Refined clothespin relocation test and assessment of motion

Electromyographically controlled prosthetic wrist improves dexterity and reduces compensatory movements without added cognitive load

Wrist function is a top priority for transradial amputees. However, the combined functional, biomechanical, and cognitive impact of using a powered prosthetic wrist is unclear. Here, we quantify task performance, compensatory movements, and cognitive load while three transradial amputees performed a modified Clothespin Relocation Task using two myoelectric prostheses with and without the wrists. The two myoelectric prostheses include a commercial prosthesis with a built-in powered wrist, and a newly developed inexpensive prosthetic wrist for research purposes, called the "Utah wrist", that can be adapted to work with various sockets and prostheses. For these three participants, task failure rate decreased significantly from 66% ± 12% without the wrist to 39% ± 9% with the Utah wrist. Compensatory forward leaning movements also decreased significantly, from 24.2° ± 2.5 without the wrist to 12.6° ± 1.0 with the Utah wrist, and from 23.6° ± 7.6 to 15.3° ± 7.2 with the commercial prosthesis with an integrated wrist. Compensatory leftward bending movements also significantly decreased, from 20.8° ± 8.6 to 12.3° ± 5.3, for the commercial with an integrated wrist. Importantly, simultaneous myoelectric control of either prosthetic wrist had no significant impact on cognitive load, as assessed by the NASA Task Load Index survey and a secondary detection response task. This work suggests that functional prosthetic wrists can improve dexterity and reduce compensation without significantly increasing cognitive effort. These results, and the introduction of a new inexpensive prosthetic wrist for research purposes, can aid future research and development and guide the prescription of upper-limb prostheses.

Discrete-Target Prosthesis Control Using Uncertainty-Aware Classification for Smooth and Efficient Gross Arm Movement

Current control approaches for gross prosthetic arm movement mainly regulate movement over a continuous range of target poses. However, these methods suffer from output fluctuation caused by input signal variations during gross arm movements. Prosthesis control approaches with a finite number of discrete target poses can address this issue and reduce the complexity of the pose control process. However, it remains under-explored in the literature and suffers from the consequences of misclassifying the target poses. Here, we propose a novel Uncertainty-Aware Discrete-Target Prosthesis Control (UA-DPC) approach. This approach consists of (1) an uncertainty-aware classification scheme to reduce unintended pose switches caused by misclassifications, and (2) real-time trajectory planning that adjusts motion to be rapid or conservative based on low or high quantified uncertainty, respectively. By addressing the impact of misclassification, this approach facilitates more efficient and smooth movements. Human-in-the-loop experiments were conducted in a virtual reality environment with 12 non-disabled participants. The participants controlled a transhumeral prosthesis using three approaches: the proposed UA-DPC, a discrete-target approach based on a traditional off-the-shelf classifier, and a continuous-target approach. The results demonstrate the superior performance of UA-DPC, which provides more efficient task completion with fewer misclassification instances as well as smoother residual limb and prosthesis movement.

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.

Intuitive control of additional prosthetic joints via electro-neuromuscular constructs improves functional and disability outcomes during home use-a case study

Objective.The advent of surgical reconstruction techniques has enabled the recreation of myoelectric controls sites that were previously lost due to amputation. This advancement is particularly beneficial for individuals with higher-level arm amputations, who were previously constrained to using a single degree of freedom (DoF) myoelectric prostheses due to the limited number of available muscles from which control signals could be extracted. In this study, we explore the use of surgically created electro-neuromuscular constructs to intuitively control multiple bionic joints during daily life with a participant who was implanted with a neuromusculoskeletal prosthetic interface.Approach.We sequentially increased the number of controlled joints, starting at a single DoF allowing to open and close the hand, subsequently adding control of the wrist (2 DoF) and elbow (3 DoF).Main results.We found that the surgically created electro-neuromuscular constructs allow for intuitive simultaneous and proportional control of up to three degrees of freedom using direct control. Extended home-use and the additional bionic joints resulted in improved prosthesis functionality and disability outcomes.Significance.Our findings indicate that electro-neuromuscular constructs can aid in restoring lost functionality and thereby support a person who lost their arm in daily-life tasks.

A multifaceted suite of metrics for comparative myoelectric prosthesis controller research

