Jacqueline S. Hebert

Novel targeted sensory reinnervation technique to restore functional hand sensation after transhumeral amputation.

We present a case study of a novel variation of the targeted sensory reinnervation technique that provides additional control over sensory restoration after transhumeral amputation. The use of intraoperative somatosensory evoked potentials on individual fascicles of the median and ulnar nerves allowed us to specifically target sensory fascicles to reroute to target cutaneous nerves at a distance away from anticipated motor sites in a transhumeral amputee. This resulted in restored hand maps of the median and ulnar nerve in discrete spatially separated areas. In addition, the subject was able to use native and reinnervated muscle sites to control a robotic arm while simultaneously sensing touch and force feedback from the robotic gripper in a physiologically correct manner. This proof of principle study is the first to demonstrate the ability to have simultaneous dual flow of information (motor and sensory) within the residual limb. In working towards clinical deployment of a sensory integrated prosthetic device, this surgical method addresses the important issue of restoring a usable access point to provide natural hand sensation after upper limb amputation.

Applications of sensory feedback in motorized upper extremity prosthesis: a review

Dexterous hand movement is possible due to closed loop control dependent on efferent motor output and afferent sensory feedback. This control strategy is significantly altered in those with upper limb amputation as sensations of touch and movement are inherently lost. For upper limb prosthetic users, the absence of sensory feedback impedes efficient use of the prosthesis and is highlighted as a major factor contributing to user rejection of myoelectric prostheses. Numerous sensory feedback systems have been proposed in literature to address this gap in prosthetic control; however, these systems have yet to be implemented for long term use. Methodologies for communicating prosthetic grasp and touch information are reviewed, including discussion of selected designs and test results. With a focus on clinical and translational challenges, this review highlights and compares techniques employed to provide amputees with sensory feedback. Additionally, promising future directions are discussed and highlighted.

Adaptive artificial limbs: a real-time approach to prediction and anticipation

Predicting the future has long been regarded as a powerful means to improvement and success. The ability to make accurate and timely predictions enhances our ability to control our situation and our environment. Assistive robotics is one prominent area in which foresight of this kind can bring improved quality of life. In this article, we present a new approach to acquiring and maintaining predictive knowledge during the online ongoing operation of an assistive robot. The ability to learn accurate, temporally abstracted predictions is shown through two case studies: 1) able-bodied myoelectric control of a robot arm and 2) an amputees interactions with a myoelectric training robot. To our knowledge, this research is the first demonstration of a practical method for real-time prediction learning during myoelectric control. Our approach therefore represents a fundamental tool for addressing one major unsolved problem: amputee-specific adaptation during the ongoing operation of a prosthetic device. The findings in this article also contribute a first explicit look at prediction learning in prosthetics as an important goal in its own right, independent of its intended use within a specific controller or system. Our results suggest that real-time learning of predictions and anticipations is a significant step toward more intuitive myoelectric prostheses and other assistive robotic devices.

Case report of modified Box and Blocks test with motion capture to measure prosthetic function

This case study report demonstrates the use of motion analysis with a modification of the Box and Blocks test. The goal was to quantify observed improvements in compensatory movements and simultaneous control in a subject using different prostheses before and after targeted muscle reinnervation (TMR) surgery. This is a single case study with data collection using a body-powered prosthesis pre-TMR surgery and 6 mo postfitting with a TMR myoelectric prosthesis. The Box and Blocks test was modified for cyclical motion within a motion capture laboratory. With the TMR myoelectric prosthesis, the subject was able to simultaneously activate the hand and elbow. Task performance was slower, but there was improved elbow flexion and less trunk compensatory motion than with the body-powered prosthesis. There are several limitations to the case study because there is no direct comparison of myoelectric performance before and after TMR surgery; however, the current report presents a potential method to quantify quality of motion and compensatory movements of prosthetic users. With further study, this test procedure has the potential to be a useful outcome measure for future standardized assessments of upper-limb prosthetic function.

Application of real-time machine learning to myoelectric prosthesis control: A case series in adaptive switching

Background: Myoelectric prostheses currently used by amputees can be difficult to control. Machine learning, and in particular learned predictions about user intent, could help to reduce the time and cognitive load required by amputees while operating their prosthetic device. Objectives: The goal of this study was to compare two switching-based methods of controlling a myoelectric arm: non-adaptive (or conventional) control and adaptive control (involving real-time prediction learning). Study design: Case series study. Methods: We compared non-adaptive and adaptive control in two different experiments. In the first, one amputee and one non-amputee subject controlled a robotic arm to perform a simple task; in the second, three able-bodied subjects controlled a robotic arm to perform a more complex task. For both tasks, we calculated the mean time and total number of switches between robotic arm functions over three trials. Results: Adaptive control significantly decreased the number of switches and total switching time for both tasks compared with the conventional control method. Conclusion: Real-time prediction learning was successfully used to improve the control interface of a myoelectric robotic arm during uninterrupted use by an amputee subject and able-bodied subjects. Clinical relevance Adaptive control using real-time prediction learning has the potential to help decrease both the time and the cognitive load required by amputees in real-world functional situations when using myoelectric prostheses.

