Designing game-based myoelectric prosthesis training

A myoelectric prosthesis (myo) is a dexterous artificial limb controlled by muscle contractions. Learning to use a myo can be challenging, so extensive training is often required to use a myo prosthesis effectively. Signal visualizations and simple muscle-controlled games are currently used to help patients train their muscles, but are boring and frustrating. Furthermore, current training systems require expensive medical equipment and clinician oversight, restricting training to infrequent clinical visits. To address these limitations, we developed a new game that promotes fun and success, and shows the viability of a low-cost myoelectric input device. We adapted a user-centered design (UCD) process to receive feedback from patients, clinicians, and family members as we iteratively addressed challenges to improve our game. Through this work, we introduce a free and open myo training game, provide new information about the design of myo training games, and reflect on an adapted UCD process for the practical iterative development of therapeutic games

Serious Games for Training Myoelectric Prostheses through Multi-Contact Devices

In the medical context, designing and developing myoelectric prostheses has made it possible for patients to regain mobility lost due to amputations; however, their use requires intensive training. Serious games through multi-touch devices can serve as a complement to the activities carried out during face-to-face sessions with occupational therapists and physiotherapists, as a useful resource to engage patients, especially children, and make them enjoy training. In this paper, we describe our work to support the training of myoelectric prostheses through digital serious games. Firstly, we studied the needs of children with myoelectric prostheses and the way they perform rehabilitation. Secondly, we designed specific games to support training accordingly. Thirdly, we developed a system able to generate variations of these games dynamically, adapting the elements at each round to the needs and progress of each child. The interfaces are simple, friendly, and based on tablets to favor autonomy. Finally, we assessed the potential of the use of these games for rehabilitation. Specialists in Physiotherapy, Occupational Therapy, Medicine and Special Education collaborated as experts; they agreed that SilverTouch is good for myoelectric prosthetic training and confirmed its potential to be widely used in this context.This research was co-funded by the Spanish Ministry of Science and Innovation, project IndiGo! number PID2019-105951RB-I00 and the Structural Funds FSE and FEDER, project e-MadridCM number S2018/TCS-4307

Serious Games Are Not Serious Enough for Myoelectric Prosthetics

Serious games show a lot of potential for use in movement rehabilitation (eg, after a stroke, injury to the spinal cord, or limb loss). However, the nature of this research leads to diversity both in the background of the researchers and in the approaches of their investigation. Our close examination and categorization of virtual training software for upper limb prosthetic rehabilitation found that researchers typically followed one of two broad approaches: (1) focusing on the game design aspects to increase engagement and muscle training and (2) concentrating on an accurate representation of prosthetic training tasks, to induce task-specific skill transfer. Previous studies indicate muscle training alone does not lead to improved prosthetic control without a transfer-enabling task structure. However, the literature shows a recent surge in the number of game-based prosthetic training tools, which focus on engagement without heeding the importance of skill transfer. This influx appears to have been strongly influenced by the availability of both software and hardware, specifically the launch of a commercially available acquisition device and freely available high-profile game development engines. In this Viewpoint, we share our perspective on the current trends and progress of serious games for prosthetic training

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)

On-Demand Myoelectric Control Using Wake Gestures to Eliminate False Activations During Activities of Daily Living

While myoelectric control has recently become a focus of increased research as a possible flexible hands-free input modality, current control approaches are prone to inadvertent false activations in real-world conditions. In this work, a novel myoelectric control paradigm -- on-demand myoelectric control -- is proposed, designed, and evaluated, to reduce the number of unrelated muscle movements that are incorrectly interpreted as input gestures . By leveraging the concept of wake gestures, users were able to switch between a dedicated control mode and a sleep mode, effectively eliminating inadvertent activations during activities of daily living (ADLs). The feasibility of wake gestures was demonstrated in this work through two online ubiquitous EMG control tasks with varying difficulty levels; dismissing an alarm and controlling a robot. The proposed control scheme was able to appropriately ignore almost all non-targeted muscular inputs during ADLs (>99.9%) while maintaining sufficient sensitivity for reliable mode switching during intentional wake gesture elicitation. These results highlight the potential of wake gestures as a critical step towards enabling ubiquitous myoelectric control-based on-demand input for a wide range of applications

Virtual reality pre-prosthetic hand training with physics simulation and robotic force interaction

