Validity and Impact of Methods for Collecting Training Data for Myoelectric Prosthetic Control Algorithms.

Intuitive regression control of prostheses relies on training algorithms to correlate biological recordings to motor intent. The quality of the training dataset is critical to run-time regression performance, but accurately labeling intended hand kinematics after hand amputation is challenging. In this study, we quantified the accuracy and precision of labeling hand kinematics using two common training paradigms: 1) mimic training, where participants mimic predetermined motions of a prosthesis, and 2) mirror training, where participants mirror their contralateral intact hand during synchronized bilateral movements. We first explored this question in healthy non-amputee individuals where the ground-truth kinematics could be readily determined using motion capture. Kinematic data showed that mimic training fails to account for biomechanical coupling and temporal changes in hand posture. Additionally, mirror training exhibited significantly higher accuracy and precision in labeling hand kinematics. These findings suggest that the mirror training approach generates a more faithful, albeit more complex, dataset. Accordingly, mirror training resulted in significantly better offline regression performance when using a large amount of training data and a non-linear neural network. Next, we explored these different training paradigms online, with a cohort of unilateral transradial amputees actively controlling a prosthesis in real-time to complete a functional task. Overall, we found that mirror training resulted in significantly faster task completion speeds and similar subjective workload. These results demonstrate that mirror training can potentially provide more dexterous control through the utilization of task-specific, user-selected training data. Consequently, these findings serve as a valuable guide for the next generation of myoelectric and neuroprostheses leveraging machine learning to provide more dexterous and intuitive control.

Impaired Discrimination of Electrocutaneous Stimulation in the Paretic Hand of Stroke Survivors.

Artificial sensory feedback via electrocutaneous stimulation can be used to assist or rehabilitate stroke survivors with sensory deficits. Conveying the magnitude of tactile stimuli is an important aspect of artificial sensory feedback. Here, we explore how stroke-related sensory deficits impact the ability of electrocutaneous stimulation to convey the magnitude of tactile stimuli. Using classical psychophysical methods, we quantified the threshold of detection and the just-noticeable difference of electrocutaneous stimulation current in five stroke survivors with unilateral sensory deficits. We show significantly greater (40%) stimulation currents are needed for initial perception on the paretic hand compared to the non-paretic hand. We also show significantly greater percent changes in stimulation current (140%) are needed for reliable incremental perception on the paretic hand compared to the non-paretic hand. Lastly, we show little correlation between electrocutaneous discrimination performance and clinical sensory assessments of light-touch and spatial mechanoperception. These findings can help guide the implementation of artificial sensory feedback as an assistive or rehabilitative intervention for individuals experiencing sensory loss after a stroke.Clinical Relevance- Our results can help guide the implementation of electrical stimulation as an assistive or rehabilitative intervention for individuals with sensory loss after stroke.

Delayed Muscle Activity in Stroke Survivors with Upper-Limb Hemiparesis.

Stroke is the leading cause of disability worldwide, and nearly 80% of stroke survivors suffer from upper-limb hemiparesis. Myoelectric exoskeletons can restore dexterity and independence to stroke survivors with upper-limb hemiparesis. However, the ability of patients to dexterously control myoelectric exoskeletons is limited by an incomplete understanding of the electromyographic (EMG) hallmarks of hemiparesis, such as muscle weakness and spasticity. Here we show that stroke survivors with upper-limb hemiparesis suffer from delayed voluntary muscle contraction and delayed muscle relaxation. We quantified the time constants of EMG activity associated with initiating and terminating voluntary hand grasps and extensions for both the paretic and non-paretic hands of stroke survivors. We found that the initiation and termination time constants were greater on the paretic side for both hand grasps and hand extensions. Notably, the initiation time constant during hand extension was approximately three times longer for the paretic hand than for the contralateral non-paretic hand (0.618 vs 0.189 s). We also show a positive correlation between the initiation and termination time constants and clinical scores on the Modified Ashworth Scale. The difficulty stroke survivors have in efficiently modulating their EMG presents a challenge for appropriate control of assistive myoelectric devices, such as exoskeletons. This work constitutes an important step towards understanding EMG differences after stroke and how to accommodate these EMG differences in assistive myoelectric devices. Real-time quantitative biofeedback of EMG time constants may also have broad implications for guiding rehabilitation and monitoring patient recovery.Clinical Relevance- After a stroke, muscle activity changes, and these changes make it difficult to use muscle activity to drive assistive and rehabilitative technologies. We identified slower muscle contraction and muscle relaxation as a key difference in muscle activity after a stroke. This quantifiable difference in muscle activity can be used to develop better assistive technologies, guide rehabilitation, and monitor patient recovery.

