Initial Clinical Evaluation of the Modular Prosthetic Limb

Unsupervised neural decoding for concurrent and continuous multi-finger force prediction

Reliable prediction of multi-finger forces is crucial for neural-machine interfaces. Various neural decoding methods have progressed substantially for accurate motor output predictions. However, most neural decoding methods are performed in a supervised manner, i.e., the finger forces are needed for model training, which may not be suitable in certain contexts, especially in scenarios involving individuals with an arm amputation. To address this issue, we developed an unsupervised neural decoding approach to predict multi-finger forces using spinal motoneuron firing information. We acquired high-density surface electromyogram (sEMG) signals of the finger extensor muscle when subjects performed single-finger and multi-finger tasks of isometric extensions. We first extracted motor units (MUs) from sEMG signals of the single-finger tasks. Because of inevitable finger muscle co-activation, MUs controlling the non-targeted fingers can also be recruited. To ensure an accurate finger force prediction, these MUs need to be teased out. To this end, we clustered the decomposed MUs based on inter-MU distances measured by the dynamic time warping technique, and we then labeled the MUs using the mean firing rate or the firing rate phase amplitude. We merged the clustered MUs related to the same target finger and assigned weights based on the consistency of the MUs being retained. As a result, compared with the supervised neural decoding approach and the conventional sEMG amplitude approach, our new approach can achieve a higher R2 (0.77 ± 0.036 vs. 0.71 ± 0.11 vs. 0.61 ± 0.09) and a lower root mean square error (5.16 ± 0.58 %MVC vs. 5.88 ± 1.34 %MVC vs. 7.56 ± 1.60 %MVC). Our findings can pave the way for the development of accurate and robust neural-machine interfaces, which can significantly enhance the experience during human-robotic hand interactions in diverse contexts.

Transhumeral Arm Reaching Motion Prediction through Deep Reinforcement Learning-Based Synthetic Motion Cloning

The lack of intuitive controllability remains a primary challenge in enabling transhumeral amputees to control a prosthesis for arm reaching with residual limb kinematics. Recent advancements in prosthetic arm control have focused on leveraging the predictive capabilities of artificial neural networks (ANNs) to automate elbow joint motion and wrist pronation-supination during target reaching tasks. However, large quantities of human motion data collected from different subjects for various activities of daily living (ADL) tasks are required to train these ANNs. For example, the reaching motion can be altered when the height of the desk is changed; however, it is cumbersome to conduct human experiments for all conditions. This paper proposes a framework for cloning motion datasets using deep reinforcement learning (DRL) to cater to training data requirements. DRL algorithms have been demonstrated to create human-like synergistic motion in humanoid agents to handle redundancy and optimize movements. In our study, we collected real motion data from six individuals performing multi-directional arm reaching tasks in the horizontal plane. We generated synthetic motion data that mimicked similar arm reaching tasks by utilizing a physics simulation and DRL-based arm manipulation. We then trained a CNN-LSTM network with different configurations of training motion data, including DRL, real, and hybrid datasets, to test the efficacy of the cloned motion data. The results of our evaluation showcase the effectiveness of the cloned motion data in training the ANN to predict natural elbow motion accurately across multiple subjects. Furthermore, motion data augmentation through combining real and cloned motion datasets has demonstrated the enhanced robustness of the ANN by supplementing and diversifying the limited training data. These findings have significant implications for creating synthetic dataset resources for various arm movements and fostering strategies for automatized prosthetic elbow motion.

Synergy-Space Recurrent Neural Network for Transferable Forearm Motion Prediction from Residual Limb Motion

