Extended home use of an advanced osseointegrated prosthetic arm improves function, performance, and control efficiency

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.

Measuring and monitoring skill learning in closed-loop myoelectric hand prostheses using speed-accuracy tradeoffs

Objective.Closed-loop myoelectric prostheses, which combine supplementary sensory feedback and electromyography (EMG) based control, hold the potential to narrow the divide between natural and bionic hands. The use of these devices, however, requires dedicated training. Therefore, it is crucial to develop methods that quantify how users acquire skilled control over their prostheses to effectively monitor skill progression and inform the development of interfaces that optimize this process.Approach.Building on theories of skill learning in human motor control, we measured speed-accuracy tradeoff functions (SAFs) to comprehensively characterize learning-induced changes in skill-as opposed to merely tracking changes in task success across training-facilitated by a closed-loop interface that combined proportional control and EMG feedback. Sixteen healthy participants and one individual with a transradial limb loss participated in a three-day experiment where they were instructed to perform the box-and-blocks task using a timed force-matching paradigm at four specified speeds to reach two target force levels, such that the SAF could be determined.Main results.We found that the participants' accuracy increased in a similar way across all speeds we tested. Consequently, the shape of the SAF remained similar across days, at both force levels. Further, we observed that EMG feedback enabled participants to improve their motor execution in terms of reduced trial-by-trial variability, a hallmark of skilled behavior. We then fit a power law model of the SAF, and demonstrated how the model parameters could be used to identify and monitor changes in skill.Significance.We comprehensively characterized how an EMG feedback interface enabled skill acquisition, both at the level of task performance and movement execution. More generally, we believe that the proposed methods are effective for measuring and monitoring user skill progression in closed-loop prosthesis control.

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.

Artificial intelligence in physical rehabilitation: A systematic review

BACKGROUND

Physical disabilities become more common with advancing age. Rehabilitation restores function, maintaining independence for longer. However, the poor availability and accessibility of rehabilitation limits its clinical impact. Artificial Intelligence (AI) guided interventions have improved many domains of healthcare, but whether rehabilitation can benefit from AI remains unclear.

METHODS

We conducted a systematic review of AI-supported physical rehabilitation technology tested in the clinical setting to understand: 1) availability of AI-supported physical rehabilitation technology; 2) its clinical effect; 3) and the barriers and facilitators to implementation. We searched in MEDLINE, EMBASE, CINAHL, Science Citation Index (Web of Science), CIRRIE (now NARIC), and OpenGrey.

RESULTS

We identified 9054 articles and included 28 projects. AI solutions spanned five categories: App-based systems, robotic devices that replace function, robotic devices that restore function, gaming systems and wearables. We identified five randomised controlled trials (RCTs), which evaluated outcomes relating to physical function, activity, pain, and health-related quality of life. The clinical effects were inconsistent. Implementation barriers included technology literacy, reliability, and user fatigue. Enablers included greater access to rehabilitation programmes, remote monitoring of progress, reduction in manpower requirements and lower cost.

CONCLUSION

Application of AI in physical rehabilitation is a growing field, but clinical effects have yet to be studied rigorously. Developers must strive to conduct robust clinical evaluations in the real-world setting and appraise post implementation experiences.

Evoking natural thermal perceptions using a thin-film thermoelectric device with high cooling power density and speed

Multimodal sensory feedback from upper-limb prostheses can increase their function and usability. Here we show that intuitive thermal perceptions during cold-object grasping with a prosthesis can be restored in a phantom hand through targeted nerve stimulation via a wearable thin-film thermoelectric device with high cooling power density and speed. We found that specific regions of the residual limb, when thermally stimulated, elicited thermal sensations in the phantom hand that remained stable beyond 48 weeks. We also found stimulation sites that selectively elicited sensations of temperature, touch or both, depending on whether the stimulation was thermal or mechanical. In closed-loop functional tasks involving the identification of cold objects by amputees and by non-amputee participants, and compared with traditional bulk thermoelectric devices, the wearable thin-film device reliably elicited cooling sensations that were up to 8 times faster and up to 3 times greater in intensity while using half the energy and 1/600th the mass of active thermoelectric material. Wearable thin-film thermoelectric devices may allow for the non-invasive restoration of thermal perceptions during touch.