Wrist EMG Improves Gesture Classification for Stroke Patients.

Electromyography (EMG) is a popular human-machine interface for hand gesture control of assistive and rehabilitative technology. EMG can be used to estimate motor intent even when an individual cannot physically move due to weakness or paralysis. EMG is traditionally recorded from the extrinsic hand muscles located in the forearm. However, the wrist has become an increasingly attractive recording location for commercial applications as EMG sensors can be integrated into wrist-worn wearables (e.g., watches, bracelets). Here we explored the impact that recording EMG from the wrist, instead of the forearm, has on stroke patients with upper-limb hemiparesis. We show that EMG signal-to-noise ratio is significantly worse at the paretic wrist relative to the paretic forearm and non-paretic wrist. Despite this, we also show that the ability to classify hand gestures from EMG was significantly better at the paretic wrist relative to the paretic forearm. Our results also provide guidance as to the ideal gestures for each recording location. Namely, single-digit gestures appeared easiest to classify from both forearm and wrist EMG on the paretic side. These results suggest commercialization of wrist-worn EMG would benefit stroke patients by providing more accurate EMG control in a more widely adopted wearable formfactor.

Correcting Temporal Inaccuracies in Labeled Training Data for Electromyographic Control Algorithms.

Electromyographic (EMG) control relies on supervised-learning algorithms that correlate EMG to motor intent. The quality of the training dataset is critical to the runtime performance of the algorithm, but labeling motor intent is imprecise and imperfect. Traditional EMG training data is collected while participants mimic predetermined movements of a virtual hand with their own hand. This assumes participants are perfectly synchronized with the predetermined movements, which is unlikely due to reaction time and signal-processing delays. Prior work has used cross-correlation to globally shift and re-align kinematic data and EMG. Here, we quantify the impact of this global re-alignment on both classification algorithms and regression algorithms with and without a human in the loop. We also introduce a novel trial-by-trial re-alignment method to re-align EMG with kinematics on a per-movement basis. We show that EMG and kinematic data are inherently misaligned, and that reaction time is inconsistent throughout data collection. Both global and trial-by-trial re-alignment significantly improved offline performance for classification and regression. Our trial-by-trial re-alignment further improved offline classification performance relative to global realignment. However, online performance, with a human actively in the loop, was no different with or without re-alignment. This work highlights inaccuracies in labeled EMG data and has broad implications for EMG-control applications.

Automated Quantifiable Assessments of Sensorimotor Function Using an Instrumented Fragile Object.

Accurate assessment of hand dexterity plays a critical role in informing rehabilitation and care of upper-limb hemiparetic stroke patients. Common upper-limb assessments, such as the Box and Blocks Test and Nine Hole Peg Test, primarily evaluate gross motor function in terms of speed. These assessments neglect an individual's ability to finely regulate grip force, which is critical in activities of daily living, such as manipulating fragile objects. Here we present the Electronic Grip Gauge (EGG), an instrumented fragile object that assesses both gross and fine motor function. Embedded with a load cell, accelerometer, and Hall-effect sensor, the EGG measures grip force, acceleration, and relative position (via magnetic fields) in real time. The EGG can emit an audible "break" sound when the applied grip force exceeds a threshold. The number of breaks, transfer duration, and applied forces are automatically logged in real-time. Using the EGG, we evaluated sensorimotor function in implicit grasping and gentle grasping for the non-paretic and paretic hands of 3 hemiparetic stroke patients. For all participants, the paretic hand took longer to transfer the EGG during implicit grasping. For 2 of 3 participants, grip forces were significantly greater for the paretic hand during gentle grasping. Differences in implicit grasping forces were unique to each participant. This work constitutes an important step towards more widespread and quantitative measures of sensorimotor function, which may ultimately lead to improved personalized rehabilitation and better patient outcomes.