Transhumeral amputees experience considerable difficulties with controlling a multifunctional prosthesis (powered hand, wrist, and elbow) due to the lack of available muscles to provide electromyographic (EMG) signals. The residual limb motion strategy has become a popular alternative for transhumeral prosthesis control. It provides an intuitive way to estimate the motion of the prosthesis based on the residual shoulder motion, especially for target reaching tasks. Conventionally, a predictive model, typically an artificial neural network (ANN), is directly trained and relied upon to map the shoulder-elbow kinematics using the data from able-bodied subjects without extracting any prior synergistic information. However, it is essential to explicitly identify effective synergies and make them transferable across amputee users for higher accuracy and robustness. To overcome this limitation of the conventional ANN learning approach, this study explicitly combines the kinematic synergies with a recurrent neural network (RNN) to propose a synergy-space neural network for estimating forearm motions (i.e., elbow joint flexion-extension and pronation-supination angles) based on residual shoulder motions. We tested 36 training strategies for each of the 14 subjects, comparing the proposed synergy-space and conventional neural network learning approaches, and we statistically evaluated the results using Pearson's correlation method and the analysis of variance (ANOVA) test. The offline cross-subject analysis indicates that the synergy-space neural network exhibits superior robustness to inter-individual variability, demonstrating the potential of this approach as a transferable and generalized control strategy for transhumeral prosthesis control.

Virtual Reality for Evaluating Prosthetic Hand Control Strategies: A Preliminary Report

Improving prosthetic hand functionality is critical in reducing abandonment rates and improving the amputee's quality of life. Techniques such as joint force estimation and gesture recognition using myoelectric signals could enable more realistic control of the prosthetic hand. To accelerate the translation of these advanced control strategies from lab to clinic, We created a virtual prosthetic control environment that enables rich user interactions and dexterity evaluation. The virtual environment is made of two parts, namely the Unity scene for rendering and user interaction, and a Python back-end to support accurate physics simulation and communication with control algorithms. By utilizing the built-in tracking capabilities of a virtual reality headset, the user can visualize and manipulate a virtual hand without additional motion tracking setups. In the virtual environment, we demonstrate actuation of the prosthetic hand through decoded EMG signal streaming, hand tracking, and the use of a VR controller. By providing a flexible platform to investigate different control modalities, we believe that our virtual environment will allow for faster experimentation and further progress in clinical translation.

Toward More Robust Hand Gesture Recognition on EIT Data

Striving for more robust and natural control of multi-fingered hand prostheses, we are studying electrical impedance tomography (EIT) as a method to monitor residual muscle activations. Previous work has shown promising results for hand gesture recognition, but also lacks generalization across multiple sessions and users. Thus, the present paper aims for a detailed analysis of an existing EIT dataset acquired with a 16-electrode wrist band as a prerequisite for further improvements of machine learning results on this type of signal. The performed t-SNE analysis confirms a much stronger inter-session and inter-user variance compared to the expected in-class variance. Additionally, we observe a strong drift of signals within a session. To handle these challenging problems, we propose new machine learning architectures based on deep learning, which allow to separate undesired from desired variation and thus significantly improve the classification accuracy. With these new architectures we increased cross-session classification accuracy on 12 gestures from 19.55 to 30.45%. Based on a fundamental data analysis we developed three calibration methods and thus were able to further increase cross-session classification accuracy to 39.01, 55.37, and 56.34%, respectively.

Psychometric evaluation of the Southampton hand assessment procedure (SHAP) in a sample of upper limb prosthesis users

BACKGROUND

The 26-item Southampton Hand Assessment Protocol (SHAP) is a test of prosthetic hand function that generates an Index of Functionality (IOF), and prehensile pattern (PP) scores. Prior researchers identified potential issues in SHAP scoring, proposing alternative scoring methods (LIF and W-LIF).

STUDY DESIGN

Cross-sectional study.

PURPOSE

Evaluate the psychometric properties of the SHAP IOF, LIF, and W-LIF and PP scores and develop the Prosthesis Index of Functionality (P-IOF).

METHODS

We examined item completion, floor andceiling effects, concurrent, discriminant, construct and structural validity. The P-IOF used increased boundary limits and information from item completion and completion time. Calibration used a nonlinear mixed model. Scores were estimated using maximum a posteriori Bayesian estimation. Mixed integer linear programing (MILP) informed development of a shorter measure. Validity analyses were repeated using the P-IOF.

RESULTS

126 persons, mean age 57 (sd 15.8), 69% with transradial amputation were included. Floors effects were observed in 18.3%-19.1% for the IOF, LIF, and W-LIF. Ten items were not completed by >15% of participants. Boundary limits were problematic for all but 1 item. Correlations with dexterity measures were strong (r =  0.54-0.73). Scores differed by amputation level (p > .0001). Factor analysis did not support use of PP scores. The P-IOF used expanded boundary limits to decrease floor effects. MILP identified 10 items that could be dropped. The 26-item P-IOF and 16-item P-IOF had reduced floor effects (<7.5%), strong evidence of concurrent and discriminant validity, and construct validity. P-IOF reduced administrative burden by 9.5 (sd 5.6) minutes.

