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Surgical techniques in amputation medicine did not change for a long time, while prosthesis technology underwent rapid development. The focus shifted to optimising the residual limb for prostheses use. At the same time, digital technologies such as gamification, virtual and mixed reality revolutionised rehabilitation. The use of gamification elements increases motivation and adherence to therapy, while immersive technologies enable realistic and interactive therapy experiences. This is particularly useful in the context of controlling modern prostheses and treating phantom pain. In addition, digital applications contribute to optimised documentation of symptoms and therapy successes. Overall, these technologies open up new, effective and personalised therapeutic approaches that can significantly improve the quality of life of amputation patients., (© 2024. The Author(s), under exclusive licence to Springer Medizin Verlag GmbH, ein Teil von Springer Nature.)

Background: Currently used prosthetic solutions in upper extremity amputation have limited functionality, owing to low information transfer rates of neuromuscular interfacing. Although surgical innovations have expanded the functional potential of the residual limb, available interfaces are inefficacious in translating this potential into improved prosthetic control. There is currently no implantable solution for functional interfacing in extremity amputation which offers long-term stability, high information transfer rates, and is applicable for all levels of limb loss. In this study, we presented a novel neuromuscular implant, the the Myoelectric Implantable Recording Array (MIRA). To our knowledge, it is the first fully implantable system for prosthetic interfacing with a large channel count, comprising 32 intramuscular electrodes., Questions/purposes: The purpose of this study was to evaluate the MIRA in terms of biocompatibility, functionality, and feasibility of implantation to lay the foundations for clinical application. This was achieved through small- and large-animal studies as well as test surgeries in a human cadaver., Methods: We evaluated the biocompatibility of the system's intramuscular electromyography (EMG) leads in a rabbit model. Ten leads as well as 10 pieces of a biologically inert control material were implanted into the paravertebral muscles of four animals. After a 3-month implantation, tissue samples were taken and histopathological assessment performed. The probes were scored according to a protocol for the assessment of the foreign body response, with primary endpoints being inflammation score, tissue response score, and capsule thickness in µm. In a second study, chronic functionality of the full system was evaluated in large animals. The MIRA was implanted into the shoulder region of six dogs and three sheep, with intramuscular leads distributed across agonist and antagonist muscles of shoulder flexion. During the observation period, regular EMG measurements were performed. The implants were removed after 5 to 6 months except for one animal, which retained the implant for prolonged observation. Primary endpoints of the large-animal study were mechanical stability, telemetric capability, and EMG signal quality. A final study involved the development of test surgeries in a fresh human cadaver, with the goal to determine feasibility to implant relevant target muscles for prosthetic control at all levels of major upper limb amputation., Results: Evaluation of the foreign body reaction revealed favorable biocompatibility and a low-grade tissue response in the rabbit study. No differences regarding inflammation score (EMG 4.60 ± 0.97 [95% CI 4.00 to 5.20] versus control 4.20 ± 1.48 [95% CI 3.29 to 5.11]; p = 0.51), tissue response score (EMG 4.00 ± 0.82 [95% CI 3.49 to 4.51] versus control 4.00 ± 0.94 [95% CI 3.42 to 4.58]; p > 0.99), or thickness of capsule (EMG 19.00 ± 8.76 µm [95% CI 13.57 to 24.43] versus control 29.00 ± 23.31 µm [95% CI 14.55 to 43.45]; p = 0.29) were found compared with the inert control article (high-density polyethylene) after 3 months of intramuscular implantation. Throughout long-term implantation of the MIRA in large animals, telemetric communication remained unrestricted in all specimens. Further, the implants retained the ability to record and transmit intramuscular EMG data in all animals except for two sheep where the implants became dislocated shortly after implantation. Electrode impedances remained stable and below 5 kΩ. Regarding EMG signal quality, there was little crosstalk between muscles and overall average signal-to-noise ratio was 22.2 ± 6.2 dB. During the test surgeries, we found that it was possible to implant the MIRA at all major amputation levels of the upper limb in a human cadaver (the transradial, transhumeral, and glenohumeral levels). For each level, it was possible to place the central unit in a biomechanically stable environment to provide unhindered telemetry, while reaching the relevant target muscles for prosthetic control. At only the glenohumeral level, it was not possible to reach the teres major and latissimus dorsi muscles, which would require longer lead lengths., Conclusion: As assessed in a combination of animal model and cadaver research, the MIRA shows promise for clinical research in patients with limb amputation, where it may be employed for all levels of major upper limb amputation to provide long-term stable intramuscular EMG transmission., Clinical Relevance: In our study, the MIRA provided high-bandwidth prosthetic interfacing through intramuscular electrode sites. Its high number of individual EMG channels may be combined with signal decoding algorithms for accessing spinal motor neuron activity after targeted muscle reinnervation, thus providing numerous degrees of freedom. Together with recent innovations in amputation surgery, the MIRA might enable improved control approaches for upper limb amputees, particularly for patients with above-elbow amputation where the mismatch between available control signals and necessary degrees of freedom for prosthetic control is highest., Competing Interests: All ICMJE Conflict of Interest Forms for authors and Clinical Orthopaedics and Related Research® editors and board members are on file with the publication and can be viewed on request., (Copyright © 2022 The Author(s). Published by Wolters Kluwer Health, Inc. on behalf of the Association of Bone and Joint Surgeons.)