Intuitive, Myoelectric Control of Adaptive Sports Equipment for Individuals with Tetraplegia.

This research aims to develop safe, robust, and easy-to-use adaptive technology for individuals with tetraplegia. After a debilitating spinal cord injury, clinical care focuses on improving quality of life. Participation in adaptive sports has been shown to improve several aspects of participants' well-being. The TetraSki is a power-assisted ski chair that allows individuals with tetraplegia to participate in downhill skiing by sipping and puffing air on an integrated straw to turn their skis. Here, we introduce a new intuitive and dexterous control strategy for the TetraSki using surface electromyography (sEMG) from the neck and shoulder muscles. As an initial assessment, six healthy participants completed a virtual ski racecourse using sEMG and Sip-and-Puff control. Participants also completed a detection response task of cognitive load and the NASA-TLX survey of subjective workload. No significant differences were observed between the performance of sEMG control and the performance of Sip-and-Puff control. However, sEMG control required significantly less cognitive load and subjective workload than Sip-and-Puff control. These results indicate that sEMG can effectively control the equipment and is significantly more intuitive than traditional Sip-and-Puff control. This suggests that sEMG is a promising control method for further validation with individuals with tetraplegia. Ultimately, long-term use of sEMG control may promote neuroplasticity and drive rehabilitation.

A portable, programmable, multichannel stimulator with high compliance voltage for noninvasive neural stimulation of motor and sensory nerves in humans.

Most neural stimulators do not have a high enough compliance voltage to pass current through the skin. The few stimulators that meet the high compliance voltage necessary for transcutaneous stimulation are typically large benchtop units that are not portable, and the stimulation waveforms cannot be readily customized. To address this, we present the design and validation of a portable, programmable, multichannel, noninvasive neural stimulator that can generate three custom bipolar waveforms at ± 150 V with microsecond temporal resolution. The design is low-cost, open-source, and validated on the benchtop and with a healthy population to demonstrate its functionality for sensory and motor stimulation. Sensory stimulation included electrocutaneous stimulation targeting cutaneous mechanoreceptors at the surface of the skin and transcutaneous nerve stimulation targeting the median nerve at the wrist. Both electrocutaneous stimulation on the hand and transcutaneous stimulation at the wrist can elicit isolated tactile percepts on the hand but changes in pulse frequency are more discriminable for electrocutaneous stimulation. Also, neuromuscular electrical stimulation of the flexor digiti profundus is evoked by applying electrical stimulation directly above the muscle in the forearm and to the median and ulnar nerves in the upper arm. Muscle and nerve stimulation evoked similar grip forces and force rise times, but nerve stimulation had a significantly slower fatigue rate. The development and validation of this noninvasive stimulator and direct comparison of common sensory and motor stimulation targets in a human population constitute an important step towards more widespread use and accessibility of neural stimulation for education and research.

A Multi-User Transradial Functional-Test Socket for Validation of New Myoelectric Prosthetic Control Strategies.

The validation of myoelectric prosthetic control strategies for individuals experiencing upper-limb loss is hindered by the time and cost affiliated with traditional custom-fabricated sockets. Consequently, researchers often rely upon virtual reality or robotic arms to validate novel control strategies, which limits end-user involvement. Prosthetists fabricate diagnostic check sockets to assess and refine socket fit, but these clinical techniques are not readily available to researchers and are not intended to assess functionality for control strategies. Here we present a multi-user, low-cost, transradial, functional-test socket for short-term research use that can be custom-fit and donned rapidly, used in conjunction with various electromyography configurations, and adapted for use with various residual limbs and terminal devices. In this study, participants with upper-limb amputation completed functional tasks in physical and virtual environments both with and without the socket, and they reported on their perceived comfort level over time. The functional-test socket was fabricated prior to participants' arrival, iteratively fitted by the researchers within 10 mins, and donned in under 1 min (excluding electrode placement, which will vary for different use cases). It accommodated multiple individuals and terminal devices and had a total cost of materials under $10 USD. Across all participants, the socket did not significantly impede functional task performance or reduce the electromyography signal-to-noise ratio. The socket was rated as comfortable enough for at least 2 h of use, though it was expectedly perceived as less comfortable than a clinically-prescribed daily-use socket. The development of this multi-user, transradial, functional-test socket constitutes an important step toward increased end-user participation in advanced myoelectric prosthetic research. The socket design has been open-sourced and is available for other researchers.

