Myoelectric Prosthesis Control Research Articles

New insights into muscle activity associated with phantom hand movements in transhumeral amputees

IntroductionMuscle activity patterns in the residual arm are systematically present during phantom hand movements (PHM) in transhumeral amputees. However, their characteristics have not been directly investigated yet, leaving their neurophysiological origin poorly understood. This study pioneers a neurophysiological perspective in examining PHM-related muscle activity patterns by characterizing and comparing them with those in the arm, forearm, and hand muscles of control participants executing intact hand movements (IHM) of similar types.MethodsTo enable rigorous comparison, we developed meta-variables independent of electrode placement, quantifying the phasic profile of recorded surface EMG signals and the specificity of their patterns across electrode sites and movement types.ResultsSimilar to the forearm and hand muscles during IHM, each signal recorded from the residual upper arm during PHM displays a phasic profile, synchronized with the onset and offset of each movement repetition. Furthermore, the PHM-related patterns of phasic muscle activity are specific not only to the type of movement but also to the electrode site, even within the same upper arm muscle, while these muscles exhibit homogeneous activities in intact arms.DiscussionOur results suggest the existence of peripheral reorganization, eventually leading to the emergence of independently controlled muscular sub-volumes. This reorganization potentially occurs through the sprouting of severed axons and the recapture of muscle fibers in the residual limb. Further research is imperative to comprehend this mechanism and its relationship with PHM, holding significant implications for the rehabilitation process and myoelectric prosthesis control.

Optimizing Sensor Placement and Machine Learning Techniques for Accurate Hand Gesture Classification

Millions of individuals are living with upper extremity amputations, making them potential beneficiaries of hand and arm prostheses. While myoelectric prostheses have evolved to meet amputees’ needs, challenges remain related to their control. This research leverages surface electromyography sensors and machine learning techniques to classify five fundamental hand gestures. By utilizing features extracted from electromyography data, we employed a nonlinear, multiple-kernel learning-based support vector machine classifier for gesture recognition. Our dataset encompassed eight young nondisabled participants. Additionally, our study conducted a comparative analysis of five distinct sensor placement configurations. These configurations capture electromyography data associated with index finger and thumb movements, as well as index finger and ring finger movements. We also compared four different classifiers to determine the most capable one to classify hand gestures. The dual-sensor setup strategically placed to capture thumb and index finger movements was the most effective—this dual-sensor setup achieved 90% accuracy for classifying all five gestures using the support vector machine classifier. Furthermore, the application of multiple-kernel learning within the support vector machine classifier showcases its efficacy, achieving the highest classification accuracy amongst all classifiers. This study showcased the potential of surface electromyography sensors and machine learning in enhancing the control and functionality of myoelectric prostheses for individuals with upper extremity amputations.

A multifaceted suite of metrics for comparative myoelectric prosthesis controller research.

Upper limb robotic (myoelectric) prostheses are technologically advanced, but challenging to use. In response, substantial research is being done to develop person-specific prosthesis controllers that can predict a user's intended movements. Most studies that test and compare new controllers rely on simple assessment measures such as task scores (e.g., number of objects moved across a barrier) or duration-based measures (e.g., overall task completion time). These assessment measures, however, fail to capture valuable details about: the quality of device arm movements; whether these movements match users' intentions; the timing of specific wrist and hand control functions; and users' opinions regarding overall device reliability and controller training requirements. In this work, we present a comprehensive and novel suite of myoelectric prosthesis control evaluation metrics that better facilitates analysis of device movement details-spanning measures of task performance, control characteristics, and user experience. As a case example of their use and research viability, we applied these metrics in real-time control experimentation. Here, eight participants without upper limb impairment compared device control offered by a deep learning-based controller (recurrent convolutional neural network-based classification with transfer learning, or RCNN-TL) to that of a commonly used controller (linear discriminant analysis, or LDA). The participants wore a simulated prosthesis and performed complex functional tasks across multiple limb positions. Analysis resulting from our suite of metrics identified 16 instances of a user-facing problem known as the "limb position effect". We determined that RCNN-TL performed the same as or significantly better than LDA in four such problem instances. We also confirmed that transfer learning can minimize user training burden. Overall, this study contributes a multifaceted new suite of control evaluation metrics, along with a guide to their application, for use in research and testing of myoelectric controllers today, and potentially for use in broader rehabilitation technologies of the future.

