Towards clinically viable neuromuscular control of bone-anchored prosthetic arms with sensory feedback

Promising developments are currently ongoing worldwide in the field of neuroprosthetics and artificial limb control. It is now possible to chronically connect a robotic limb to bone, nerves, and muscles of a human being, and to use the signals sourced from these connections to enable movements of the artificial limb. It is also possible to surgically redirect a nerve, deprived from its original target muscle due to amputation, to a new target in order to restore the original motor functionality. Intelligent signal processing algorithms can now utilize the bioelectric signals gathered from remaining muscles on the stump to decode the motor intention of the amputee, providing an intuitive control interface. Unfortunately, clinical implementations still lag behind the advancements made in research, and the conventional solutions for amputees have remained largely unchanged for decades. More efforts are needed from researchers to close the gap between scientific developments and clinical practices.This thesis ultimately focuses on the intuitive control of a prosthetic upper limb. In the first part of this doctoral project, an embedded system capable of prosthetic control via the processing of bioelectric signals and pattern recognition algorithms was developed. The design included a neurostimulator to provide direct neural feedback modulated by sensory information from artificial sensors. The system was designed towards clinical implementation and its functionality was proven by its use by amputee subjects in daily life. This system was then used during the second part of the doctoral project as a research platform to monitor prosthesis usage and training, machine learning based control algorithms, and neural stimulation paradigms for tactile sensory feedback. Within this work, a novel method for interfacing a multi-grip prosthetic hand to facilitate posture selection via pattern recognition was proposed. Moreover, the need for tactile sensory feedback was investigated in order to restore natural grasping behavior in amputees. Notably, the benefit for motor coordination of somatotopic tactile feedback achieved via direct neural stimulation was demonstrated. The findings and the technology developed during this project open to the clinical use of a new class of prosthetic arms that are directly connected to the neuromusculoskeletal system, intuitively controlled and capable of tactile sensory feedback

Stationary wavelet processing and data imputing in myoelectric pattern recognition on a low-cost embedded system

Pattern recognition-based decoding of surface electromyography allows for intuitive and flexible control of prostheses but comes at the cost of sensitivity to in-band noise and sensor faults. System robustness can be improved with wavelet-based signal processing and data imputing, but no attempt has been made to implement such algorithms on real-time, portable systems. The aim of this work was to investigate the feasibility of low-latency, wavelet-based processing and data imputing on an embedded device capable of controlling upper-arm prostheses. Nine able-bodied subjects performed Motion Tests while inducing transient disturbances. Additional investigation was performed on pre-recorded Motion Tests from 15 able-bodied subjects with simulated disturbances. Results from real-time tests were inconclusive, likely due to the low number of disturbance episodes, but simulated tests showed significant improvements in most metrics for both algorithms. However, both algorithms also showed reduced responsiveness during disturbance episodes. These results suggest wavelet-based processing and data imputing can be implemented in portable, real-time systems to potentially improve robustness to signal distortion in prosthetic devices with the caveat of reduced responsiveness for the typically short duration of signal disturbances. The trade-off between large-scale signal corruption robustness and system responsiveness warrants further studies in daily life activities

Cross-Channel Impedance Measurement for Monitoring Implanted Electrodes

Implanted electrodes, such as those used for cochlear implants, brain-computer interfaces, and prosthetic limbs, rely on particular electrical conditions for optimal operation. Measurements of electrical impedance can be a diagnostic tool to monitor implanted electrodes for changing conditions arising from glial scarring, encapsulation, and shorted or broken wires. Such measurements provide information about the electrical impedance between a single electrode and its electrical reference, but offer no insights into the overall network of impedances between electrodes. Other solutions generally rely on geometrical assumptions of the arrangement of the electrodes and may not generalize to other electrode networks. Here, we propose a linear algebra-based approach, Cross-Channel Impedance Measurement (CCIM), for measuring a network of impedances between electrodes which all share a common electrical reference. This is accomplished by measuring the voltage response from all electrodes to a known current applied between each electrode and the shared reference, and is agnostic to the number and arrangement of electrodes. The approach is validated using a simulated 8-electrode network, demonstrating direct impedance measurements between electrodes and the reference with 96.6% \ub10.2% accuracy, and cross-channel impedance measurements with 93.3% \ub10.6% accuracy in a typical system. Subsequent analyses on randomized systems demonstrate the sensitivity of the model to impedance range and measurement noise. Clinical Relevance- CCIM provides a system-agnostic diagnostic test for implanted electrode networks, which may aid in the longitudinal tracking of electrode performance and early identification of electronics failures