A Recurrent Neural Network Provides Stable Across-Day Prosthetic Control for a Human Amputee with Implanted Intramuscular Electromyographic Recording Leads.

Upper-limb prosthetic control is often challenging and non-intuitive, leading to up to 50% of prostheses users abandoning their prostheses. Convolutional neural networks (CNN) and recurrent long short-term memory (LSTM) networks have shown promise in extracting high-degree-of-freedom motor intent from myoelectric signals, thereby providing more intuitive and dexterous prosthetic control. An important next consideration for these algorithms is if performance remains stable over multiple days. Here we introduce a new LSTM network and compare its performance to previously established state-of-the-art algorithms-a CNN and a modified Kalman filter (MKF)-in offline analyses using 76 days of intramuscular recordings from one amputee participant collected over 425 calendar days. Specifically, we assessed the robustness of each algorithm over time by training on data from the first (one, five, ten, 30, or 60) days and then testing on myoelectric signals on the last 16 days. Results indicate that training on additional datasets from prior days generally decreases the Root Mean Squared Error (RMSE) of intended and unintended movements for all algorithms. Across all algorithms trained with 60 days of data, the lowest RMSE for unintended movements was achieved with the LSTM. The LSTM also showed less across-day variance in RMSE of unintended movements relative to the other algorithms. Altogether this work suggests that the LSTM algorithm introduced here can provide more intuitive and dexterous control for prosthetic users, and that training on multiple days of data improves overall performance on subsequent days, at least for offline analyses.

A Bionic Hand for Semi-Autonomous Fragile Object Manipulation via Proximity and Pressure Sensors.

Multiarticulate bionic hands are now capable of recreating the endogenous movements and grip patterns of the human hand, yet amputees continue to be dissatisfied with existing control strategies. One approach towards more dexterous and intuitive control is to create a semi-autonomous bionic hand that can synergistically aid a human with complex tasks. To that end, we have developed a bionic hand that can automatically detect and grasp nearby objects with minimal force using multi-modal fingertip sensors. We evaluated performance using a fragile-object task in which participants must move an object over a barrier without applying pressure above specified thresholds. Participants completed the task under three conditions: 1) with their native hand, 2) with the bionic hand using surface electromyography control, and 3) using the semi-autonomous bionic hand. We show that the semi-autonomous hand is extremely capable of completing this dexterous task and significantly outperforms a more traditional surface-electromyography controller. Furthermore, we show that the semi-autonomous bionic hand significantly increased users' grip precision and reduced users' perceived task workload. This work constitutes an important step towards more dexterous and intuitive bionic hands and serves as a foundation for future work on shared human-machine control for intelligent bionic systems.

Robust Torque Predictions From Electromyography Across Multiple Levels of Active Exoskeleton Assistance Despite Non-linear Reorganization of Locomotor Output.