Machine learning for hand pose classification from phasic and tonic EMG signals during bimanual activities in virtual reality.

Myoelectric prostheses have recently shown significant promise for restoring hand function in individuals with upper limb loss or deficiencies, driven by advances in machine learning and increasingly accessible bioelectrical signal acquisition devices. Here, we first introduce and validate a novel experimental paradigm using a virtual reality headset equipped with hand-tracking capabilities to facilitate the recordings of synchronized EMG signals and hand pose estimation. Using both the phasic and tonic EMG components of data acquired through the proposed paradigm, we compare hand gesture classification pipelines based on standard signal processing features, convolutional neural networks, and covariance matrices with Riemannian geometry computed from raw or xDAWN-filtered EMG signals. We demonstrate the performance of the latter for gesture classification using EMG signals. We further hypothesize that introducing physiological knowledge in machine learning models will enhance their performances, leading to better myoelectric prosthesis control. We demonstrate the potential of this approach by using the neurophysiological integration of the "move command" to better separate the phasic and tonic components of the EMG signals, significantly improving the performance of sustained posture recognition. These results pave the way for the development of new cutting-edge machine learning techniques, likely refined by neurophysiology, that will further improve the decoding of real-time natural gestures and, ultimately, the control of myoelectric prostheses.

Using Serious Games to Measure Upper-Limb Myoelectric Pattern Recognition Prosthesis Control Performance in an At-Home Environment

Using Serious Games to Measure Upper-Limb Myoelectric Pattern Recognition Prosthesis Control Performance in an At-Home Environment

Social media analysis of pain outcomes following targeted muscle reinnervation

AimTMR was developed to improve myoelectric prosthesis control for amputees; however, it has become an area of interest in pain modulation. Studies show evidence that this procedure alleviates chronic pain in amputees. The primary objective of this study is to utilize social media analysis to understand patients’ post-operative pain, satisfaction, and recovery time after TMR. MethodsData was collected from one Facebook group via posts and comments referencing TMR. Posts published between January 1st, 2020, and March 24th, 2023, were analyzed. Data collected included pain prior to surgery, pain in immediate post-op period, and how pain changed after surgery. Results43 individuals commented on their TMR experience. 31 had favorable surgical outcomes, 7 felt surgery worsened their pain or there was not significant change in their pain levels, and the remaining 5 commented during the initial post-operative period. 24 patients described their pain in the immediate post-operative period and 100% said that post-operative pain was worse than chronic pain. Of 28 authors who commented on overall reduction in chronic pain, 24 reported TMR reduced their pain, while 4 had no change or worsened pain. ConclusionsThe 24 patients who reported improvement in chronic pain aligns with current literature suggesting that TMR is a viable treatment option for pain. Current medical management of similar conditions leaves up to 80% of patients unsatisfied with pain control. This analysis supports the evidence that TMR is an effective treatment for patients who are experiencing post-amputation pain.

Targeted muscle reinnervation in bionic upper limb reconstruction: current status and future directions.

Selective nerve transfers are used in the setting of upper limb amputation to improve myoelectric prosthesis control. This surgical concept is referred to as targeted muscle reinnervation (TMR) and describes the rerouting of the major nerves of the arm onto the motor branches of the residual limb musculature. Aside from providing additional myosignals for prosthetic control, TMR can treat and prevent neuroma pain and possibly also phantom limb pain. This article reviews the history and current applications of TMR in upper limb amputation, with a focus on practical considerations. It further explores and identifies technological innovations to improve the man-machine interface in amputation care, particularly regarding implantable interfaces, such as muscle electrodes and osseointegration. Finally, future clinical directions and possible scientific avenues in this field are presented and critically discussed.