Fixed and self-paced treadmill walking for able-bodied and transtibial amputees in a multi-terrain virtual environment

A self-paced treadmill automatically adjusts speed in real-time to match the users walking speed, potentially enabling more natural gait than fixed-speed treadmills. This research examined walking speed changes for able-bodied and transtibial amputee populations on a self-paced treadmill in a multi-terrain virtual environment and examined gait differences between fixed and self-paced treadmill speed conditions. Twelve able-bodied (AB) individuals and 12 individuals with unilateral transtibial amputation (TT) walked in a park-like virtual environment with level, slopes, and simulated uneven terrain scenarios. Temporal-spatial and range-of-motion parameters were analyzed. Within the self-paced condition, all participants significantly varied walking speed (p<0.001) across different walking activities. Compared to level walking, participants reduced speed for uphill and hilly activities (p<0.001). TT also reduced speed downhill (p<0.001). Generally, differences in temporal-spatial and range-of-motion parameters between fixed and self-paced speed conditions were no longer significantly different with a speed covariate. However, for uphill walking, both groups decreased stride length during self-paced trials, and increased stride length during fixed-speed trials to maintain the constant speed (p<0.01). The results from this study demonstrated self-paced treadmill mode is important for virtual reality systems with multiple movement scenarios in order to elicit more natural gait across various terrain. Fixed-speed treadmills may induce gait compensations to maintain the fixed speed.

The effect of biomechanical variables on force sensitive resistor error: Implications for calibration and improved accuracy.

Force Sensitive Resistors (FSRs) are commercially available thin film polymer sensors commonly employed in a multitude of biomechanical measurement environments. Reasons for such wide spread usage lie in the versatility, small profile, and low cost of these sensors. Yet FSRs have limitations. It is commonly accepted that temperature, curvature and biological tissue compliance may impact sensor conductance and resulting force readings. The effect of these variables and degree to which they interact has yet to be comprehensively investigated and quantified. This work systematically assesses varying levels of temperature, sensor curvature and surface compliance using a full factorial design-of-experiments approach. Three models of Interlink FSRs were evaluated. Calibration equations under 12 unique combinations of temperature, curvature and compliance were determined for each sensor. Root mean squared error, mean absolute error, and maximum error were quantified as measures of the impact these thermo/mechanical factors have on sensor performance. It was found that all three variables have the potential to affect FSR calibration curves. The FSR model and corresponding sensor geometry are sensitive to these three mechanical factors at varying levels. Experimental results suggest that reducing sensor error requires calibration of each sensor in an environment as close to its intended use as possible and if multiple FSRs are used in a system, they must be calibrated independently.

Normative data for modified Box and Blocks test measuring upper-limb function via motion capture.

Motion analysis is an important tool for examining upper-limb function. Based on previous work demonstrating a modified Box and Blocks (BB) test with motion capture to assess prosthetic performance, we collected data in 16 nondisabled participants to establish normative kinematics for this test. Four motions of the modified BB test were analyzed to establish kinematic data for upper-limb and trunk motion. The test was repeated for right and left arms in standing and seated positions. Data were compared using a nonparametric Friedman test. No differences were found between right- and left-hand performance other than for task completion time. Small but significant differences were found for standing and seated performance, with slightly greater ranges in standing for axial trunk rotation, medial-lateral sternum displacement, and anterior-posterior hand displacement. The kinematic trajectories, however, were very consistent. The consistency in our nondisabled data suggests that normative kinematic trajectories can be defined for this task. This motion capture procedure may add to the understanding of movement in upper-limb impairment and may be useful for measuring the effect of interventions to improve upper-limb function.

Updates in Targeted Sensory Reinnervation for Upper Limb Amputation

Advanced robotic devices capable of simulating the dexterous ability of the upper limb with an array of internal sensors have raised the enticing prospect of replacing the lost intricate functions of the arm following upper limb amputation. However, a large gap still exists in the application of this technology to the human user. In particular, the ability to provide physiologically relevant sensory feedback—to have the amputee feel the prosthetic hand as their own—has not yet been achieved. Although a number of different approaches are being investigated, targeted sensory reinnervation, a refinement of the original targeted muscle reinnervation procedure, is the most recent and promising development in the effort to create a functional human–machine interface with a closed loop sensory feedback system. This technique aims to re-establish hand sensation on the skin so that it can be readily accessed non-invasively during functional tasks. Recent efforts are being directed towards distributing hand maps widely on the stump without interference of sensations from the native area. In this article, we will review the surgical approaches that have been used for sensory reinnervation in upper arm amputation and compare the resultant outcomes and potential functional utility of the techniques.

Cutaneous sensory outcomes from three transhumeral targeted reinnervation cases

Background: Although targeted muscle reinnervation has been shown to be effective in enhancing prosthetic control for upper limb amputees, restored hand sensations have been variable. An understanding of possible sensory feedback channels is crucial in working toward more effective closed-loop prosthetic control. Objectives: To compare sensory outcomes of different targeted sensory reinnervation approaches. Study design: Case series, cross-sectional, and retrospective. Methods: Three transhumeral amputees that had undergone different sensory reinnervation approaches were recruited. Skin pressure sensitivity thresholds and anatomic sensory mapping were performed using Semmes-Weinstein monofilaments. The clinical charts of the subjects were reviewed to compare the sensory maps performed during the earlier post-reinnervation period. Results: While the first two subjects achieved return of hand sensations on the stump skin in early follow-up, the maps showed attenuation over time. The last subject developed discrete sensations of all digits in the recipient cutaneous nerve territories away from the reinnervated muscles. Conclusions: These findings confirm that it is feasible to restore hand sensation after transhumeral targeted reinnervation, but there is a significant intersubject variability. The intrafascicular approach may be particularly effective in restoring digit sensation and deserves further exploration, as do factors affecting stability of the hand maps over time. Clinical relevance In addition to enabling intuitive motor control of myoelectric prosthesis, targeted reinnervation can also result in sensory restoration of the hand. Documentation of sensory mapping present after reinnervation may assist with exploring future techniques for sensory enhancement, with the goal of working toward closed-loop prosthetic control.