Upper limb robotic (myoelectric) prostheses are technologically advanced, but challenging to use. In response, substantial research is being done to develop person-specific prosthesis controllers that can predict a user's intended movements. Most studies that test and compare new controllers rely on simple assessment measures such as task scores (e.g., number of objects moved across a barrier) or duration-based measures (e.g., overall task completion time). These assessment measures, however, fail to capture valuable details about: the quality of device arm movements; whether these movements match users' intentions; the timing of specific wrist and hand control functions; and users' opinions regarding overall device reliability and controller training requirements. In this work, we present a comprehensive and novel suite of myoelectric prosthesis control evaluation metrics that better facilitates analysis of device movement details-spanning measures of task performance, control characteristics, and user experience. As a case example of their use and research viability, we applied these metrics in real-time control experimentation. Here, eight participants without upper limb impairment compared device control offered by a deep learning-based controller (recurrent convolutional neural network-based classification with transfer learning, or RCNN-TL) to that of a commonly used controller (linear discriminant analysis, or LDA). The participants wore a simulated prosthesis and performed complex functional tasks across multiple limb positions. Analysis resulting from our suite of metrics identified 16 instances of a user-facing problem known as the "limb position effect". We determined that RCNN-TL performed the same as or significantly better than LDA in four such problem instances. We also confirmed that transfer learning can minimize user training burden. Overall, this study contributes a multifaceted new suite of control evaluation metrics, along with a guide to their application, for use in research and testing of myoelectric controllers today, and potentially for use in broader rehabilitation technologies of the future.

A Comparison of Myoelectric Control Modes for an Assistive Robotic Virtual Platform

In this paper, we propose a daily living situation where objects in a kitchen can be grasped and stored in specific containers using a virtual robot arm operated by different myoelectric control modes. The main goal of this study is to prove the feasibility of providing virtual environments controlled through surface electromyography that can be used for the future training of people using prosthetics or with upper limb motor impairments. We propose that simple control algorithms can be a more natural and robust way to interact with prostheses and assistive robotics in general than complex multipurpose machine learning approaches. Additionally, we discuss the advantages and disadvantages of adding intelligence to the setup to automatically assist grasping activities. The results show very good performance across all participants who share similar opinions regarding the execution of each of the proposed control modes.

Current status and clinical perspectives of extended reality for myoelectric prostheses: review

Training with "Extended Reality" or X-Reality (XR) systems can undoubtedly enhance the control of the myoelectric prostheses. However, there is no consensus on which factors improve the efficiency of skill transfer from virtual training to actual prosthesis abilities. This review examines the current status and clinical applications of XR in the field of myoelectric prosthesis training and analyses possible influences on skill migration. We have conducted a thorough search on databases in the field of prostheses using keywords such as extended reality, virtual reality and serious gaming. Our scoping review encompassed relevant applications, control methods, performance evaluation and assessment metrics. Our findings indicate that the implementation of XR technology for myoelectric rehabilitative training on prostheses provides considerable benefits. Additionally, there are numerous standardised methods available for evaluating training effectiveness. Recently, there has been a surge in the number of XR-based training tools for myoelectric prostheses, with an emphasis on user engagement and virtual training evaluation. Insufficient attention has been paid to significant limitations in the behaviour, functionality, and usage patterns of XR and myoelectric prostheses, potentially obstructing the transfer of skills and prospects for clinical application. Improvements are recommended in four critical areas: activities of daily living, training strategies, feedback, and the alignment of the virtual environment with the physical devices.

Training prosthesis users to switch between modes of a multi-articulating prosthetic hand

PURPOSE

Producing triggers to switch between modes of myoelectric prosthetic hands has proven to be difficult. We evaluated whether digital training methods were feasible in individuals with an upper limb defect (ULD), whether myosignals in these individuals differ from those of non-impaired individuals and whether acquired skills transfer to prosthesis use.

MATERIALS AND METHODS

Two groups participated in a 9-day pre-test-post-test design study with seven 45-minute training sessions. One group trained using a serious game, the other with their myosignals digitally displayed. Both groups also trained using a prosthesis. The pre- and post-tests consisted of an adapted Clothespin Relocation Test and the spherical subset of the Southampton Hand Assessment Procedure. After the post-test, the System Usability Scale (SUS) was administered. Clinically relevant performance measures and myosignal features were analysed.

RESULTS

Four individuals with a ULD participated. SUS-scores deemed both training methods feasible. Three participants produced only a few correct triggers. Myosignals features indicated larger variability for individuals with a ULD compared to non-impaired individuals (previously published data [1]). Three participants indicated transfer of skill.

CONCLUSIONS

Even though both training methods were deemed feasible and most participants showed transfer, seven training sessions were insufficient to learn reliable switching behaviour.Trial registration: The study was approved by the medical ethics committee of the University Medical Center Groningen (METc 2018.268).Implications for rehabilitationSwitching between pre-programmed modes of a myoelectric prosthetic hand can be learned, however it does require training.Serious games can be considered useful training tools for trigger production in early phases of myoelectric prosthesis control training.In order to evoke transfer of skill from training to daily life both task-specificity and focus of attention during training should be taken into account.