DISCUSSION

Floor effects limit a measure's ability to distinguish between persons with low function.

CONCLUSION

Analyses supported the validity of the SHAP IOF, LIF, and W-LIF, but identified large floor effects, as well as issues with structural validity of the PP scores. The 16-item P-IOF minimizes floor effects and reduces administrative burden.

Fascicle-Specific Targeting of Longitudinal Intrafascicular Electrodes for Motor and Sensory Restoration in Upper-Limb Amputees

Multichannel longitudinal intrafascicular electrode (LIFE) interfaces provide optimized balance of invasiveness and stability for chronic sensory stimulation and motor recording/decoding of peripheral nerve signals. Using a fascicle-specific targeting (FAST)-LIFE approach, where electrodes are individually placed within discrete sensory- and motor-related fascicular subdivisions of the residual ulnar and/or median nerves in an amputated upper limb, FAST-LIFE interfacing can provide discernment of motor intent for individual digit control of a robotic hand, and restoration of touch- and movement-related sensory feedback. The authors describe their findings from clinical studies performed with 6 human amputee trials using FAST-LIFE interfacing of the residual upper limb.

Extended home use of an advanced osseointegrated prosthetic arm improves function, performance, and control efficiency

Objective.Full restoration of arm function using a prosthesis remains a grand challenge; however, advances in robotic hardware, surgical interventions, and machine learning are bringing seamless human-machine interfacing closer to reality.Approach.Through extensive data logging over 1 year, we monitored at-home use of the dexterous Modular Prosthetic Limb controlled through pattern recognition of electromyography (EMG) by an individual with a transhumeral amputation, targeted muscle reinnervation, and osseointegration (OI).Main results.Throughout the study, continuous prosthesis usage increased (1% per week,p< 0.001) and functional metrics improved up to 26% on control assessments and 76% on perceived workload evaluations. We observed increases in torque loading on the OI implant (up to 12.5% every month,p< 0.001) and prosthesis control performance (0.5% every month,p< 0.005), indicating enhanced user integration, acceptance, and proficiency. More importantly, the EMG signal magnitude necessary for prosthesis control decreased, up to 34.7% (p< 0.001), over time without degrading performance, demonstrating improved control efficiency with a machine learning-based myoelectric pattern recognition algorithm. The participant controlled the prosthesis up to one month without updating the pattern recognition algorithm. The participant customized prosthesis movements to perform specific tasks, such as individual finger control for piano playing and hand gestures for communication, which likely contributed to continued usage.Significance.This work demonstrates, in a single participant, the functional benefit of unconstrained use of a highly anthropomorphic prosthetic limb over an extended period. While hurdles remain for widespread use, including device reliability, results replication, and technical maturity beyond a prototype, this study offers insight as an example of the impact of advanced prosthesis technology for rehabilitation outside the laboratory.

Concurrent Prediction of Finger Forces Based on Source Separation and Classification of Neuron Discharge Information

A reliable neural-machine interface is essential for humans to intuitively interact with advanced robotic hands in an unconstrained environment. Existing neural decoding approaches utilize either discrete hand gesture-based pattern recognition or continuous force decoding with one finger at a time. We developed a neural decoding technique that allowed continuous and concurrent prediction of forces of different fingers based on spinal motoneuron firing information. High-density skin-surface electromyogram (HD-EMG) signals of finger extensor muscle were recorded, while human participants produced isometric flexion forces in a dexterous manner (i.e. produced varying forces using either a single finger or multiple fingers concurrently). Motoneuron firing information was extracted from the EMG signals using a blind source separation technique, and each identified neuron was further classified to be associated with a given finger. The forces of individual fingers were then predicted concurrently by utilizing the corresponding motoneuron pool firing frequency of individual fingers. Compared with conventional approaches, our technique led to better prediction performances, i.e. a higher correlation ([Formula: see text] versus [Formula: see text]), a lower prediction error ([Formula: see text]% MVC versus [Formula: see text]% MVC), and a higher accuracy in finger state (rest/active) prediction ([Formula: see text]% versus [Formula: see text]%). Our decoding method demonstrated the possibility of classifying motoneurons for different fingers, which significantly alleviated the cross-talk issue of EMG recordings from neighboring hand muscles, and allowed the decoding of finger forces individually and concurrently. The outcomes offered a robust neural-machine interface that could allow users to intuitively control robotic hands in a dexterous manner.