Extra-neural signals from severed nerves enable intrinsic hand movements in transhumeral amputations

Robotic prostheses controlled by myoelectric signals can restore limited but important hand function in individuals with upper limb amputation. The lack of individual finger control highlights the yet insurmountable gap to fully replacing a biological hand. Implanted electrodes around severed nerves have been used to elicit sensations perceived as arising from the missing limb, but using such extra-neural electrodes to record motor signals that allow for the decoding of phantom movements has remained elusive. Here, we showed the feasibility of using signals from non-penetrating neural electrodes to decode intrinsic hand and finger movements in individuals with above-elbow amputations. We found that information recorded with extra-neural electrodes alone was enough to decode phantom hand and individual finger movements, and as expected, the addition of myoelectric signals reduced classification errors both in offline and in real-time decoding

Chronic Use of a Sensitized Bionic Hand Does Not Remap the Sense of Touch

Electrical stimulation of tactile nerve fibers can be used to restore touch through a bionic hand. Ortiz-Catalan et al. show that a mismatch between the location of the sensor on the bionic hand and the tactile experience is not resolved after long-term prosthesis use

Patterned Stimulation of Peripheral Nerves Produces Natural Sensations With Regards to Location but Not Quality

Sensory feedback is crucial for dexterous manipulation and sense of ownership. Electrical stimulation of severed afferent fibers due to an amputation elicits referred sensations in the missing limb. However, these sensations are commonly reported with a concurrent “electric” or “tingling” character (paresthesia). In this paper, we examined the effect of modulating different pulse parameters on the quality of perceived sensations. Three subjects with above-elbow amputation were implanted with cuff electrodes and stimulated with a train of pulses modulated in either amplitude, width, or frequency (“patterned stimulation”). Pulses were shaped using a slower carrier wave or via quasi-random generation. Subjects were asked to evaluate the natural quality of the resulting sensations using a numeric rating scale. We found that the location of the percepts was distally referred and somatotopically congruent, but their quality remained largely perceived as artificial despite employing patterned modulation. Sensations perceived as arising from the missing limb are intuitive and natural with respect to their location and, therefore, useful for functional restoration. However, our results indicate that sensory transformation from paresthesia to natural qualia seems to require more than patterned stimulation

Decoding of Multiple Wrist and Hand Movements Using a Transient EMG Classifier

: The design of prosthetic controllers by means of neurophysiological signals still poses a crucial challenge to bioengineers. State of the art of electromyographic (EMG) continuous pattern recognition controllers rely on the questionable assumption that repeated muscular contractions produce repeatable patterns of steady-state EMG signals. Conversely, we propose an algorithm that decodes wrist and hand movements by processing the signals that immediately follow the onset of contraction (i.e., the transient EMG). We collected EMG data from the forearms of 14 non-amputee and 5 transradial amputee participants while they performed wrist flexion/extension, pronation/supination, and four hand grasps (power, lateral, bi-digital, open). We firstly identified the combination of wrist and hand movements that yielded the best control performance for the same participant (intra-subject classification). Then, we assessed the ability of our algorithm to classify participant data that were not included in the training set (cross-subject classification). Our controller achieved a median accuracy of ~96% with non-amputees, while it achieved heterogeneous outcomes with amputees, with a median accuracy of ~89%. Importantly, for each amputee, it produced at least one acceptable combination of wrist-hand movements (i.e., with accuracy >85%). Regarding the cross-subject classifier, while our algorithm obtained promising results with non-amputees (accuracy up to ~80%), they were not as good with amputees (accuracy up to ~35%), possibly suggesting further assessments with domain-adaptation strategies. In general, our offline outcomes, together with a preliminary online assessment, support the hypothesis that the transient EMG decoding could represent a viable pattern recognition strategy, encouraging further online assessments