Review and critique of current testing protocols for upper-limb prostheses: a call for standardization amidst rapid technological advancements

This article provides a comprehensive narrative review of physical task-based assessments used to evaluate the multi-grasp dexterity and functional impact of varying control systems in pediatric and adult upper-limb prostheses. Our search returned 1,442 research articles from online databases, of which 25 tests-selected for their scientific rigor, evaluation metrics, and psychometric properties-met our review criteria. We observed that despite significant advancements in the mechatronics of upper-limb prostheses, these 25 assessments are the only validated evaluation methods that have emerged since the first measure in 1948. This not only underscores the lack of a consistently updated, standardized assessment protocol for new innovations, but also reveals an unsettling trend: as technology outpaces standardized evaluation measures, developers will often support their novel devices through custom, study-specific tests. These boutique assessments can potentially introduce bias and jeopardize validity. Furthermore, our analysis revealed that current validated evaluation methods often overlook the influence of competing interests on test success. Clinical settings and research laboratories differ in their time constraints, access to specialized equipment, and testing objectives, all of which significantly influence assessment selection and consistent use. Therefore, we propose a dual testing approach to address the varied demands of these distinct environments. Additionally, we found that almost all existing task-based assessments lack an integrated mechanism for collecting patient feedback, which we assert is essential for a holistic evaluation of upper-limb prostheses. Our review underscores the pressing need for a standardized evaluation protocol capable of objectively assessing the rapidly advancing prosthetic technologies across all testing domains.

A Case Series in Position-Aware Myoelectric Prosthesis Control Using Recurrent Convolutional Neural Network Classification with Transfer Learning

Position-aware myoelectric prosthesis controllers require long, data-intensive training routines. Transfer Learning (TL) might reduce training burden. A TL model can be pre-trained using forearm muscle signal data from many individuals to become the starting point for a new user. A recurrent convolutional neural network (RCNN)-based classifier has already been shown to benefit from TL in offline analysis (95% accuracy). The present real-time study tested whether an RCNN-based classification controller with TL (RCNN-TL) could reduce training burden, offer improved device control (per functional task performance metrics), and mitigate what is known as the "limb position effect". 27 participants without amputation were recruited. 19 participants performed wrist/hand movements across multiple limb positions, with resulting forearm muscle signal data used to pre-train RCNN-TL. 8 other participants donned a simulated prosthesis, retrained (calibrated) and tested RCNN-TL, plus trained and tested a conventional linear discriminant analysis classification controller (LDA-Baseline). Results confirmed that TL reduces user training burden. RCNN-TL yielded improved task performance durations over LDA-Baseline (in specific Grasp and Release phases), yet other metrics worsened. Overall, this work contributes training condition factors necessary for TL success, identifies metrics needed for comprehensive control analysis, and contributes insights towards improved position-aware control.

The multi-grip and standard myoelectric hand prosthesis compared: does the multi-grip hand live up to its promise?

BACKGROUND

Multi-grip myoelectric hand prostheses (MHPs), with five movable and jointed fingers, have been developed to increase functionality. However, literature comparing MHPs with standard myoelectric hand prostheses (SHPs) is limited and inconclusive. To establish whether MHPs increase functionality, we compared MHPs with SHPs on all categories of the International Classification of Functioning, Disability, and Health-model (ICF-model).

METHODS

MHP users (N = 14, 64.3% male, mean age = 48.6 years) performed physical measurements (i.e., Refined Clothespin Relocation Test (RCRT), Tray-test, Box and Blocks Test, Southampton Hand Assessment Procedure) with their MHP and an SHP to compare the joint angle coordination and functionality related to the ICF-categories 'Body Function' and 'Activities' (within-group comparisons). SHP users (N = 19, 68.4% male, mean age = 58.1 years) and MHP users completed questionnaires/scales (i.e., Orthotics and Prosthetics Users' Survey-The Upper Extremity Functional Status Survey /OPUS-UEFS, Trinity Amputation and Prosthesis Experience Scales for upper extremity/TAPES-Upper, Research and Development-36/RAND-36, EQ-5D-5L, visual analogue scale/VAS, the Dutch version of the Quebec User Evaluation of Satisfaction with assistive technology/D-Quest, patient-reported outcome measure to assess the preferred usage features of upper limb prostheses/PUF-ULP) to compare user experiences and quality of life in the ICF-categories 'Activities', 'Participation', and 'Environmental Factors' (between-group comparisons).

RESULTS

'Body Function' and 'Activities': nearly all users of MHPs had similar joint angle coordination patterns with an MHP as when they used an SHP. The RCRT in the upward direction was performed slower in the MHP condition compared to the SHP condition. No other differences in functionality were found. 'Participation': MHP users had a lower EQ-5D-5L utility score; experienced more pain or limitations due to pain (i.e., measured with the RAND-36). 'Environmental Factors': MHPs scored better than SHPs on the VAS-item holding/shaking hands. The SHP scored better than the MHP on five VAS-items (i.e., noise, grip force, vulnerability, putting clothes on, physical effort to control) and the PUF-ULP.

CONCLUSION

MHPs did not show relevant differences in outcomes compared to SHPs on any of the ICF-categories. This underlines the importance of carefully considering whether the MHP is the most suitable option for an individual taking into account the additional costs of MHPs.

Actual prosthetic usage in relation to functional outcomes and wearing time in individuals with below-elbow amputation

BACKGROUND

Wearing time of a prosthesis is regarded as an indicator for success of prosthetic rehabilitation. However, prostheses are frequently worn for esthetic purposes only. Although different supervised measurements to assess prosthetic dexterity are used, it is not clear how performance in such tests translates into actual use in everyday life.