Can Prosthetic Hands Mimic a Healthy Human Hand?

Historical evidence suggests that prostheses have been used since ancient Egyptian times. Prostheses were usually utilized for function and cosmetic appearances. Nowadays, with the advancement of technology, prostheses such as artificial hands can not only improve functional, but have psychological advantages as well and, therefore, can significantly enhance an individual’s standard of living. Combined with advanced science, a prosthesis is not only a simple mechanical device, but also an aesthetic, engineering and medical marvel. Prosthetic limbs are the best tools to help amputees reintegrate into society. In this article, we discuss the background and advancement of prosthetic hands with their working principles and possible future implications. We also leave with an open question to the readers whether prosthetic hands could ever mimic and replace our biological hands.

Clinical evaluation of the revolutionizing prosthetics modular prosthetic limb system for upper extremity amputees

Individuals with upper extremity (UE) amputation abandon prostheses due to challenges with significant device weight-particularly among myoelectric prostheses-and limited device dexterity, durability, and reliability among both myoelectric and body-powered prostheses. The Modular Prosthetic Limb (MPL) system couples an advanced UE prosthesis with a pattern recognition paradigm for intuitive, non-invasive prosthetic control. Pattern recognition accuracy and functional assessment-Box & Blocks (BB), Jebsen-Taylor Hand Function Test (JHFT), and Assessment of Capacity for Myoelectric Control (ACMC)-scores comprised the main outcomes. 10 participants were included in analyses, including seven individuals with traumatic amputation, two individuals with congenital limb absence, and one with amputation secondary to malignancy. The average (SD) time since limb loss, excluding congenital participants, was 85.9 (59.5) months. Participants controlled an average of eight motion classes compared to three with their conventional prostheses. All participants made continuous improvements in motion classifier accuracy, pathway completion efficiency, and MPL manipulation. BB and JHFT improvements were not statistically significant. ACMC performance improved for all participants, with mean (SD) scores of 162.6 (105.3), 213.4 (196.2), and 383.2 (154.3), p = 0.02 between the baseline, midpoint, and exit assessments, respectively. Feedback included lengthening the training period to further improve motion classifier accuracy and MPL control. The MPL has potential to restore functionality to individuals with acquired or congenital UE loss.

Portable Take-Home System Enables Proportional Control and High-Resolution Data Logging With a Multi-Degree-of-Freedom Bionic Arm

This paper describes a portable, prosthetic control system and the first at-home use of a multi-degree-of-freedom, proportionally controlled bionic arm. The system uses a modified Kalman filter to provide 6 degree-of-freedom, real-time, proportional control. We describe (a) how the system trains motor control algorithms for use with an advanced bionic arm, and (b) the system's ability to record an unprecedented and comprehensive dataset of EMG, hand positions and force sensor values. Intact participants and a transradial amputee used the system to perform activities-of-daily-living, including bi-manual tasks, in the lab and at home. This technology enables at-home dexterous bionic arm use, and provides a high-temporal resolution description of daily use—essential information to determine clinical relevance and improve future research for advanced bionic arms.

Sensory restoration by epidural stimulation of the lateral spinal cord in upper-limb amputees

Restoring somatosensory feedback to people with limb amputations is crucial to improve prosthetic control. Multiple studies have demonstrated that peripheral nerve stimulation and targeted reinnervation can provide somatotopically relevant sensory feedback. While effective, the surgical procedures required for these techniques remain a major barrier to translatability. Here, we demonstrate in four people with upper-limb amputation that epidural spinal cord stimulation (SCS), a common clinical technique to treat pain, evoked somatosensory percepts that were perceived as emanating from the missing arm and hand. Over up to 29 days, stimulation evoked sensory percepts in consistent locations in the missing hand regardless of time since amputation or level of amputation. Evoked sensations were occasionally described as naturalistic (e.g. touch or pressure), but were often paresthesias. Increasing stimulus amplitude increased the perceived intensity linearly, without increasing area of the sensations. These results demonstrate the potential of SCS as a tool to restore somatosensation after amputations.

