Treatment of phantom limb pain (PLP) based on augmented reality and gaming controlled by myoelectric pattern recognition: a case study of a chronic PLP patient

A variety of treatments have been historically used to alleviate phantom limb pain (PLP) with varying efficacy. Recently, virtual reality (VR) has been employed as a more sophisticated mirror therapy. Despite the advantages of VR over a conventional mirror, this approach has retained the use of the contralateral limb and is therefore restricted to unilateral amputees. Moreover, this strategy disregards the actual effort made by the patient to produce phantom motions. In this work, we investigate a treatment in which the virtual limb responds directly to myoelectric activity at the stump, while the illusion of a restored limb is enhanced through augmented reality (AR). Further, phantom motions are facilitated and encouraged through gaming. The proposed set of technologies was administered to a chronic PLP patient who has shown resistance to a variety of treatments (including mirror therapy) for 48 years. Individual and simultaneous phantom movements were predicted using myoelectric pattern recognition and were then used as input for VR and AR environments, as well as for a racing game. The sustained level of pain reported by the patient was gradually reduced to complete pain-free periods. The phantom posture initially reported as a strongly closed fist was gradually relaxed, interestingly resembling the neutral posture displayed by the virtual limb. The patient acquired the ability to freely move his phantom limb, and a telescopic effect was observed where the position of the phantom hand was restored to the anatomically correct distance. More importantly, the effect of the interventions was positively and noticeably perceived by the patient and his relatives. Despite the limitation of a single case study, the successful results of the proposed system in a patient for whom other medical and non-medical treatments have been ineffective justifies and motivates further investigation in a wider study

The effect of feedback during training sessions on learning pattern-recognition based prosthesis control

Human-machine interfaces have not yet advanced to enable intuitive control of multiple degrees of freedom as offered by modern myoelectric prosthetic hands. Pattern Recognition (PR) control has been proposed to make human-machine interfaces in myoelectric prosthetic hands more intuitive, but it requires the user to generate high-quality, i.e., consistent and separable, electromyogram (EMG) patterns. To generate such patterns, user training is required and has shown promising results. However, how different levels of feedback affect effectivity in training differently, has not been established yet. Furthermore, a correlation between qualities of the EMG patterns (the focus of training) and user performance has not been shown yet. In this study, 37 able-bodied participants (mean age 21 years, 19 males) were recruited and trained PR control over five days. Three levels of feedback were tested for their effectiveness: no external feedback, visual feedback and visual feedback with coaching. Training resulted in improved performance from pre-to post-test with no interaction effect of feedback. Feedback did however affect the quality of the EMG patterns where people who did not receive external feedback generated higher amplitude patterns. A weak correlation was found between a principal component, composed of EMG amplitude and pattern variability, and performance. Our results show that training is highly effective in improving PR control regardless of feedback and that none of the quality metrics correlate with performance. We discuss how different levels of feedback can be leveraged to improve PR control training

Serious gaming to generate separated and consistent EMG patterns in pattern-recognition prosthesis control

Pattern-Recognition (PR) control of upper-limb prosthetics has shown inconsistent results outside lab settings, which might be due to the inadequacy of users’ electromyogram (EMG) patterns. To improve the separability and consistency of their EMG, users can receive training. Conventional training uses an internal focus of attention as prosthesis users focus on the muscle contractions of their (phantom) hand together with explicit learning processes facilitated by a coach guiding the user. In this study we investigated if an alternative training paradigm using an external focus of attention exploiting implicit learning processes based on serious gaming without a coach could lead to more separable and consistent EMG. Able-bodied participants (N = 25; mean age 22 years, 13 females) were recruited and followed conventional or game training for five days. In conventional training, participants performed the Motion Test thrice daily and received coaching on how to adapt their muscle contractions. In game training, participants controlled an avatar using a direct mapping from electrode to avatar direction. The participants utilized implicit learning processes, by exploring which muscle contractions made the avatar go in which directions. Performance in both groups was evaluated by using the Motion Test in a pre/post-test design. Training resulted in improved performance, with no differences between training paradigms. Participants who followed game training showed 51% more separated EMG patterns. EMG pattern consistency did not change over training. It was concluded that serious game training using an external focus of attention and implicit learning can be considered as a viable alternative to conventional training.</p

Should Hands Be Restricted When Measuring Able-Bodied Participants To Evaluate Machine Learning Controlled Prosthetic Hands?