OBJECTIVES

To evaluate the actual daily use of the prosthetic device in patients with below-elbow amputations by recording the number of grasping motions.

STUDY DESIGN

Observational study.

METHODS

Upper extremity function was evaluated using different objective and timed assessments in five unilateral patients with below-elbow amputations. In addition, patients reported daily wearing time, and the number of performed prosthetic movements over a period of at least three months was recorded.

RESULTS

The patients achieved a mean Southampton Hand Assessment Procedure score of 66.60 ± 18.64 points. The average blocks moved in the Box and Block Test were 20.80 ± 7.46, and the mean score in the Action Research Arm Test was 37.20 ± 5.45. The mean time for the Clothespin-Relocation Test was 26.90 ± 11.61 seconds. The patients reported a wearing time of an average of 12.80 ± 3.11 hours per day. The mean number of prosthetic motions performed each day was 257.23 ± 192.95 with a range from 23.07 to 489.13.

CONCLUSIONS

Neither high functionality nor long wearing times necessitated frequent use of a prosthesis in daily life. However, frequent daily motions did translate into good functional scores, indicating that regular device use in different real-life settings relates to functionality.

A comparison of compensatory movements between body-powered and myoelectric prosthesis users during activities of daily living

BACKGROUND

People with upper limb absence use compensatory movements to accommodate lack of motion in the prosthetic hand. The purpose of this study was to determine if the type of prosthesis used (i.e. body-powered or myoelectric) affects compensatory movements during activities of daily living.

METHODS

Twelve transradial body-powered and/or myoelectric prosthesis users performed up to six unimanual and bimanual activities of daily living. Trunk range of motion and peak upper limb angles for each task were compared between prostheses.

FINDINGS

Compensatory movement generally did not differ based on prosthesis type. However, body-powered users had increased trunk lateral lean compared to myoelectric users during a deodorant application task (P = 0.025). Body-powered users also had increased trunk axial rotation (P = 0.048) and decreased shoulder elevation (P = 0.046) when transferring a box between shelves. Compensatory movements were not systematically correlated with duration of prosthesis ownership, socket comfort, or terminal device type.

INTERPRETATION

A prosthesis user's compensatory movements may depend on other factors beyond whether the prosthesis terminal device is actuated through body-powered or myoelectric mechanisms. Further exploration of the factors that influence joint kinematics in prosthesis users may inform future prosthesis prescription practices and help patients become successful users.

Simultaneous control of 2DOF upper-limb prosthesis with body compensations-based control: a multiple cases study

Controlling several joints simultaneously is a common feature of natural arm movements. Robotic prostheses shall offer this possibility to their wearer. Yet, existing approaches to control a robotic upper-limb prosthesis from myoelectric interfaces do not satisfactorily respond to this need: standard methods provide sequential joint-by-joint motion control only; advanced pattern recognition-based approaches allow the control of a limited subset of synchronized multi-joint movements and remain complex to set up. In this paper, we exploit a control method of an upper-limb prosthesis based on body motion measurement called Compensations Cancellation Control (CCC). It offers a straightforward simultaneous control of the intermediate joints, namely the wrist and the elbow. Four transhumeral amputated participants performed the Refined Rolyan Clothespin Test with an experimental prosthesis alternatively running CCC and conventional joint-by-joint myoelectric control. Task performance, joint motions, body compensations and cognitive load were assessed. This experiment shows that CCC restores simultaneity between prosthetic joints while maintaining the level of performance of conventional myoelectric control (used on a daily basis by three participants), without increasing compensatory motions nor cognitive load.

First Demonstration of Functional Task Performance Using a Sonomyographic Prosthesis: A Case Study

Ultrasound-based sensing of muscle deformation, known as sonomyography, has shown promise for accurately classifying the intended hand grasps of individuals with upper limb loss in offline settings. Building upon this previous work, we present the first demonstration of real-time prosthetic hand control using sonomyography to perform functional tasks. An individual with congenital bilateral limb absence was fitted with sockets containing a low-profile ultrasound transducer placed over forearm muscle tissue in the residual limbs. A classifier was trained using linear discriminant analysis to recognize ultrasound images of muscle contractions for three discrete hand configurations (rest, tripod grasp, index finger point) under a variety of arm positions designed to cover the reachable workspace. A prosthetic hand mounted to the socket was then controlled using this classifier. Using this real-time sonomyographic control, the participant was able to complete three functional tasks that required selecting different hand grasps in order to grasp and move one-inch wooden blocks over a broad range of arm positions. Additionally, these tests were successfully repeated without retraining the classifier across 3 hours of prosthesis use and following simulated donning and doffing of the socket. This study supports the feasibility of using sonomyography to control upper limb prostheses in real-world applications.

