectric Control and Activities Measure for Upper Limb Amputees outcome measures had no difference pre- and post-surgery. An offline electromyography analysis showed a decrease in grip classification error post-TMR surgery compared to pre-TMR surgery. Additionally, a majority of subjects noted qualitative improvements in their residual limb and phantom limb sensations post-TMR. The potential for TMR surgery to result in more repeatable muscle contractions, possibly due to the reduction in pain levels and/or changes to phantom limb sensations, may increase functional use of many of the clinically available dexterous prosthetic hands.

Virtual regression-based myoelectric hand-wrist prosthesis control and electrode site selection using no force feedback

Most transradial prosthesis users with conventional “Sequential” myoelectric control have two electrode sites which control one degree of freedom (DoF) at a time. Rapid EMG co-activation toggles control between DoFs (e.g., hand and wrist), providing limited function. We implemented a regression-based EMG control method which achieved simultaneous and proportional control of two DoFs in a virtual task. We automated electrode site selection using a short-duration (90 s) calibration period, without force feedback. Backward stepwise selection located the best electrodes for either six or 12 electrodes (selected from a pool of 16). We additionally studied two, 2-DoF controllers: “Intuitive” control (hand open-close and wrist pronation-supination controlled virtual target size and rotation, respectively) and “Mapping” control (wrist flexion–extension and ulnar-radial deviation controlled virtual target left–right and up-down movement, respectively). In practice, a Mapping controller would be mapped to control prosthesis hand open-close and wrist pronation-supination. Eleven able-bodied subjects and 4 limb-absent subjects completed virtual target matching tasks (fixed target moves to a new location after being “matched,” and subject immediately pursues) and fixed (static) target tasks. For all subjects, both 2-DoF controllers with 6 optimally-sited electrodes had statistically better target matching performance than Sequential control in number of matches (average of 4–7 vs 2 matches, p < 0.001) and throughput (average of 0.75–1.25 vs 0.4 bits/s, p < 0.001), but not overshoot rate and path efficiency. There were no statistical differences between 6 and 12 optimally-sited electrodes for both 2-DoF controllers. These results support the feasibility of 2-DoF simultaneous, proportional myoelectric control.