Robotic exoskeletons can assist humans with walking by providing supplemental torque in proportion to the user's joint torque. Electromyographic (EMG) control algorithms can estimate a user's joint torque directly using real-time EMG recordings from the muscles that generate the torque. However, EMG signals change as a result of supplemental torque from an exoskeleton, resulting in unreliable estimates of the user's joint torque during active exoskeleton assistance. Here, we present an EMG control framework for robotic exoskeletons that provides consistent joint torque predictions across varying levels of assistance. Experiments with three healthy human participants showed that using diverse training data (from different levels of assistance) enables robust torque predictions, and that a convolutional neural network (CNN), but not a Kalman filter (KF), can capture the non-linear transformations in EMG due to exoskeleton assistance. With diverse training, the CNN could reliably predict joint torque from EMG during zero, low, medium, and high levels of exoskeleton assistance [root mean squared error (RMSE) below 0.096 N-m/kg]. In contrast, without diverse training, RMSE of the CNN ranged from 0.106 to 0.144 N-m/kg. RMSE of the KF ranged from 0.137 to 0.182 N-m/kg without diverse training, and did not improve with diverse training. When participant time is limited, training data should emphasize the highest levels of assistance first and utilize at least 35 full gait cycles for the CNN. The results presented here constitute an important step toward adaptive and robust human augmentation via robotic exoskeletons. This work also highlights the non-linear reorganization of locomotor output when using assistive exoskeletons; significant reductions in EMG activity were observed for the soleus and gastrocnemius, and a significant increase in EMG activity was observed for the erector spinae. Control algorithms that can accommodate spatiotemporal changes in muscle activity have broad implications for exoskeleton-based assistance and rehabilitation following neuromuscular injury.

Toward automated assessment of mole similarity on dermoscopic images.

Purpose: Current skin cancer detection relies on dermatologists' visual assessments of moles directly or dermoscopically. Our goal is to show that our similarity assessment algorithm on dermoscopic images can perform as well as a dermatologist's assessment. Approach: Given one target mole and two other moles from the same patient, our model determines which mole is more similar to the target mole. Similarity was quantified as the Euclidean distance in a feature space designed to capture mole properties such as size, shape, and color. We tested our model on 18 patients, each of whom had at least five moles, and compared the model assessments of mole similarity with that of three dermatologists. Fleiss' Kappa agreement coefficients and iteration tests were used to evaluate the agreement in similarity assessment among dermatologists and our model. Results: With the selected features of size, entropy (color variation), and cluster prominence (asymmetry), our algorithm's similarity assessments agreed moderately with the similarity assessments of dermatologists. The mean Kappa of 1000 iteration tests was 0.49 ( confidence interval ( CI ) = [ 0.23 , 0.74 ] ) when comparing three dermatologists and our model, which is comparable to the agreement in similarity assessment among the dermatologists themselves (the mean Kappa of 1000 iteration tests for three dermatologists was 0.48, CI = [ 0.19 , 0.77 ] .) By contrast, the mean Kappa was 0.22 ( CI = [ - 0.00 , 0.43 ] ) when comparing the similarity assessments of three dermatologists and random guesses. Conclusions: Our study showed that our image feature-engineering-based algorithm can effectively assess the similarity of moles as dermatologists do. Such a similarity assessment could serve as the foundation for computer-assisted intra-patient evaluation of moles.

Activities of daily living with bionic arm improved by combination training and latching filter in prosthesis control comparison.

BACKGROUND: Advanced prostheses can restore function and improve quality of life for individuals with amputations. Unfortunately, most commercial control strategies do not fully utilize the rich control information from residual nerves and musculature. Continuous decoders can provide more intuitive prosthesis control using multi-channel neural or electromyographic recordings. Three components influence continuous decoder performance: the data used to train the algorithm, the algorithm, and smoothing filters on the algorithm's output. Individual groups often focus on a single decoder, so very few studies compare different decoders using otherwise similar experimental conditions. METHODS: We completed a two-phase, head-to-head comparison of 12 continuous decoders using activities of daily living. In phase one, we compared two training types and a smoothing filter with three algorithms (modified Kalman filter, multi-layer perceptron, and convolutional neural network) in a clothespin relocation task. We compared training types that included only individual digit and wrist movements vs. combination movements (e.g., simultaneous grasp and wrist flexion). We also compared raw vs. nonlinearly smoothed algorithm outputs. In phase two, we compared the three algorithms in fragile egg, zipping, pouring, and folding tasks using the combination training and smoothing found beneficial in phase one. In both phases, we collected objective, performance-based (e.g., success rate), and subjective, user-focused (e.g., preference) measures. RESULTS: Phase one showed that combination training improved prosthesis control accuracy and speed, and that the nonlinear smoothing improved accuracy but generally reduced speed. Phase one importantly showed simultaneous movements were used in the task, and that the modified Kalman filter and multi-layer perceptron predicted more simultaneous movements than the convolutional neural network. In phase two, user-focused metrics favored the convolutional neural network and modified Kalman filter, whereas performance-based metrics were generally similar among all algorithms. CONCLUSIONS: These results confirm that state-of-the-art algorithms, whether linear or nonlinear in nature, functionally benefit from training on more complex data and from output smoothing. These studies will be used to select a decoder for a long-term take-home trial with implanted neuromyoelectric devices. Overall, clinical considerations may favor the mKF as it is similar in performance, faster to train, and computationally less expensive than neural networks.