Study Design: This is a Delphi study based on a scoping literature review., Introduction: Targeted muscle reinnervation (TMR) enables patients with high upper limb amputations to intuitively control a prosthetic arm with up to six independent control signals. Although there is a broad agreement regarding the importance of structured motor learning and prosthetic training after such nerve transfers, to date, no evidence-based protocol for rehabilitation after TMR exists., Purpose of the Study: We aimed at developing a structured rehabilitation protocol after TMR surgery after major upper limb amputation. The purpose of the protocol is to guide clinicians through the full rehabilitation process, from presurgical patient education to functional prosthetic training., Methods: European clinicians and researchers working in upper limb prosthetic rehabilitation were invited to contribute to a web-based Delphi study. Within the first round, clinical experts were presented a summary of recent literature and were asked to describe the rehabilitation steps based on their own experience and scientific evidence. The second round was used to refine these steps, while the importance of each step was rated within the third round., Results: Experts agreed on a rehabilitation protocol that consists of 16 steps and starts before surgery. It is based on two overarching principles, namely the necessity of multiprofessional teamwork and a careful selection and education of patients within the rehabilitation team. Among the different steps in therapy, experts rated the training with electromyographic biofeedback as the most important one., Discussion: Within this study, a first rehabilitation protocol for TMR patients based on a broad experts' consensus and relevant literature could be developed. The detailed steps for rehabilitation start well before surgery and prosthetic fitting, and include relatively novel interventions as motor imagery and biofeedback. Future studies need to further investigate the clinical outcomes and thereby improve therapists' practice., Conclusion: Graded rehabilitation offered by a multiprofessional team is needed to enable individuals with upper limb amputations and TMR to fully benefit from prosthetic reconstruction., Level of Evidence: Low., (Copyright © 2020 The Authors. Published by Elsevier Inc. All rights reserved.)