Even some of the most advanced prosthetic arms lack an important feature: the ability to relay information about touch or pressure to the wearer. In fact, many people prefer to use simpler prostheses whose cables and harnesses pass on information about tension. However, recent studies suggest that electrical stimulation might give prosthesis users more sensation and better control.

After an amputation, the nerves that used to deliver sensory information from the hand still exist above the injury. Stimulating these nerves can help to recreate sensations in the missing limb and improve the control of the prosthesis. Still, this stimulation requires complicated surgical interventions to implant electrodes in or around the nerves. Spinal cord stimulation – a technique where a small electrical device is inserted near the spinal cord to stimulate nerves – may be an easier alternative. This approach only requires a simple outpatient procedure, and it is routinely used to treat chronic pain conditions.

Now, Chandrasekaran, Nanivadekar et al. show that spinal cord stimulation can produce the feeling of sensations in a person’s missing hand or arm. In the experiments, four people who had an arm amputation underwent spinal cord stimulation over 29 days. During the stimulation, the participants reported feeling electrical buzzing, vibration, or pressure in their missing limb. Changing the strength of the electric signals delivered to the spinal cord altered the intensity of these sensations.

The experiments are a step toward developing better prosthetics that restore some sensation. Further studies are now needed to determine whether spinal cord stimulation would allow people to perform sensory tasks with a prosthetic, for example handling an object that they cannot see.

Dexterous Force Estimation during Finger Flexion and Extension Using Motor Unit Discharge Information

With the development of advanced robotic hands, a reliable neural-machine interface is essential to take full advantage of the functional dexterity of the robots. In this preliminary study, we developed a novel method to estimate isometric forces of individual fingers continuously and concurrently during dexterous finger flexion and extension. Specifically, motor unit (MU) discharge activity was extracted from the surface high-density electromyogram (EMG) signals recorded from the finger extensors and flexors, respectively. The MU information was separated into different groups to be associated with the flexion or extension of individual fingers and was then used to predict individual finger forces during multi-finger flexion and extension tasks. Compared with the conventional EMG amplitude-based method, our method can obtain a better force estimation performance (a higher correlation and a smaller estimation error between the predicted and the measured force) when a linear regression model was used. Further exploration of our method can potentially provide a robust neural-machine interface for intuitive control of robotic hands.

Improving Reliability of Myocontrol Using Formal Verification

In the context of assistive robotics, myocontrol is one of the so-far unsolved problems of upper-limb prosthetics. It consists of swiftly, naturally, and reliably converting biosignals, non-invasively gathered from an upper-limb disabled subject, into control commands for an appropriate self-powered prosthetic device. Despite decades of research, traditional surface electromyography cannot yet detect the subject's intent to an acceptable degree of reliability, that is, enforce an action exactly when the subject wants it to be enforced.. In this paper, we tackle one such kind of mismatch between the subject's intent and the response by the myocontrol system, and show that formal verification can indeed be used to mitigate it. Eighteen intact subjects were engaged in two target achievement control tests in which a standard myocontrol system was compared to two "repaired" ones, one based on a non-formal technique, thus enforcing no guarantee of safety, and the other using the satisfiability modulo theories (SMT) technology to rigorously enforce the desired property. The experimental results indicate that both repaired systems exhibit better reliability than the non-repaired one. The SMT-based system causes only a modest increase in the required computational resources with respect to the non-formal technique; as opposed to this, the non-formal technique can be easily implemented in existing myocontrol systems, potentially increasing their reliability.