Discriminability of multiple cutaneous and proprioceptive hand percepts evoked by intraneural stimulation with Utah slanted electrode arrays in human amputees.

BACKGROUND: Electrical stimulation of residual afferent nerve fibers can evoke sensations from a missing limb after amputation, and bionic arms endowed with artificial sensory feedback have been shown to confer functional and psychological benefits. Here we explore the extent to which artificial sensations can be discriminated based on location, quality, and intensity. METHODS: We implanted Utah Slanted Electrode Arrays (USEAs) in the arm nerves of three transradial amputees and delivered electrical stimulation via different electrodes and frequencies to produce sensations on the missing hand with various locations, qualities, and intensities. Participants performed blind discrimination trials to discriminate among these artificial sensations. RESULTS: Participants successfully discriminated cutaneous and proprioceptive sensations ranging in location, quality and intensity. Performance was significantly greater than chance for all discrimination tasks, including discrimination among up to ten different cutaneous location-intensity combinations (15/30 successes, p < 0.0001) and seven different proprioceptive location-intensity combinations (21/40 successes, p < 0.0001). Variations in the site of stimulation within the nerve, via electrode selection, enabled discrimination among up to five locations and qualities (35/35 successes, p < 0.0001). Variations in the stimulation frequency enabled discrimination among four different intensities at the same location (13/20 successes, p < 0.0005). One participant also discriminated among individual stimulation of two different USEA electrodes, simultaneous stimulation on both electrodes, and interleaved stimulation on both electrodes (20/24 successes, p < 0.0001). CONCLUSION: Electrode location, stimulation frequency, and stimulation pattern can be modulated to evoke functionally discriminable sensations with a range of locations, qualities, and intensities. This rich source of artificial sensory feedback may enhance functional performance and embodiment of bionic arms endowed with a sense of touch.

Bilaterally Mirrored Movements Improve the Accuracy and Precision of Training Data for Supervised Learning of Neural or Myoelectric Prosthetic Control.

Intuitive control of prostheses relies on training algorithms to correlate biological recordings to motor intent. The quality of the training dataset is critical to run-time performance, but it is difficult to label hand kinematics accurately after the hand has been amputated. We quantified the accuracy and precision of labeling hand kinematics for two different training approaches: 1) assuming a participant is perfectly mimicking predetermined motions of a prosthesis (mimicked training), and 2) assuming a participant is perfectly mirroring their contralateral hand during identical bilateral movements (mirrored training). We compared these approaches in non-amputee individuals, using an infrared camera to track eight different joint angles of the hands in real-time. Aggregate data revealed that mimicked training does not account for biomechanical coupling or temporal changes in hand posture. Mirrored training was significantly more accurate and precise at labeling hand kinematics. However, when training a modified Kalman filter to estimate motor intent, the mimicked and mirrored training approaches were not significantly different. The results suggest that the mirrored training approach creates a more faithful but more complex dataset. Advanced algorithms, more capable of learning the complex mirrored training dataset, may yield better run-time prosthetic control.

Intensity Discriminability of Electrocutaneous and Intraneural Stimulation Pulse Frequency in Intact Individuals and Amputees.

