Evoking natural thermal perceptions using a thin-film thermoelectric device with high cooling power density and speed.

Multimodal sensory feedback from upper-limb prostheses can increase their function and usability. Here we show that intuitive thermal perceptions during cold-object grasping with a prosthesis can be restored in a phantom hand through targeted nerve stimulation via a wearable thin-film thermoelectric device with high cooling power density and speed. We found that specific regions of the residual limb, when thermally stimulated, elicited thermal sensations in the phantom hand that remained stable beyond 48 weeks. We also found stimulation sites that selectively elicited sensations of temperature, touch or both, depending on whether the stimulation was thermal or mechanical. In closed-loop functional tasks involving the identification of cold objects by amputees and by non-amputee participants, and compared with traditional bulk thermoelectric devices, the wearable thin-film device reliably elicited cooling sensations that were up to 8 times faster and up to 3 times greater in intensity while using half the energy and 1/600th the mass of active thermoelectric material. Wearable thin-film thermoelectric devices may allow for the non-invasive restoration of thermal perceptions during touch.

Monitoring at-home prosthesis control improvements through real-time data logging.

Objective.Validating the ability for advanced prostheses to improve function beyond the laboratory remains a critical step in enabling long-term benefits for prosthetic limb users.Approach.A nine week take-home case study was completed with a single participant with upper limb amputation and osseointegration to better understand how an advanced prosthesis is used during daily activities. The participant was already an expert prosthesis user and used the Modular Prosthetic Limb (MPL) at home during the study. The MPL was controlled using wireless electromyography (EMG) pattern recognition-based movement decoding. Clinical assessments were performed before and after the take-home portion of the study. Data was recorded using an onboard data log in order to measure daily prosthesis usage, sensor data, and EMG data.Main results.The participant's continuous prosthesis usage steadily increased (p= 0.04, max = 5.5 h) over time and over 30% of the total time was spent actively controlling the prosthesis. The duration of prosthesis usage after each pattern recognition training session also increased over time (p= 0.04), resulting in up to 5.4 h of usage before retraining the movement decoding algorithm. Pattern recognition control accuracy improved (1.2% per week,p< 0.001) with a maximum number of ten classes trained at once and the transitions between different degrees of freedom increased as the study progressed, indicating smooth and efficient control of the advanced prosthesis. Variability of decoding accuracy also decreased with prosthesis usage (p< 0.001) and 30% of the time was spent performing a prosthesis movement. During clinical evaluations, Box and Blocks and the Assessment of the Capacity for Myoelectric Control scores increased by 43% and 6.2%, respectively, demonstrating prosthesis functionality and the NASA Task Load Index scores decreased, on average, by 25% across assessments, indicating reduced cognitive workload while using the MPL, over the nine week study.Significance. In this case study, we demonstrate that an onboard system to monitor prosthesis usage enables better understanding of how prostheses are incorporated into daily life. That knowledge can support the long-term goal of completely restoring independence and quality of life to individuals living with upper limb amputation.

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.

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.

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: ß-coefficient 0.125, p = 0.022; ß-coefficient 0.45, p = 0.001, respectively), and trended toward significance for Digit sets (ß-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.

Initial Clinical Evaluation of the Modular Prosthetic Limb.

The Modular Prosthetic Limb (MPL) was examined for its feasibility and usability as an advanced, dexterous upper extremity prosthesis with surface electromyography (sEMG) control in with two individuals with below-elbow amputations. Compared to currently marketed prostheses, the MPL has a greater number of sequential and simultaneous degrees of motion, as well as wrist modularity, haptic feedback, and individual digit control. The MPL was successfully fit to a 33-year-old with a trans-radial amputation (TR01) and a 30-year-old with a wrist disarticulation amputation (TR02). To preserve anatomical limb length, we adjusted the powered degrees of freedom of wrist motion between users. Motor training began with practicing sEMG and pattern recognition control within the virtual integration environment (VIE). Prosthetic training sessions then allowed participants to complete a variety of activities of daily living with the MPL. Training and Motion Control Accuracy scores quantified their ability to consistently train and execute unique muscle-to-motion contraction patterns. Each user also completed one prosthetic functional metric-the Southampton Hand Assessment Procedure (SHAP) for TR01 and the Jebsen-Taylor Hand Function Test (JHFT) for TR02. Haptic feedback capabilities were integrated for TR01. TR01 achieved 95% accuracy at 84% of his VIE sessions. He demonstrated improved scores over a year of prosthetic training sessions, ultimately achieving simultaneous control of 13 of the 17 (76%) attempted motions. His performance on the SHAP improved from baseline to final assessment with an increase in number of tasks achieved. TR01 also used vibrotactile sensors to successfully discriminate between hard and soft objects being grasped by the MPL hand. TR02 demonstrated 95% accuracy at 79% of his VIE sessions. He demonstrated improved scores over months of prosthetic training sessions, however there was a significant drop in scores initially following a mid-study pause in testing. He ultimately achieved simultaneous control of all 13 attempted powered motions, and both attempted passive motions. He completed 5 of the 7 (71%) JHFT tasks within the testing time limit. These case studies confirm that it is possible to use non-invasive motor control to increase functional outcomes with individuals with below-elbow amputation and will help to guide future myoelectric prosthetic studies.

Targeted Transcutaneous Electrical Nerve Stimulation for Phantom Limb Sensory Feedback.

In this work, we investigated the use of noninvasive, targeted transcutaneous electrical nerve stimulation (TENS) of peripheral nerves to provide sensory feedback to two amputees, one with targeted sensory reinnervation (TSR) and one without TSR. A major step in developing a closed-loop prosthesis is providing the sense of touch back to the amputee user. We investigated the effect of targeted nerve stimulation amplitude, pulse width, and frequency on stimulation perception. We discovered that both subjects were able to reliably detect stimulation patterns with pulses less than 1 ms. We utilized the psychophysical results to produce a subject specific stimulation pattern using a leaky integrate and fire (LIF) neuron model from force sensors on a prosthetic hand during a grasping task. For the first time, we show that TENS is able to provide graded sensory feedback at multiple sites in both TSR and non-TSR amputees while using behavioral results to tune a neuromorphic stimulation pattern driven by a force sensor output from a prosthetic hand.

Using a virtual integration environment in treating phantom limb pain.

The Revolutionizing Prosthetics 2009 program conducted by the Defense Advanced Research Projects Agency (DARPA) has resulted in a Virtual Integration Environment (VIE) that provides a common development platform for researchers and clinicians that design, model and build prosthetic limbs and then integrate and test them with patients. One clinical need that arose during the VIE development was a feature to easily create and model animations that represent patient activities of daily living (ADLs) and simultaneously capture real-time surface EMG activity from the residual limb corresponding to the ADLs. An application of this feature is being made by the Walter Reed Military Amputee Research Program (MARP) where they are utilizing the VIE to investigate methods of reducing upper extremity amputee phantom limb pain (PLP).