OBJECTIVE: When evaluating methods for machine-learning controlled prosthetic hands, able-bodied participants are often recruited, for practical reasons, instead of participants with upper limb absence (ULA). However, able-bodied participants have been shown to often perform myoelectric control tasks better than participants with ULA. It has been suggested that this performance difference can be reduced by restricting the wrist and hand movements of able-bodied participants. However, the effect of such restrictions on the consistency and separability of the electromyogram's (EMG) features remains unknown. The present work investigates whether the EMG separability and consistency between unaffected and affected arms differ and whether they change after restricting the unaffected limb in persons with ULA. METHODS: Both arms of participants with unilateral ULA were compared in two conditions: with the unaffected hand and wrist restricted or not. Furthermore, it was tested if the effect of arm and restriction is influenced by arm posture (arm down, arm in front, or arm up). RESULTS: Fourteen participants (two women, age=53.4±4.05) with acquired transradial limb loss were recruited. We found that the unaffected limb generated more separated EMG than the affected limb. Furthermore, restricting the unaffected hand and wrist lowered the separability of the EMG when the arm was held down. CONCLUSION: Limb restriction is a viable method to make the EMG of able-bodied participants more similar to that of participants with ULA. SIGNIFICANCE: Future research that evaluates methods for machine learning controlled hands in able-bodied participants should restrict the participants' hand and wrist

Upper Limb Stroke Rehabilitation Using Surface Electromyography: A Systematic Review and Meta-Analysis

Background: Upper limb impairment is common after stroke, and many will not regain full upper limb function. Different technologies based on surface electromyography (sEMG) have been used in stroke rehabilitation, but there is no collated evidence on the different sEMG-driven interventions and their effect on upper limb function in people with stroke. Aim: Synthesize existing evidence and perform a meta-analysis on the effect of different types of sEMG-driven interventions on upper limb function in people with stroke. Methods: PubMed, SCOPUS, and PEDro databases were systematically searched for eligible randomized clinical trials that utilize sEMG-driven interventions to improve upper limb function assessed by Fugl-Meyer Assessment (FMA-UE) in stroke. The PEDro scale was used to evaluate the methodological quality and the risk of bias of the included studies. In addition, a meta-analysis utilizing a random effect model was performed for studies comparing sEMG interventions to non-sEMG interventions and for studies comparing different sEMG interventions protocols. Results: Twenty-four studies comprising 808 participants were included in this review. The methodological quality was good to fair. The meta-analysis showed no differences in the total effect, assessed by total FMA-UE score, comparing sEMG interventions to non-sEMG interventions (14 studies, 509 participants, SMD 0.14, P 0.37, 95% CI –0.18 to 0.46, I2 55%). Similarly, no difference in the overall effect was found for the meta-analysis comparing different types of sEMG interventions (7 studies, 213 participants, SMD 0.42, P 0.23, 95% CI –0.34 to 1.18, I2 73%). Twenty out of the twenty-four studies, including participants with varying impairment levels at all stages of stroke recovery, reported statistically significant improvements in upper limb function at post-sEMG intervention compared to baseline. Conclusion: This review and meta-analysis could not discern the effect of sEMG in comparison to a non-sEMG intervention or the most effective type of sEMG intervention for improving upper limb function in stroke populations. Current evidence suggests that sEMG is a promising tool to further improve functional recovery, but randomized clinical trials with larger sample sizes are needed to verify whether the effect on upper extremity function of a specific sEMG intervention is superior compared to other non-sEMG or other type of sEMG interventions

User training for machine learning controlled upper limb prostheses:a serious game approach