Electrical stimulation of residual nerves can be used to provide amputees with intuitive sensory feedback. An important aspect of this artificial sensory feedback is the ability to convey the magnitude of tactile stimuli. Using classical psychophysical methods, we quantified the just-noticeable differences for electrocutaneous stimulation pulse frequency in both intact participants and one transradial amputee. For the transradial amputee, we also quantified the just-noticeable difference of intraneural microstimulation pulse frequency via chronically implanted Utah Slanted Electrode Arrays. We demonstrate that intensity discrimination is similar across conditions: intraneural microstimulation of the residual nerves, electrocutaneous stimulation of the reinnervated skin on the residual limb, and electrocutaneous stimulation of intact hands. We also show that intensity discrimination performance is significantly better at lower pulse frequencies than at higher pulse frequencies - a finding that's unique to electrocutaneous and intraneural stimulation and suggests that supplemental sensory cues may be present at lower pulse frequencies. These results can help guide the implementation of artificial sensory feedback for sensorized bionic arms.Clinical Relevance- Intraneural and electrocutaneous artificial sensory feedback are comparable in their ability to convey the magnitude of tactile stimuli via pulse frequency.

Long-term performance of Utah slanted electrode arrays and intramuscular electromyographic leads implanted chronically in human arm nerves and muscles.

OBJECTIVE: We explore the long-term performance and stability of seven percutaneous Utah Slanted Electrode Arrays (USEAs) and intramuscular recording leads (iEMGs) implanted chronically in the residual arm nerves and muscles of three human participants as a means to permanently restore sensorimotor function after transradial amputations. APPROACH: We quantify the number of functional recording and functional stimulating electrodes over time. We also calculate the signal-to-noise ratio (SNR) of USEA and iEMG recordings and quantify the stimulation current necessary to evoke detectable sensory percepts. Furthermore, we quantify the consistency of the sensory modality, receptive field location, and receptive field size of USEA-evoked percepts. MAIN RESULTS: In the most recent subject, involving USEAs with technical improvements, neural recordings persisted for 502 d (entire implant duration) and the number of functional recording electrodes for one USEA increased over time. However, for six out of seven USEAs across the three participants, the number of functional recording electrodes decreased within the first 2 months after implantation. The SNR of neural recordings and electromyographic recordings stayed relatively consistent over time. Sensory percepts were consistently evoked over the span of 14 months, were not significantly different in size, and highlighted the nerves' fascicular organization. The percentage of percepts with consistent modality or consistent receptive field location between sessions (∼1 month apart) varied between 0%-86.2% and 9.1%-100%, respectively. Stimulation thresholds and electrode impedances increased initially but then remained relatively stable over time. SIGNIFICANCE: This work demonstrates improved performance of USEAs, and provides a basis for comparing the longevity and stability of USEAs to that of other neural interfaces. USEAs provide a rich repertoire of neural recordings and sensory percepts. Although their performance still generally declines over time, functionality can persist long-term. Future work should leverage the results presented here to further improve USEA design or to develop adaptive algorithms that can maintain a high level of performance.

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.

Intuitive neuromyoelectric control of a dexterous bionic arm using a modified Kalman filter.

BACKGROUND: Multi-articulate prostheses are capable of performing dexterous hand movements. However, clinically available control strategies fail to provide users with intuitive, independent and proportional control over multiple degrees of freedom (DOFs) in real-time. NEW METHOD: We detail the use of a modified Kalman filter (MKF) to provide intuitive, independent and proportional control over six-DOF prostheses such as the DEKA "LUKE" arm. Input features include neural firing rates recorded from Utah Slanted Electrode Arrays and mean absolute value of intramuscular electromyographic (EMG) recordings. Ad-hoc modifications include thresholds and non-unity gains on the output of a Kalman filter. RESULTS: We demonstrate that both neural and EMG data can be combined effectively. We also highlight that modifications can be optimized to significantly improve performance relative to an unmodified Kalman filter. Thresholds significantly reduced unintended movement and promoted more independent control of the different DOFs. Gains were significantly greater than one and served to ease movement initiation. Optimal modifications can be determined quickly offline and translate to functional improvements online. Using a portable take-home system, participants performed various activities of daily living. COMPARISON WITH EXISTING METHODS: In contrast to pattern recognition, the MKF allows users to continuously modulate their force output, which is critical for fine dexterity. The MKF is also computationally efficient and can be trained in less than five minutes. CONCLUSIONS: The MKF can be used to explore the functional and psychological benefits associated with long-term, at-home control of dexterous prosthetic hands.