Improved control of a prosthetic limb by surgically creating electro-neuromuscular constructs with implanted electrodes

Remnant muscles in the residual limb after amputation are the most common source of control signals for prosthetic hands, because myoelectric signals can be generated by the user at will. However, for individuals with amputation higher up the arm, such as an above-elbow (transhumeral) amputation, insufficient muscles remain to generate myoelectric signals to enable control of the lost arm and hand joints, thus making intuitive control of wrist and finger prosthetic joints unattainable. We show that severed nerves can be divided along their fascicles and redistributed to concurrently innervate different types of muscle targets, particularly native denervated muscles and nonvascularized free muscle grafts. We engineered these neuromuscular constructs with implanted electrodes that were accessible via a permanent osseointegrated interface, allowing for bidirectional communication with the prosthesis while also providing direct skeletal attachment. We found that the transferred nerves effectively innervated their new targets as shown by a gradual increase in myoelectric signal strength. This allowed for individual flexion and extension of all five fingers of a prosthetic hand by a patient with a transhumeral amputation. Improved prosthetic function in tasks representative of daily life was also observed. This proof-of-concept study indicates that motor neural commands can be increased by creating electro-neuromuscular constructs using distributed nerve transfers to different muscle targets with implanted electrodes, enabling improved control of a limb prosthesis.

Examining the in vivo functionality of the Magnetically Aligned Regenerative Tissue-Engineered Electronic Nerve Interface (MARTEENI)

OBJECTIVE

Although neural-enabled prostheses have been used to restore some lost functionality in clinical trials, they have faced difficulty in achieving high degree of freedom, natural use compared to healthy limbs. This study investigated the in vivo functionality of a flexible and scalable regenerative peripheral-nerve interface suspended within a microchannel-embedded, tissue-engineered hydrogel (the Magnetically Aligned Regenerative Tissue-Engineered Electronic Nerve Interface, MARTEENI) as a potential approach to improving current issues in peripheral nerve interfaces.

APPROACH

Assembled MARTEENI devices were implanted in the gaps of severed sciatic nerves in Lewis rats. Both acute and chronic electrophysiology were recorded, and channel-isolated activity was examined. In terminal experiments, evoked activity during paw compression and stimulus response curves generated from proximal nerve stimulation were examined. Electrochemical impedance spectroscopy was performed to assess the complex impedance of recording sites during chronic data collection. Features of the foreign-body response in non-functional implants were examined using immunohistological methods.

MAIN RESULTS

Channel-isolated activity was observed in acute, chronic, and terminal experiments and showed a typically biphasic morphology with peak-to-peak amplitudes varying between 50 to 500 µV. For chronic experiments, electrophysiology was observed for 77 days post-implant. Within the templated hydrogel, regenerating axons formed minifascicles that varied in both size and axon count and were also found to surround device threads. No axons were found to penetrate the foreign-body response. Together these results suggest the MARTEENI is a promising approach for interfacing with peripheral nerves.

SIGNIFICANCE

Findings demonstrate a high likelihood that observed electrophysiological activity recorded from implanted MARTEENIs originated from neural tissue. The variation in minifascicle size seen histologically suggests that amplitude distributions observed in functional MARTEENIs may be due to a combination of individual axon and mini-compound action potentials. This study provided an assessment of a functional MARTEENI in an in vivo animal model for the first time.

Finger Movement Recognition via High-Density Electromyography of Intrinsic and Extrinsic Hand Muscles

Surface electromyography (sEMG) is commonly used to observe the motor neuronal activity within muscle fibers. However, decoding dexterous body movements from sEMG signals is still quite challenging. In this paper, we present a high-density sEMG (HD-sEMG) signal database that comprises simultaneously recorded sEMG signals of intrinsic and extrinsic hand muscles. Specifically, twenty able-bodied participants performed 12 finger movements under two paces and three arm postures. HD-sEMG signals were recorded with a 64-channel high-density grid placed on the back of hand and an 8-channel armband around the forearm. Also, a data-glove was used to record the finger joint angles. Synchronisation and reproducibility of the data collection from the HD-sEMG and glove sensors were ensured. The collected data samples were further employed for automated recognition of dexterous finger movements. The introduced dataset offers a new perspective to study the synergy between the intrinsic and extrinsic hand muscles during dynamic finger movements. As this dataset was collected from multiple participants, it also provides a resource for exploring generalized models for finger movement decoding.