BACKGROUND: Upper limb prosthetics with multiple degrees of freedom (DoFs) are still mostly operated through the clinical standard Direct Control scheme. Machine learning control, on the other hand, allows controlling multiple DoFs although it requires separable and consistent electromyogram (EMG) patterns. Whereas user training can improve EMG pattern quality, conventional training methods might limit user potential. Training with serious games might lead to higher quality EMG patterns and better functional outcomes. In this explorative study we compare outcomes of serious game training with conventional training, and machine learning control with the users' own one DoF prosthesis. METHODS: Participants with upper limb absence participated in 7 training sessions where they learned to control a 3 DoF prosthesis with two grips which was fitted. Participants received either game training or conventional training. Conventional training was based on coaching, as described in the literature. Game-based training was conducted using two games that trained EMG pattern separability and functional use. Both groups also trained functional use with the prosthesis donned. The prosthesis system was controlled using a neural network regressor. Outcome measures were EMG metrics, number of DoFs used, the spherical subset of the Southampton Hand Assessment Procedure and the Clothespin Relocation Test. RESULTS: Eight participants were recruited and four completed the study. Training did not lead to consistent improvements in EMG pattern quality or functional use, but some participants improved in some metrics. No differences were observed between the groups. Participants achieved consistently better results using their own prosthesis than the machine-learning controlled prosthesis used in this study. CONCLUSION: Our explorative study showed in a small group of participants that serious game training seems to achieve similar results as conventional training. No consistent improvements were found in either group in terms of EMG metrics or functional use, which might be due to insufficient training. This study highlights the need for more research in user training for machine learning controlled prosthetics. In addition, this study contributes with more data comparing machine learning controlled prosthetics with Direct Controlled prosthetics

Skin stimulation and recording: Moving towards metal-free electrodes

When one thinks about electrodes, especially ones meant for humans, one typically thinks of some kind of metal. Whether on the skin or in the brain, metal electrodes are characteristically expensive, stiff, non-efficient in electron-ion transduction, and prone to toxic metal ion by-products during stimulation. In order to circumvent these disadvantages, electrically-conductive laser-induced graphene (LIG) and mixed electron-ion conducting polymer (poly(3, 4‐ethylenedioxythiophene) polystyrene sulfonate – PEDOT:PSS) was leveraged to create a metal-free electrode combination that allows for an economical, soft, and organic electrode for applications on human skin. Compared to clinical-standard silver – silver chloride (Ag/AgCl) skin electrodes, the metal-free hydrogel electrodes show notable improvement in electrochemical stability and prolonged stable potentials during long-term DC stimulation (0.5–24 h). Recording and stimulation performance on human participants rivals that of Ag/AgCl, thus fortifying the notion that they are an appropriate progression to their noble metal counterparts

Skin stimulation and recording: Moving towards metal-free electrodes

When one thinks about electrodes, especially ones meant for humans, one typically thinks of some kind of metal. Whether on the skin or in the brain, metal electrodes are characteristically expensive, stiff, non-efficient in electron-ion transduction, and prone to toxic metal ion by-products during stimulation. In order to circumvent these disadvantages, electrically-conductive laser-induced graphene (LIG) and mixed electron-ion conducting polymer (poly(3, 4‐ethylenedioxythiophene) polystyrene sulfonate – PEDOT:PSS) was leveraged to create a metal-free electrode combination that allows for an economical, soft, and organic electrode for applications on human skin. Compared to clinical-standard silver – silver chloride (Ag/AgCl) skin electrodes, the metal-free hydrogel electrodes show notable improvement in electrochemical stability and prolonged stable potentials during long-term DC stimulation (0.5–24 h). Recording and stimulation performance on human participants rivals that of Ag/AgCl, thus fortifying the notion that they are an appropriate progression to their noble metal counterparts

Model-Checking Real-Time Control Programs. Verifying LEGO Mindstorms Systems Using UPPAAL

In this paper, we present a method for automatic verificationof real-time control programs running on LEGO RCX bricks using the verification tool UPPAAL. The controlprograms, consisting of a number of tasks running concurrently,are automatically translated into the timed automatamodel of UPPAAL. The fixed scheduling algorithmused by the LEGO RCX processor is modeled in UPPAAL,and supply of similar (sufficient) timed automatamodels for the environment allows analysis of the overallreal-time system using the tools of UPPAAL. To illustrateour techniques we have constructed, modeled and verifieda machine for sorting LEGO bricks by color