Myoelectric Control Performance of Two Degree of Freedom Hand-Wrist Prosthesis by Able-Bodied and Limb-Absent Subjects

Recent research has advanced two degree-of-freedom (DoF), simultaneous, independent and proportional control of hand-wrist prostheses using surface electromyogram signals from remnant muscles as the control input. We evaluated two such regression-based controllers, along with conventional, sequential two-site control with co-contraction mode switching (SeqCon), in box-block, refined-clothespin and door-knob tasks, on 10 able-bodied and 4 limb-absent subjects. Subjects operated a commercial hand and wrist using a socket bypass harness. One 2-DoF controller (DirCon) related the intuitive hand actions of open-close and pronation-supination to the associated prosthesis hand-wrist actions, respectively. The other (MapCon) mapped myoelectrically more distinct, but less intuitive, actions of wrist flexion-extension and ulnar-radial deviation. Each 2-DoF controller was calibrated from separate 90 s calibration contractions. SeqCon performed better statistically than MapCon in the predominantly 1-DoF box-block task (>20 blocks/minute vs. 8-18 blocks/minute, on average). In this task, SeqCon likely benefited from an ability to easily focus on 1-DoF and not inadvertently trigger co-contraction for mode switching. The remaining two tasks require 2-DoFs, and both 2-DoF controllers each performed better (factor of 2-4) than SeqCon. We also compared the use of 12 vs. 6 optimally-selected EMG electrodes as inputs, finding no statistical difference. Overall, we provide further evidence of the benefits of regression-based EMG prosthesis control of 2-DoFs in the hand-wrist.

Transfer of mode switching performance: from training to upper-limb prosthesis use

BACKGROUND

Current myoelectric prostheses are multi-articulated and offer multiple modes. Switching between modes is often done through pre-defined myosignals, so-called triggers, of which the training hardly is studied. We evaluated if switching skills trained without using a prosthesis transfer to actual prosthesis use and whether the available feedback during training influences this transfer. Furthermore we examined which clinically relevant performance measures and which myosignal features were adapted during training.

METHODS

Two experimental groups and one control group participated in a five day pre-test-post-test design study. Both experimental groups used their myosignals to perform a task. One group performed a serious game without seeing their myosignals, the second group was presented their myosignal on a screen. The control group played the serious game using the touchpad of the laptop. Each training session lasted 15 min. The pre- and post-test were identical for all groups and consisted of performing a task with an actual prosthesis, where switches had to be produced to change grip mode to relocate clothespins. Both clinically relevant performance measures and myosignal features were analysed.

RESULTS

10 participants trained using the serious game, 10 participants trained with the visual myosignal and 8 the control task. All participants were unimpaired. Both experimental groups showed significant transfer of skill from training to prosthesis use, the control group did not. The degree of transfer did not differ between the two training groups. Clinically relevant measure 'accuracy' and feature of the myosignals 'variation in phasing' changed during training.

CONCLUSIONS

Training switching skills appeared to be successful. The skills trained in the game transferred to performance in a functional task. Learning switching skills is independent of the type of feedback used during training. Outcome measures hardly changed during training and further research is needed to explain this. It should be noted that five training sessions did not result in a level of performance needed for actual prosthesis use. Trial registration The study was approved by the local ethics committee (ECB 2014.02.28_1) and was included in the Dutch trial registry (NTR5876).

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.

Simultaneous control of multiple functions of bionic hand prostheses: Performance and robustness in end users

Myoelectric hand prostheses are usually controlled with two bipolar electrodes located on the flexor and extensor muscles of the residual limb. With clinically established techniques, only one function can be controlled at a time. This is cumbersome and limits the benefit of additional functions offered by modern prostheses. Extensive research has been conducted on more advanced control techniques, but the clinical impact has been limited, mainly due to the lack of reliability in real-world conditions. We implemented a regression-based control approach that allows for simultaneous and proportional control of two degrees of freedom and evaluated it on five prosthetic end users. In the evaluation of tasks mimicking daily life activities, we included factors that limit reliability, such as tests in different arm positions and on different days. The regression approach was robust over multiple days and only slightly affected by changing in the arm position. Additionally, the regression approach outperformed two clinical control approaches in most conditions.

Current rates of prosthetic usage in upper-limb amputees - have innovations had an impact on device acceptance?

PURPOSE

There is a large body of evidence demonstrating high rates of prosthesis abandonment in the upper extremity. However, these surveys were conducted years ago, thus the influence of recent refinements in prosthetic technology on acceptance is unknown. This study aims to gather current data on prosthetic usage, to assess the effects of these advancements.

MATERIALS AND METHODS

A questionnaire was sent to 68 traumatic upper limb amputees treated within the Austrian Trauma Insurance Agency between the years 1996 and 2016. Responses were grouped by the year of amputation to assess the effect of time.

RESULTS

The rejection rate at all levels of amputation was 44%. There was no significant difference in acceptance between responders amputated before or after 2006 (p = 0.939). Among users, 92.86% (n = 13) used a myoelectric, while only one amputee (7.14%, n = 1) used a body-powered device. Most responders complained about the comfort (60.87%, n = 14) as well as the weight of the device (52.17%, n = 12).