Competitive motivation increased home use and improved prosthesis self-perception after Cybathlon 2020 for neuromusculoskeletal prosthesis user

Background

Assistive technologies, such as arm prostheses, are intended to improve the quality of life of individuals with physical disabilities. However, certain training and learning is usually required from the user to make these technologies more effective. Moreover, some people can be encouraged to train more through competitive motivation.

Methods

In this study, we investigated if the training for and participation in a competitive event (Cybathlon 2020) could promote behavioral changes in an individual with upper limb amputation (the pilot). We defined behavioral changes as the active time while his prosthesis was actuated, ratio of opposing and simultaneous movements, and the pilot’s ability to finely modulate his movement speeds. The investigation was based on extensive home-use data from the period before, during and after the Cybathlon 2020 competition.

Results

Relevant behavioral changes were found from both quantitative and qualitative analyses. The pilot’s home use of his prosthesis nearly doubled in the period before the Cybathlon, and remained 66% higher than baseline after the competition. Moreover, he improved his speed modulation when controlling his prosthesis, and he learned and routinely operated new movements in the prosthesis (wrist rotation) at home. Additionally, as confirmed by semi-structured interviews, his self-perception of the prosthetic arm and its functionality also improved.

Conclusions

An event like the Cybathlon may indeed promote behavioral changes in how competitive individuals with amputation use their prostheses. Provided that the prosthesis is suitable in terms of form and function for both competition and at-home daily use, daily activities can become opportunities for training, which in turn can improve prosthesis function and create further opportunities for daily use. Moreover, these changes appeared to remain even well after the event, albeit relevant only for individuals who continue using the technology employed in the competition.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12984-022-01024-4.

Monitoring at-home prosthesis control improvements through real-time data logging

OBJECTIVE

Validating the ability for advanced prostheses to improve function beyond the laboratory remains a critical step in enabling long-term benefits for prosthetic limb users.

APPROACH

A nine week take-home case study was completed with a single participant with upper limb amputation and osseointegration (OI) to better understand how an advanced prosthesis is used during daily activities. The participant was already an expert prosthesis user and used the Modular Prosthetic Limb (MPL) at home during the study. The MPL was controlled using wireless electromyography (EMG) pattern recognition-based movement decoding. Clinical assessments were performed before and after the take-home portion of the study. Data was recorded using an onboard data log in order to measure daily prosthesis usage, sensor data, and EMG data.

MAIN RESULT

The participant's continuous prosthesis usage steadily increased (p = 0.04, max = 5.5 hr) over time and over 30% of the total time was spent actively controlling the prosthesis. The duration of prosthesis usage after each pattern recognition training session also increased over time (p = 0.04), resulting in up to 5.4 hr of usage before retraining the movement decoding algorithm. Pattern recognition control accuracy improved (1.2% per week, p < 0.001) with a maximum number of 10 classes trained at once and the transitions between different degrees of freedom increased as the study progressed, indicating smooth and efficient control of the advanced prosthesis. Variability of decoding accuracy also decreased with prosthesis usage (p < 0.001) and 30% of the time was spent performing a prosthesis movement. During clinical evaluations, Box and Blocks and the Assessment of the Capacity for Myoelectric Control (ACMC) scores increased by 43% and 6.2%, respectively, demonstrating prosthesis functionality and the NASA Task Load Index (NASA-TLX) scores decreased, on average, by 25% across assessments, indicating reduced cognitive workload while using the MPL, over the nine week study.

SIGNIFICANCE

In this case study, we demonstrate that an onboard system to monitor prosthesis usage enables better understanding of how prostheses are incorporated into daily life. That knowledge can support the long-term goal of completely restoring independence and quality of life to individuals living with upper limb amputation.