Огляд досліджень в напрямку міоелектричного методу керування біонічним протезом

Myoelectric control of bionic prostheses is an important field of research in the field of rehabilitation. Intuitive and intelligent myoelectric control can restore upper limb function. However, much research now focuses on the development of various myoelectrical and biotechnical control methods, limiting research to the complex daily tasks of prosthetic manipulation, such as grasping and releasing. The article examines the latest advances in the research areas of bionic prosthesis management. In particular, attention is paid to the methods of determining movement intentions, classification of discrete movements, estimation of continuous movements, single-channel control, feedback control and combined control. Motor neurons group input signals from the central nervous system that affect muscles and form motor units. The electromyography (EMG) signal, which is obtained by recording motor neuron action potentials, reflects muscle activity. This signal, oscillating within ±5000 μV with a frequency of 6 to 500 Hz, reflects the characteristics of muscle contraction. Depending on the location of the sensors, EMG signals are divided into intramuscular and surface electromyography. Intramuscular electromyography provides an accurate study of muscle activation, but requires the implantation of sensors, which can lead to physical problems. EMG, which captures a signal from the surface of the skin, is easier to use and is widely used in experiments with myoelectric prostheses.

Combining Surgical Innovations in Amputation Surgery-Robotic Harvest of the Rectus Abdominis Muscle, Transplantation and Targeted Muscle Reinnervation Improves Myocontrol Capability and Pain in a Transradial Amputee.

Adding robotic surgery to bionic reconstruction might open a new dimension. The objective was to evaluate if a robotically harvested rectus abdominis (RA) transplant is a feasible procedure to improve soft-tissue coverage at the residual limb (RL) and serve as a recipient for up to three nerves due to its unique architecture and to allow the generation of additional signals for advanced myoelectric prosthesis control. A transradial amputee with insufficient soft-tissue coverage and painful neuromas underwent the interventions and was observed for 18 months. RA muscle was harvested using robotic-assisted surgery and transplanted to the RL, followed by end-to-end neurroraphy to the recipient nerves of the three muscle segments to reanimate radial, median, and ulnar nerve function. The transplanted muscle healed with partial necrosis of the skin mesh graft. Twelve months later, reliable, and spatially well-defined Hoffmann-Tinel signs were detectable at three segments of the RA muscle flap. No donor-site morbidities were present, and EMG activity could be detected in all three muscle segments. The linear discriminant analysis (LDA) classifier could reliably distinguish three classes within 1% error tolerance using only the three electrodes on the muscle transplant and up to five classes outside the muscle transplant. The combination of these surgical procedure advances with emerging (myo-)control technologies can easily be extended to different amputation levels to reduce RL complications and augment control sites with a limited surface area, thus facilitating the usability of advanced myoelectric prostheses.

Exploring the EMG transient: the muscular activation sequences used as novel time-domain features for hand gestures classification.

Muscular activation sequences have been shown to be suitable time-domain features for classification of motion gestures. However, their clinical application in myoelectric prosthesis control was never investigated so far. The aim of the paper is to evaluate the robustness of these features extracted from the EMG signal in transient state, on the forearm, for classifying common hand tasks. The signal associated to four hand gestures and the rest condition were acquired from ten healthy people and two persons with trans-radial amputation. A feature extraction algorithm allowed for encoding the EMG signals into muscular activation sequences, which were used to train four commonly used classifiers, namely Linear Discriminant Analysis (LDA), Support Vector Machine (SVM), Non-linear Logistic Regression (NLR) and Artificial Neural Network (ANN). The offline performances were assessed with the entire sample of recruited people. The online performances were assessed with the amputee subjects. Moreover, a comparison of the proposed method with approaches based on the signal envelope in the transient state and in the steady state was conducted. The highest performance were obtained with the NLR classifier. Using the sequences, the offline classification accuracy was higher than 93% for healthy and amputee subjects and always higher than the approach with the signal envelope in transient state. As regards the comparison with the steady state, the performances obtained with the proposed method are slightly lower (<4%), but the classification occurred at least 200 ms earlier. In the online application, the motion completion rate reached up to 85% of the total classification attempts, with a motion selection time that never exceeded 218 ms. Muscular activation sequences are suitable alternatives to the time-domain features commonly used in classification problems belonging to the sole EMG transient state and could be potentially exploited in control strategies of myoelectric prosthesis hands.