Self-contained neuromusculoskeletal arm prostheses

We report the use of a bone-anchored, self-contained robotic arm with both sensory and motor components over 3 to 7 years in four patients after transhumeral amputation. The implant allowed for bidirectional communication between a prosthetic hand and electrodes implanted in the nerves and muscles of the upper arm and was anchored to the humerus through osseointegration, the process in which bone cells attach to an artificial surface without formation of fibrous tissue. Use of the device did not require formal training and depended on the intuitive intent of the user to activate movement and sensory feedback from the prosthesis. Daily use resulted in increasing sensory acuity and effectiveness in work and other activities of daily life

Competitive motivation increased home use and improved prosthesis self-perception after Cybathlon 2020 for neuromusculoskeletal prosthesis user

Background Assistive technologies, such as arm prostheses, are intended to improve the quality of life of individuals with physical disabilities. However, certain training and learning is usually required from the user to make these technologies more effective. Moreover, some people can be encouraged to train more through competitive motivation. Methods In this study, we investigated if the training for and participation in a competitive event (Cybathlon 2020) could promote behavioral changes in an individual with upper limb amputation (the pilot). We defined behavioral changes as the active time while his prosthesis was actuated, ratio of opposing and simultaneous movements, and the pilot\u27s ability to finely modulate his movement speeds. The investigation was based on extensive home-use data from the period before, during and after the Cybathlon 2020 competition. Results Relevant behavioral changes were found from both quantitative and qualitative analyses. The pilot\u27s home use of his prosthesis nearly doubled in the period before the Cybathlon, and remained 66% higher than baseline after the competition. Moreover, he improved his speed modulation when controlling his prosthesis, and he learned and routinely operated new movements in the prosthesis (wrist rotation) at home. Additionally, as confirmed by semi-structured interviews, his self-perception of the prosthetic arm and its functionality also improved. Conclusions An event like the Cybathlon may indeed promote behavioral changes in how competitive individuals with amputation use their prostheses. Provided that the prosthesis is suitable in terms of form and function for both competition and at-home daily use, daily activities can become opportunities for training, which in turn can improve prosthesis function and create further opportunities for daily use. Moreover, these changes appeared to remain even well after the event, albeit relevant only for individuals who continue using the technology employed in the competition

Online Classification of Transient EMG Patterns for the Control of the Wrist and Hand in a Transradial Prosthesis

Decoding human motor intentions by processing electrophysiological signals is a crucial, yet unsolved, challenge for the development of effective upper limb prostheses. Pattern recognition of continuous myoelectric (EMG) signals represents the state-of-art for multi-DoF prosthesis control. However, this approach relies on the unreliable assumption that repeatable muscular contractions produce repeatable patterns of steady-state EMGs. Here, we propose an approach for decoding wrist and hand movements by processing the signals associated with the onset of contraction (transient EMG). Specifically, we extend the concept of a transient EMG controller for the control of both wrist and hand, and tested it online. We assessed it with one transradial amputee and 15 non-amputees via the Target Achievement Control test. Non-amputees successfully completed 95% of the trials with a median completion time of 17 seconds, showing a significant learning trend (p < 0.001). The transradial amputee completed about the 80% of the trials with a median completion time of 26 seconds. Although the performance proved comparable with earlier studies, the long completion times suggest that the current controller is not yet clinically viable. However, taken collectively, our outcomes reinforce earlier hypothesis that the transient EMG could represent a viable alternative to steady-state pattern recognition approaches