Background: Loss of an extremity at any level has a major impact on a patient's life. Using bionic reconstruction, extremity function can be restored and the patient reintegrated into daily life. Surgical procedures including selective nerve transfer and anchoring of prostheses into bone are combined with structured rehabilitation and modern prosthetic fitting. The patient is thereby able to use the prostheses intuitively and with multiple degrees of freedom., Methods: This article presents the concept and approach for modern bionic reconstruction in detail and the relevant literature. The nerve transfer matrices for targeted muscle reinnervation (TMR) and the concept of osseointegration to optimally fit a patient with a modern prosthesis are described in detail. As a clinical example, the case of a patient who suffered from traumatic amputation and subsequently received TMR in combination with an osseointegrated implant and structured rehabilitation is presented., Results: Using bionic reconstruction, basic hand functions can be restored and bimanual dexterity can expand the range of daily activities. Besides this approach to bionic reconstruction, its advantages and disadvantages are compared to hand transplantation. The limitations and perspectives of modern bionic reconstruction are also discussed., Conclusions: Bionic reconstruction is a sophisticated method for restoring extremity function and nowadays can be considered a standard of care for all levels of upper extremity amputations. An interdisciplinary approach and structured rehabilitation are necessary to master prosthetic function to ultimately reintegrate patients into daily life.

Research on machine learning approaches for upper-limb prosthesis control has shown impressive progress. However, translating these results from the lab to patient's everyday lives remains a challenge because advanced control schemes tend to break down under everyday disturbances, such as electrode shifts. Recently, it has been suggested to apply adaptive transfer learning to counteract electrode shifts using as little newly recorded training data as possible. In this paper, we present a novel, simple version of transfer learning and provide the first user study demonstrating the effectiveness of transfer learning to counteract electrode shifts. For this purpose, we introduce the novel Box and Beans test to evaluate prosthesis proficiency and compare user performance with an initial simple pattern recognition system, the system under electrode shifts, and the system after transfer learning. Our results show that transfer learning could significantly alleviate the impact of electrode shifts on user performance in the Box and Beans test.

Even though the hand comprises only 1% of our body weight, about 30% of our central nervous systems (CNS) capacity is related to its control. The loss of a hand thus presents not only the loss of the most important tool allowing us to interact with our environment, but also leaves a dramatic sensory-motor deficit that challenges our CNS. Reconstruction of hand function is therefore not only an essential part of restoring body integrity and functional wholeness but also closes the loop of our neural circuits diminishing phantom sensation and neural pain. If biology fails to restore meaningful function, today we can resort to complex mechatronic replacement that have functional capabilities that in some respects even outperform biological alternatives, such as hand transplantation. As with replantation and transplantations, the challenge of bionic replacement is connecting the target with the CNS to achieve natural and intuitive control. In recent years, we have developed a number of strategies to improve neural interfacing, signal extraction, interpretation and stable mechanical attachment that are important parts of our current research. This work gives an overview of recent advances in bionic reconstruction, surgical refinements over technological interfacing, skeletal fixation, and modern rehabilitation tools that allow quick integration of prosthetic replacement., (© 2019 International Center for Artificial Organs and Transplantation and Wiley Periodicals, Inc.)

Background: Control of a myoelectric prostheses entails rehabilitative training, based on repetitive exercises with a physiotherapist. However, many patients lack the motivation to continue the exercises in their home environment. Mobile games on the smartphone can provide patients with long-term motivation to continue the repetitive exercises that prepare the muscles for controlling a prosthesis at home. The aim of this study was to confirm the feasibility of a myoelectrical controlled mobile application and the impact of this game-based rehabilitation on the patient's maximum voluntary contraction strength, proportionally activated muscle contraction and ability to separate muscle groups., Patients and Methods: We developed a training system that consisted of a game-based mobile rehabilitation application that is controlled by the patient's muscle signal, a tablet to play on, an electrode armband and a manual. So far two patients have participated in this study. They were asked to use the app for 4 weeks at home, 5 times a week, for 10 to 15 minutes. The intervention was designed in a randomised controlled pre-test/post-test design and patients were measured for neuromuscular parameters before the intervention and afterwards. Evaluated parameters included maximum voluntary contraction force, muscle separation, proportional control and muscle endurance, as well as user statistics., Results: After training with the app, a significant improvement (p < .01) in all examined clinical parameters for myoelectric control of a prosthesis could be achieved. The user statistics showed a high motivation to play the game and ran an additional diagnostic EMG-Test on one patient; the other participating patient, however, had played the game but neglected the EMG test and only completed half of it., Conclusion: The training app not only provides instruction and feedback on the correct execution of myoelectric commands, but also maintains patient motivation through various game modes and feedback elements. The rehabilitation process could be monitored and compared through an overview of training progress in the form of user statistics and high scores. It could be shown that patients with upper extremity amputation could significantly improve their neuromuscular control, strength and coordination after using the game-based app so that they can fully benefit from the potential of a myoelectric prosthesis., Competing Interests: Die Autoren geben an, dass kein Interessenkonflikt besteht., (© Georg Thieme Verlag KG Stuttgart · New York.)