Improving Motion Intention Recognition for Trans-Radial Amputees Based on sEMG and Transfer Learning

Hand motion intentions can be detected by analyzing the surface electromyographic (sEMG) signals obtained from the remaining forearm muscles of trans-radial amputees. This technology sheds new light on myoelectric prosthesis control; however, fewer signals from amputees can be collected in clinical practice. The collected signals can further suffer from quality deterioration due to the muscular atrophy of amputees, which significantly decreases the accuracy of hand motion intention recognition. To overcome these problems, this work proposed a transfer learning strategy combined with a long-exposure-CNN (LECNN) model to improve the amputees’ hand motion intention recognition accuracy. Transfer learning can leverage the knowledge acquired from intact-limb subjects to amputees, and LECNN can effectively capture the information in the sEMG signals. Two datasets with 20 intact-limb and 11 amputated-limb subjects from the Ninapro database were used to develop and evaluate the proposed method. The experimental results demonstrated that the proposed transfer learning strategy significantly improved the recognition performance (78.1%±19.9%, p-value &lt; 0.005) compared with the non-transfer case (73.4%±20.8%). When the source and target data matched well, the after-transfer accuracy could be improved by up to 8.5%. Compared with state-of-the-art methods in two previous studies, the average accuracy was improved by 11.6% (from 67.5% to 78.1%, p-value &lt; 0.005) and 12.1% (from 67.0% to 78.1%, p-value &lt; 0.005). This result is also among the best from the contrast methods.

Continuous joint velocity estimation using CNN-based deep learning for multi-DoF prosthetic wrist for activities of daily living.

Myoelectric control of prostheses is a long-established technique, using surface electromyography (sEMG) to detect user intention and perform subsequent mechanical actions. Most machine learning models utilized in control systems are trained using isolated movements that do not reflect the natural movements occurring during daily activities. Moreover, movements are often affected by arm postures, the duration of activities, and personal habits. It is crucial to have a control system for multi-degree-of-freedom (DoF) prosthetic arms that is trained using sEMG data collected from activities of daily living (ADL) tasks. This work focuses on two major functional wrist movements: pronation-supination and dart-throwing movement (DTM), and introduces a new wrist control system that directly maps sEMG signals to the joint velocities of the multi-DoF wrist. Additionally, a specific training strategy (Quick training) is proposed that enables the controller to be applied to new subjects and handle situations where sensors may displace during daily living, muscles can become fatigued, or sensors can become contaminated (e.g., due to sweat). The prosthetic wrist controller is designed based on data from 24 participants and its performance is evaluated using the Root Mean Square Error (RMSE) and Pearson Correlation. The results are found to depend on the characteristics of the tasks. For example, tasks with dart-throwing motion show smaller RSME values (Hammer: 6.68 deg/s and Cup: 7.92 deg/s) compared to tasks with pronation-supination (Bulb: 43.98 deg/s and Screw: 53.64 deg/s). The proposed control technique utilizing Quick training demonstrates a decrease in the average root mean square error (RMSE) value by 35% and an increase in the average Pearson correlation value by 40% across all four ADL tasks.

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.

Multivariable grasping force control of myoelectric multi-fingered hand prosthesis

Multivariable grasping force control of myoelectric multi-fingered hand prosthesis

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.

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.

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.

Closed-Loop Control of a Multifunctional Myoelectric Prosthesis With Full-State Anatomically Congruent Electrotactile Feedback.