CONCLUSIONS

The advancements of the last decade in the arena of upper limb prosthetics have not yet achieved a significant change in prosthetic abandonment within this study cohort. Although academic solutions have been presented to tackle patient's complaints, clinical reality still shows high rejection rates of cost-intensive prosthetic devices.Implications for rehabilitationAbandonment rates in prosthetic rehabilitation after upper limb amputation have shown to be 50% and higher.The advancements of the last decade in the arena of upper limb prosthetics have not yet achieved a significant change in prosthetic abandonment.Well-structured and patient-tailored prosthetic training as well as ensuring the amputee's active participation in the decision making process will most likely improve prosthetic acceptance.

Longitudinal Case Study of Regression-Based Hand Prosthesis Control in Daily Life

Hand prostheses are usually controlled by electromyographic (EMG) signals from the remnant muscles of the residual limb. Most prostheses used today are controlled with very simple techniques using only two EMG electrodes that allow to control a single prosthetic function at a time only. Recently, modern prosthesis controllers based on EMG classification, have become clinically available, which allow to directly access more functions, but still in a sequential manner only. We have recently shown in laboratory tests that a regression-based mapping from EMG signals into prosthetic control commands allows for a simultaneous activation of two functions and an independent control of their velocities with high reliability. Here we aimed to study how such regression-based control performs in daily life in a two-month case study. The performance is evaluated in functional tests and with a questionnaire at the beginning and the end of this phase and compared with the participant's own prosthesis, controlled with a classical approach. Already 1 day after training of the regression model, the participant with transradial amputation outperformed the performance achieved with his own Michelangelo hand in two out of three functional metrics. No retraining of the model was required during the entire study duration. During the use of the system at home, the performance improved further and outperformed the conventional control in all three metrics. This study demonstrates that the high fidelity of linear regression-based prosthesis control is not restricted to a laboratory environment, but can be transferred to daily use.

The Merits of Dynamic Data Acquisition for Realistic Myocontrol

Natural myocontrol is the intuitive control of a prosthetic limb via the user's voluntary muscular activations. This type of control is usually implemented by means of pattern recognition, which uses a set of training data to create a model that can decipher these muscular activations. A consequence of this approach is that the reliability of a myocontrol system depends on how representative this training data is for all types of signal variability that may be encountered when the amputee puts the prosthesis into real use. Myoelectric signals are indeed known to vary according to the position and orientation of the limb, among other factors, which is why it has become common practice to take this variability into account by acquiring training data in multiple body postures. To shed further light on this problem, we compare two ways of collecting data: while the subjects hold their limb statically in several positions one at a time, which is the traditional way, or while they dynamically move their limb at a constant pace through those same positions. Since our interest is to investigate any differences when controlling an actual prosthetic device, we defined an evaluation protocol that consisted of a series of complex, bimanual daily-living tasks. Fourteen intact participants performed these tasks while wearing prosthetic hands mounted on splints, which were controlled via either a statically or dynamically built myocontrol model. In both cases all subjects managed to complete all tasks and participants without previous experience in myoelectric control manifested a significant learning effect; moreover, there was no significant difference in the task completion times achieved with either model. When evaluated in a simulated scenario with traditional offline performance evaluation, on the other hand, the dynamically-trained system showed significantly better accuracy. Regardless of the setting, the dynamic data acquisition was faster, less tiresome, and better accepted by the users. We conclude that dynamic data acquisition is advantageous and confirm the limited relevance of offline analyses for online myocontrol performance.

Performance among different types of myocontrolled tasks is not related

Studies on myocontrolled assistive technology (AT), such as myoelectric prostheses, as well as rehabilitation practice using myoelectric controlled interfaces, commonly assume the existence of a general myocontrol skill. This is the skill to control myosignals in such a way that they are employable in multiple tasks. If this skill exists, training any myocontrolled task using a certain set of muscles would improve the use of myocontrolled AT when the AT is controlled using these muscles. We examined whether a general myocontrol skill exists in myocontrolled tasks with and without a prosthesis. Unimpaired, right-handed adults used the sEMG of wrist flexors and extensors to perform several tasks in two experiments. In Experiment 1, twelve participants trained a myoelectric prosthesis-simulator task and a myocontrolled serious game for five consecutive days. Performance was compared between tasks and over the course of the training period. In Experiment 2, thirty-one participants performed five myocontrolled tasks consisting of two serious games, two prosthesis-simulator tasks and one digital signal matching task. All tasks were based on tasks currently used in clinical practice or research settings. Kendall rank correlation coefficients were computed to analyze correlations between the performance on different tasks. In Experiment 1 performance on the tasks showed no correlation for multiple outcome measures. Rankings within tasks did not change over the training period. In Experiment 2 performance did not correlate between any of the tasks. Since performance between different tasks did not correlate, results suggest that a general myocontrol skill does not exist and that each myocontrolled task requires a specific skill. Generalization of those findings to amputees using AT should be done with caution since in both experiments unimpaired participants were included. Moreover, training duration in Experiment 2 was short. Our findings indicate that training and assessment methods for myocontrolled AT use should focus on tasks frequently performed in daily life by the individual using the AT instead of merely focusing on training myosignals.