Background: Prosthetic motor rehabilitation usually relies on the highly repetitive training of movements. Patients might drop out of training because the rehabilitation process is long and often discouraging. Game-based interventions provide a potentially useful alternative to standard myoelectric (electromyographic [EMG]) training and can increase engagement with training., Objective: To assess the short-term impact of a game-based rehabilitation protocol on parameters for EMG control, evaluate how game-based interventions affect patient motivation, performance, and effort, and compare the game-based intervention with a standard tool in rehabilitation (MyoBoy)., Design: This randomized controlled trial included 2 patient groups and 1 control group. After establishing a baseline, the 2 patient groups received different interventions that were compared with each other and with the able-bodied control group., Setting: University hospital-based study., Participants: Fourteen patients with traumatic transradial or transhumeral upper extremity amputation and 10 able-bodied participants., Methods: For the game-based intervention, EMG proficiency was assessed before and after playing the games and 2 days later as follow-up to measure retention rate. EMG proficiency was measured using maximum voluntary contraction, proportional precision control, signal separation, and muscle endurance. Questionnaires for rating the game-based intervention and intrinsic motivation were provided after the intervention., Outcomes: Outcome measures for EMG proficiency were provisional maximum voluntary muscle contraction, precise proportional control, electrode separation, and muscle endurance. Quantitative outcome measures for participant experience were intrinsic motivation, enjoyment, pressure, exerted effort, and usefulness of the intervention. The qualitative outcome measure was the surveyed attitude toward the game-based intervention., Results: Results showed an overall improvement in EMG control, fine muscle activation, and electrode separation. Patients stated that racing games provided slightly more fun, but rhythm-based games were considered to provide better challenges for EMG control., Conclusion: Game-based interventions provide a useful addition to standard EMG training and can achieve better results in clinical outcome measures. The racing and music game genres provide solid starting points for interventions. Further studies can look at a wider range of genres and identify more specific game mechanics suitable for training., Level of Evidence: I., (Copyright © 2018 American Academy of Physical Medicine and Rehabilitation. Published by Elsevier Inc. All rights reserved.)

Background: Controlling a myoelectric prosthesis for upper limbs is increasingly challenging for the user as more electrodes and joints become available. Motion classification based on pattern recognition with a multi-electrode array allows multiple joints to be controlled simultaneously. Previous pattern recognition studies are difficult to compare, because individual research groups use their own data sets. To resolve this shortcoming and to facilitate comparisons, open access data sets were analysed using components of BioPatRec and Netlab pattern recognition models., Methods: Performances of the artificial neural networks, linear models, and training program components were compared. Evaluation took place within the BioPatRec environment, a Matlab-based open source platform that provides feature extraction, processing and motion classification algorithms for prosthetic control. The algorithms were applied to myoelectric signals for individual and simultaneous classification of movements, with the aim of finding the best performing algorithm and network model. Evaluation criteria included classification accuracy and training time., Results: Results in both the linear and the artificial neural network models demonstrated that Netlab's implementation using scaled conjugate training algorithm reached significantly higher accuracies than BioPatRec., Conclusions: It is concluded that the best movement classification performance would be achieved through integrating Netlab training algorithms in the BioPatRec environment so that future prosthesis training can be shortened and control made more reliable. Netlab was therefore included into the newest release of BioPatRec (v4.0).