Virtual reality (VR) rehabilitation systems have been proposed to enable prosthetic hand users to perform training before receiving their prosthesis. Improving pre-prosthetic training to be more representative and better prepare the patient for prosthesis use is a crucial step forwards in rehabilitation. However, existing VR platforms lack realism and accuracy in terms of the virtual hand and the forces produced when interacting with the environment. To address these shortcomings, this work presents a VR training platform based on accurate simulation of an anthropomorphic prosthetic hand, utilising an external robot arm to render realistic forces that the user would feel at the attachment point of their prosthesis. Experimental results with non-disabled participants show that training with this platform leads to a significant improvement in Box and Block scores compared to training in VR alone and a control group with no prior training. Results performing pick-and-place tasks with a wider range of objects demonstrates that training in VR alone negatively impacts performance, whereas the proposed platform has no significant impact on performance. User perception results highlight that the platform is much closer to using a physical prosthesis in terms of physical demand and effort, however frustration is significantly higher during training

Immersive augmented reality system for the training of pattern classification control with a myoelectric prosthesis

Background!#!Hand amputation can have a truly debilitating impact on the life of the affected person. A multifunctional myoelectric prosthesis controlled using pattern classification can be used to restore some of the lost motor abilities. However, learning to control an advanced prosthesis can be a challenging task, but virtual and augmented reality (AR) provide means to create an engaging and motivating training.!##!Methods!#!In this study, we present a novel training framework that integrates virtual elements within a real scene (AR) while allowing the view from the first-person perspective. The framework was evaluated in 13 able-bodied subjects and a limb-deficient person divided into intervention (IG) and control (CG) groups. The IG received training by performing simulated clothespin task and both groups conducted a pre- and posttest with a real prosthesis. When training with the AR, the subjects received visual feedback on the generated grasping force. The main outcome measure was the number of pins that were successfully transferred within 20 min (task duration), while the number of dropped and broken pins were also registered. The participants were asked to score the difficulty of the real task (posttest), fun-factor and motivation, as well as the utility of the feedback.!##!Results!#!The performance (median/interquartile range) consistently increased during the training sessions (4/3 to 22/4). While the results were similar for the two groups in the pretest, the performance improved in the posttest only in IG. In addition, the subjects in IG transferred significantly more pins (28/10.5 versus 14.5/11), and dropped (1/2.5 versus 3.5/2) and broke (5/3.8 versus 14.5/9) significantly fewer pins in the posttest compared to CG. The participants in IG assigned (mean ± std) significantly lower scores to the difficulty compared to CG (5.2 ± 1.9 versus 7.1 ± 0.9), and they highly rated the fun factor (8.7 ± 1.3) and usefulness of feedback (8.5 ± 1.7).!##!Conclusion!#!The results demonstrated that the proposed AR system allows for the transfer of skills from the simulated to the real task while providing a positive user experience. The present study demonstrates the effectiveness and flexibility of the proposed AR framework. Importantly, the developed system is open source and available for download and further development

Convergence in myoelectric control:Between individual patterns of myoelectric learning

Objective: To support the design of assistive devices and prostheses, we investigated the changes in upper-limb muscle synergies during the practice of a myoelectric controlled game using proportional-sequential control. Methods: We evaluated 1) whether individual muscle synergies change in their structure; 2) variability; 3) distinctiveness; and 4) whether individuals become more similar with practice. Ten individuals practiced a myoelectric-controlled serious game for ten consecutive days (25 min/day) and one day after one week without training (retention). Results: The results showed that individuals decreased the number of synergies employed and modified their flexor synergies structure, becoming more similar as a group with practice. Nevertheless, within-individual synergies' variability and distinctiveness did not change. Conclusion: These results point out that individuals do not demonstrate muscle patterns less variable or differentiable after practice. However, participants increased performance and became more attuned to the task dynamics. Significance: The present findings indicate that, depending on the task requirements, individuals converge to more similar muscle activation patterns - a feature that should be further explored in prosthetic design

A review on the usability,flexibility, affinity, and affordability of virtual technology for rehabilitation training of upper limb amputees

(1) Background: Prosthetic rehabilitation is essential for upper limb amputees to regain their ability to work. However, the abandonment rate of prosthetics is higher than 50% due to the high cost of rehabilitation. Virtual technology shows potential for improving the availability and cost-effectiveness of prosthetic rehabilitation. This article systematically reviews the application of virtual technology for the prosthetic rehabilitation of upper limb amputees.(2) Methods: We followed PRISMA review guidance, STROBE, and CASP to evaluate the included articles. Finally, 17 articles were screened from 22,609 articles.(3) Results: This study reviews the possible benefits of using virtual technology from four aspects: usability, flexibility, psychological affinity, and long-term affordability. Three significant challenges are also discussed: realism, closed-loop control, and multi-modality integration.(4) Conclusions: Virtual technology allows for flexible and configurable control rehabilitation, both during hospital admissions and after discharge, at a relatively low cost. The technology shows promise in addressing the critical barrier of current prosthetic training issues, potentially improving the practical availability of prosthesis techniques for upper limb amputees

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