Functional Outcome Scores With Standard Myoelectric Prostheses in Below-Elbow Amputees

OBJECTIVE

The aim of the study was to report normative outcome data of prosthetic hand function in below-elbow amputees using four different objective measurements closely related to activities of daily living.

DESIGN

Seventeen patients who underwent prosthetic fitting after unilateral below-elbow amputation were enrolled in this study. Global upper extremity function was evaluated using the Action Research Arm Test, Southampton Hand Assessment Procedure, the Clothespin-Relocation Test, and the Box and Block Test, which monitor hand and extremity function.

RESULTS

The patients achieved a mean ± SD Action Research Arm Test score of 35.06 ± 4.42 of 57. The mean ± SD Southampton Hand Assessment Procedure score was 65.12 ± 13.95 points. The mean ± SD time for the Clothespin-Relocation Test was 22.57 ± 7.50 secs, and the mean ± SD score in the Box and Block Test was 20.90 ± 5.74.

CONCLUSIONS

In the current economic situation of health care systems, demonstrating the effectiveness and necessity of rehabilitation interventions is of major importance. This study reports outcome data of below-elbow amputees and provides a useful guide for expected prosthetic user performance.

Clinical evaluation of the refined clothespin relocation test: A pilot study

BACKGROUND

The refined clothespin relocation test is a test used to evaluate the performance of a prosthesis user by analysing the compensatory motions and time to complete a grasping and placement exercise. The test has been studied previously with a motion capture laboratory and has now been adapted for a clinical setting. A comparison of prosthesis user to an able-bodied group is needed to determine efficacy as an assessment tool.

OBJECTIVE

To modify the previous refined clothespin relocation test and assess whether it can distinguish between able-bodied and prosthesis users.

STUDY DESIGN

Comparative analysis.

METHODS

Forty-two able-bodied subjects and three prosthesis users completed the adapted refined clothespin relocation test protocol. Average refined clothespin relocation test scores describing the degree of compensatory movements and the time to complete the protocol were compared using a Mann-Whitney U-test.

RESULTS

A significant difference was found in the refined clothespin relocation test score between the able-bodied (Md = 65.32, n = 42) and prosthesis users (Md = 23.07, n = 3) with a medium effect size (p < 0.001, r = 0.43).

CONCLUSION

Prosthesis users demonstrated larger compensations and longer completion times, as reflected in the refined clothespin relocation test final score. The refined clothespin relocation test has the potential to be a useful clinical tool to assess user performance on a functional task.

CLINICAL RELEVANCE

This preliminary study demonstrates that the adapted protocol can distinguish between the two groups based on refined clothespin relocation test score. A future multi-centre study is required using multiple raters and comparing it with the existing outcome measures to validate the refined clothespin relocation test and determine inter-rater reliability.

Upbeat: Augmented Reality-Guided Dancing for Prosthetic Rehabilitation of Upper Limb Amputees

Unsuccessful rehabilitation therapy is a widespread issue amongst modern day amputees. Of the estimated 10 million amputees worldwide, 3 million of whom are upper limb amputees, a large majority are discontent and experience rejection with their current prosthesis during activities of daily living (ADL). Here we introduce Upbeat, an augmented reality (AR) dance game designed to improve rehabilitation therapies in upper limb amputees. In Upbeat, the patient is instructed to follow a virtual dance instructor, performing choreographed dance movements containing hand gestures involved in upper limb rehabilitation therapy. The patient's position is then tracked using a Microsoft Kinect sensor while the hand gestures are analyzed using EMG data collected from a Myo Armband. Additionally, a gamified score is calculated based on how many gestures and movements were correctly performed. Upon completion of the game, a diagnostic summary of the results is shown in the form of a graph summarizing the collected EMG data, as well as with a video displaying an augmented visualization of the patient's upper arm muscle activity during gameplay. By gamifying the rehabilitation process, Upbeat has the potential to improve therapy on upper limb amputees by enabling the start of rehabilitation immediately after trauma, providing personalized feedback which professionals can utilize to accurately assess patient's progress, and increasing patient excitement, therefore increasing patient willingness to complete rehabilitation. This paper is concerned with the description and evaluation of our prototypic implementation of Upbeat that will serve as the basis for conducting clinical studies to evaluate its impact on rehabilitation.

A hybrid auricular control system: direct, simultaneous, and proportional myoelectric control of two degrees of freedom in prosthetic hands

OBJECTIVE

The conventional myoelectric control scheme of hand prostheses provides a high level of robustness during continuous use. Typically, the electrical activity of an agonist/antagonist muscle pair in the forearm is detected and used to control either opening/closing or rotation of the prosthetic hand. The translation of more sophisticated control approaches (e.g. regression-based classifiers) to clinical practice is limited mainly because of their lack of robustness in real-world conditions (e.g. due to different arm positions). We therefore explore a new hybrid approach, in which a second degree of freedom (DOF) controlled by the myoelectric activity of the posterior auricular muscles is added to the conventional forearm control. With this, an independent, simultaneous and proportional control of rotation and opening/closing of the hand is possible.