State-of-the-art myoelectric hand prostheses provide multi-functional control but lack somatosensory feedback. To accommodate the full functionality of a dexterous prosthesis, the artificial sensory feedback needs to convey several degrees of freedom (DoF) simultaneously. However, this is a challenge with current methods as they are characterized by a low information bandwidth. In this study, we leverage the flexibility of a recently developed system for simultaneous electrotactile stimulation and electromyography (EMG) recording to present the first solution for closed-loop myoelectric control of a multifunctional prosthesis with full-state anatomically congruent electrotactile feedback. The novel feedback scheme (coupled encoding) conveyed proprioceptive (hand aperture, wrist rotation) and exteroceptive information (grasping force). The coupled encoding was compared to the conventional approach (sectorized encoding) and incidental feedback in 10 non-disabled and one amputee participant who used the system to perform a functional task. The results showed that both feedback approaches increased the accuracy of position control compared to incidental feedback. However, the feedback increased completion time, and it did not significantly improve grasping force control. Importantly, the performance of the coupled feedback was not significantly different compared to the conventional scheme, despite the latter being easier to learn during training. Overall, the results indicate that the developed feedback can improve prosthesis control across multiple DoFs but they also highlight the subjects' ability to exploit minimal incidental information. Importantly, the current setup is the first to convey three feedback variables simultaneously using electrotactile stimulation while providing multi-DoF myoelectric control with all hardware components mounted on the same forearm.

Training prosthesis users to switch between modes of a multi-articulating prosthetic hand

Purpose Producing triggers to switch between modes of myoelectric prosthetic hands has proven to be difficult. We evaluated whether digital training methods were feasible in individuals with an upper limb defect (ULD), whether myosignals in these individuals differ from those of non-impaired individuals and whether acquired skills transfer to prosthesis use. Materials and methods Two groups participated in a 9-day pre-test–post-test design study with seven 45-minute training sessions. One group trained using a serious game, the other with their myosignals digitally displayed. Both groups also trained using a prosthesis. The pre- and post-tests consisted of an adapted Clothespin Relocation Test and the spherical subset of the Southampton Hand Assessment Procedure. After the post-test, the System Usability Scale (SUS) was administered. Clinically relevant performance measures and myosignal features were analysed. Results Four individuals with a ULD participated. SUS-scores deemed both training methods feasible. Three participants produced only a few correct triggers. Myosignals features indicated larger variability for individuals with a ULD compared to non-impaired individuals (previously published data [1]). Three participants indicated transfer of skill. Conclusions Even though both training methods were deemed feasible and most participants showed transfer, seven training sessions were insufficient to learn reliable switching behaviour. Trial registration: The study was approved by the medical ethics committee of the University Medical Center Groningen (METc 2018.268). Implications for rehabilitation Switching between pre-programmed modes of a myoelectric prosthetic hand can be learned, however it does require training. Serious games can be considered useful training tools for trigger production in early phases of myoelectric prosthesis control training. In order to evoke transfer of skill from training to daily life both task-specificity and focus of attention during training should be taken into account.

Feasibility of Nerve Transfer to Palmaris Longus in Forearm-Level TMR: Anatomic Study and Clinical Series.

Targeted muscle re-innervation (TMR) is increasingly being used for treatment of postamputation pain and myoelectric prosthesis (MYP) control. Palmaris longus (PL) is a potential target following transradial amputation. The purpose of this study was to determine the branching pattern of the median nerve (MN) as it pertains to the PL motor branch entry point (MEP) and to present clinical results of patients who had PL used as a target. Eight cadaveric arms were dissected and branching patterns of the MN were documented. Additionally, we reviewed adult patients from a prospectively collected database who underwent TMR using PL. We recorded patient-reported outcomes and signal strength generated by the PL. The average distance from the medial epicondyle to PL MEP was 53 mm. All palmaris motor branches passed through a chiasm within the flexor digitorum superficialis muscle belly, which was a mean of 18 mm away from the MN proper. Patients with long-term follow-up reported an average Pain visual analog scale of 3.3 and Disabilities of the Arm, Shoulder and Hand of 46.2. All but one patient were using an MYP, and all generated at least 10 mV of signal from the PL, which is ample signal for surface electrode detection and MYP control. There were no postoperative neuromas and only one patient-reported postoperative phantom limb pain. Palmaris longus is a suitable target for TMR. Our objective measurements and anatomic relationships may help surgeons consistently find the PL's motor branch. Our series of patients reveal sufficient signal strength and acceptable clinical outcomes following TMR using the PL.