Virtual Integration Environment as an Advanced Prosthetic Limb Training Platform

Background: Despite advances in prosthetic development and neurorehabilitation, individuals with upper extremity (UE) loss continue to face functional and psychosocial challenges following amputation. Recent advanced myoelectric prostheses offer intuitive control over multiple, simultaneous degrees of motion and promise sensory feedback integration, but require complex training to effectively manipulate. We explored whether a virtual reality simulator could be used to teach dexterous prosthetic control paradigms to individuals with UE loss. Methods: Thirteen active-duty military personnel with UE loss (14 limbs) completed twenty, 30-min passive motor training sessions over 1-2 months. Participants were asked to follow the motions of a virtual avatar using residual and phantom limbs, and electrical activity from the residual limb was recorded using surface electromyography. Eight participants (nine limbs), also completed twenty, 30-min active motor training sessions. Participants controlled a virtual avatar through three motion sets of increasing complexity (Basic, Advanced, and Digit) and were scored on how accurately they performed requested motions. Score trajectory was assessed as a function of time using longitudinal mixed effects linear regression. Results: Mean classification accuracy for passive motor training was 43.8 ± 10.7% (14 limbs, 277 passive sessions). In active motor sessions, >95% classification accuracy (which we used as the threshold for prosthetic acceptance) was achieved by all participants for Basic sets and by 50% of participants in Advanced and Digit sets. Significant improvement in active motor scores over time was observed in Basic and Advanced sets (per additional session: β-coefficient 0.125, p = 0.022; β-coefficient 0.45, p = 0.001, respectively), and trended toward significance for Digit sets (β-coefficient 0.594, p = 0.077). Conclusions: These results offer robust evidence that a virtual reality training platform can be used to quickly and efficiently train individuals with UE loss to operate advanced prosthetic control paradigms. Participants can be trained to generate muscle contraction patterns in residual limbs that are interpreted with high accuracy by computer software as distinct active motion commands. These results support the potential viability of advanced myoelectric prostheses relying on pattern recognition feedback or similar controls systems.

Clinical Trial of the Virtual Integration Environment to Treat Phantom Limb Pain With Upper Extremity Amputation

Background: Phantom limb pain (PLP) is commonly seen following upper extremity (UE) amputation. Use of both mirror therapy, which utilizes limb reflection in a mirror, and virtual reality therapy, which utilizes computer limb simulation, has been used to relieve PLP. We explored whether the Virtual Integration Environment (VIE), a virtual reality UE simulator, could be used as a therapy device to effectively treat PLP in individuals with UE amputation. Methods: Participants with UE amputation and PLP were recruited at Walter Reed National Military Medical Center (WRNMMC) and instructed to follow the limb movements of a virtual avatar within the VIE system across a series of study sessions. At the end of each session, participants drove virtual avatar limb movements during a period of "free-play" utilizing surface electromyography recordings collected from their residual limbs. PLP and phantom limb sensations were assessed at baseline and following each session using the Visual Analog Scale (VAS) and Short Form McGill Pain Questionnaire (SF-MPQ), respectively. In addition, both measures were used to assess residual limb pain (RLP) at baseline and at each study session. In total, 14 male, active duty military personnel were recruited for the study. Results: Of the 14 individuals recruited to the study, nine reported PLP at the time of screening. Eight of these individuals completed the study, while one withdrew after three sessions and thus is not included in the final analysis. Five of these eight individuals noted RLP at baseline. Participants completed an average of 18, 30-min sessions with the VIE leading to a significant reduction in PLP in seven of the eight (88%) affected limbs and a reduction in RLP in four of the five (80%) affected limbs. The same user reported an increase in PLP and RLP across sessions. All participants who denied RLP at baseline (n = 3) continued to deny RLP at each study session. Conclusions: Success with the VIE system confirms its application as a non-invasive and low-cost therapy option for PLP and phantom limb symptoms for individuals with upper limb loss.

A review of current theories and treatments for phantom limb pain

Following amputation, most amputees still report feeling the missing limb and often describe these feelings as excruciatingly painful. Phantom limb sensations (PLS) are useful while controlling a prosthesis; however, phantom limb pain (PLP) is a debilitating condition that drastically hinders quality of life. Although such experiences have been reported since the early 16th century, the etiology remains unknown. Debate continues regarding the roles of the central and peripheral nervous systems. Currently, the most posited mechanistic theories rely on neuronal network reorganization; however, greater consideration should be given to the role of the dorsal root ganglion within the peripheral nervous system. This Review provides an overview of the proposed mechanistic theories as well as an overview of various treatments for PLP.