Towards User-Centred Prosthetics Research Beyond the Laboratory

The purpose of this study was to explore a range of perspectives on how academic research and clinical assessment of upper-limb prosthetics could happen in environments outside of laboratories and clinics, such as within peoples' homes. Two co-creation workshops were held, which included people who use upper limb prosthetic devices (hereafter called users), clinicians, academics, a policy stakeholder, and a representative from the upper-limb prosthetics industry (hereafter called professionals). The discussions during the workshops indicate that research and clinical assessment conducted remotely from a laboratory or clinic could inform future solutions that address user needs. Users were open to the idea of sharing sensor and contextual data from within their homes to external laboratories during research studies. However, this was dependent upon several considerations, such as choice and control over data collection. Regarding clinical assessment, users had reservations of how data may be used to inform future prosthetic prescriptions whilst, clinicians were concerned with resource implications and capacity to process user data. The paper presents findings of the discussions shared by participants during both workshops. The paper concludes with a conjecture that collecting sensor and contextual data from users within their home environment will contribute towards literature within the field, and potentially inform future care policies for upper limb prosthetics. The involvement of users during such studies will be critical and can be enabled via a co-creation approach. In the short term, this may be achieved through academic research studies, which may in the long term inform a framework for clinical in-home trials and clinical remote assessment.

Towards Robust, Adaptive and Reliable Upper-limb Motion Estimation Using Machine Learning and Deep Learning--A Survey in Myoelectric Control

To develop multi-functional human-machine interfaces that can help disabled people reconstruct lost functions of upper-limbs, machine learning (ML) and deep learning (DL) techniques have been widely implemented to decode human movement intentions from surface electromyography (sEMG) signals. However, due to the high complexity of upper-limb movements and the inherent non-stable characteristics of sEMG, the usability of ML/DL based control schemes is still greatly limited in practical scenarios. To this end, tremendous efforts have been made to improve model robustness, adaptation, and reliability. In this article, we provide a systematic review on recent achievements, mainly from three categories: multi-modal sensing fusion to gain additional information of the user, transfer learning (TL) methods to eliminate domain shift impacts on estimation models, and post-processing approaches to obtain more reliable outcomes. Special attention is given to fusion strategies, deep TL frameworks, and confidence estimation. \textcolor{red}{Research challenges and emerging opportunities, with respect to hardware development, public resources, and decoding strategies, are also analysed to provide perspectives for future developments.

Spatio-temporal warping for myoelectric control: an offline, feasibility study

Objective.The efficacy of an adopted feature extraction method directly affects the classification of the electromyographic (EMG) signals in myoelectric control applications. Most methods attempt to extract the dynamics of the multi-channel EMG signals in the time domain and on a channel-by-channel, or at best pairs of channels, basis. However, considering multi-channel information to build a similarity matrix has not been taken into account.Approach.Combining methods of long and short-term memory (LSTM) and dynamic temporal warping, we developed a new feature, called spatio-temporal warping (STW), for myoelectric signals. This method captures the spatio-temporal relationships of multi-channels EMG signals.Main results. Across four online databases, we show that in terms of average classification error and standard deviation values, the STW feature outperforms traditional features by 5%-17%. In comparison to the more recent deep learning models, e.g. convolutional neural networks (CNNs), STW outperformed by 5%-18%. Also, STW showed enhanced performance when compared to the CNN + LSTM model by 2%-14%. All differences were statistically significant with a large effect size.Significance.This feasibility study provides evidence supporting the hypothesis that the STW feature of the EMG signals can enhance the classification accuracy in an explainable way when compared to recent deep learning methods. Future work includes real-time implementation of the method and testing for prosthesis control.

Intracortical microstimulation of somatosensory cortex enables object identification through perceived sensations

Advances in brain-machine interfaces have helped restore function and independence for individuals with sensorimotor deficits; however, providing efficient and effective sensory feedback remains challenging. Intracortical microstimulation (ICMS) of sensorimotor brain regions is a promising technique for providing bioinspired sensory feedback. In a human participant with chronically-implanted microelectrode arrays, we provided ICMS to the primary somatosensory cortex to generate tactile percepts in his hand. In a 3-choice object identification task, the participant identified virtual objects using tactile sensory feedback and no visual information. We evaluated three different stimulation paradigms, each with a different weighting of the grip force and its derivative, to explore the potential benefits of a more bioinspired stimulation strategy. In all paradigms, the participant's ability to identify the objects was above-chance, with object identification accuracy reaching 80% correct when using only sustained grip force feedback and 76.7% when using equal weighting of both sustained grip force and its derivative. These results demonstrate that bioinspired ICMS can provide sensory feedback that is functionally beneficial in sensorimotor tasks. Designing more efficient stimulation paradigms is important because it will allow us to 1) provide safer stimulation delivery methods that reduce overall injected charge without sacrificing function and 2) more effectively transmit sensory information to promote intuitive integration and usage by the human body.