APPROACH

In this study, we compared the hybrid auricular control system (hACS) to the two most commonly used control techniques for two DOF. Ten able-bodied subjects and one person with transradial amputation performed two standardizes tests in three different arm positions.

MAIN RESULTS

Subjects controlled a hand prosthesis significantly more rapidly and more accurately using the hACS. Moreover, the robustness of the system was not influenced by different arm positions.

SIGNIFICANCE

The hACS therefore offers an alternative solution for simultaneous and proportional myoelectric control of two degrees of freedom that avoids several robustness issues related to machine learning based approaches.

Home Use of a Neural-connected Sensory Prosthesis Provides the Functional and Psychosocial Experience of Having a Hand Again

The loss of a hand has many psychosocial repercussions. While advanced multi-articulated prostheses can improve function, without sensation, they cannot restore the full experience and connection of a hand. Direct nerve interfaces can restore naturalistic sensation to amputees. Our sensory restoration system produced tactile and proprioceptive sensations on the hand via neural stimulation through chronically implanted electrodes. In this study, upper limb amputees used a sensory-enabled prosthesis in their homes and communities, autonomously and unconstrained to specific tasks. These real-life conditions enabled us to study the impact of sensation on prosthetic usage, functional performance, and psychosocial experience. We found that sensory feedback fundamentally altered the way participants used their prosthesis, transforming it from a sporadically-used tool into a readily and frequently-used hand. Functional performance with sensation improved following extended daily use. Restored sensation improved a wide range of psychosocial factors, including self-efficacy, prosthetic embodiment, self-image, social interaction, and quality of life. This study demonstrates that daily use of a sensory-enabled prosthesis restores the holistic experience of having a hand and more fully reconnects amputees with the world.

Case-study of a user-driven prosthetic arm design: bionic hand versus customized body-powered technology in a highly demanding work environment

BACKGROUND

Prosthetic arm research predominantly focuses on "bionic" but not body-powered arms. However, any research orientation along user needs requires sufficiently precise workplace specifications and sufficiently hard testing. Forensic medicine is a demanding environment, also physically, also for non-disabled people, on several dimensions (e.g., distances, weights, size, temperature, time).

METHODS

As unilateral below elbow amputee user, the first author is in a unique position to provide direct comparison of a "bionic" myoelectric iLimb Revolution (Touch Bionics) and a customized body-powered arm which contains a number of new developments initiated or developed by the user: (1) quick lock steel wrist unit; (2) cable mount modification; (3) cast shape modeled shoulder anchor; (4) suspension with a soft double layer liner (Ohio Willowwood) and tube gauze (Molnlycke) combination. The iLimb is mounted on an epoxy socket; a lanyard fixed liner (Ohio Willowwood) contains magnetic electrodes (Liberating Technologies). An on the job usage of five years was supplemented with dedicated and focused intensive two-week use tests at work for both systems.

RESULTS

The side-by-side comparison showed that the customized body-powered arm provides reliable, comfortable, effective, powerful as well as subtle service with minimal maintenance; most notably, grip reliability, grip force regulation, grip performance, center of balance, component wear down, sweat/temperature independence and skin state are good whereas the iLimb system exhibited a number of relevant serious constraints.

CONCLUSIONS

Research and development of functional prostheses may want to focus on body-powered technology as it already performs on manually demanding and heavy jobs whereas eliminating myoelectric technology's constraints seems out of reach. Relevant testing could be developed to help expediting this. This is relevant as Swiss disability insurance specifically supports prostheses that enable actual work integration. Myoelectric and cosmetic arm improvement may benefit from a less forgiving focus on perfecting anthropomorphic appearance.

Characterisation of the Clothespin Relocation Test as a functional assessment tool

METHOD

The Clothespin Relocation Test has been adapted from an arm training tool to create an instrument to measure hand function. It is based on the time to move three clothespins from a horizontal to a vertical bar, and back. To be generally useful, the measures need to have their psychometric properties investigated. This paper measures the characteristics of an able-bodied population to gain an understanding of the underlying statistical properties of the test, in order that it can then be used to compare with different subject groups. Fifty adults (29 males, 21 females, mean age 31) were tested with five runs of three clothespins moved up and then down. Ten subjects returned twice more to observe repeatability.

RESULTS

There was a non-Gaussian range of times, from 2.5 to 7.37 s. Mean time for Up was 4.1 s, and was 4.0 s for Down, with a skew towards the faster times of 0.57 for Up and 0.97 for Down. Over the three sessions there was a small (not significant) increase in speed 4.1 ± 0.5 s first run Down to 3.5 ± 0.4 s for third.

CONCLUSION

These initial tests confirm that it has potential to be used as a measurement of the performance of arm movement.