Embedded System for Prosthetic Control Using Implanted Neuromuscular Interfaces Accessed Via an Osseointegrated Implant

Performance in myoelectric pattern recognition improves with transcranial direct current stimulation

Sensorimotor impairments, resulting from conditions like stroke and amputations, can profoundly impact an individual's functional abilities and overall quality of life. Assistive and rehabilitation devices such as prostheses, exo-skeletons, and serious gaming in virtual environments can help to restore some degree of function and alleviate pain after sensorimotor impairments. Myoelectric pattern recognition (MPR) has gained popularity in the past decades as it provides superior control over said devices, and therefore efforts to facilitate and improve performance in MPR can result in better rehabilitation outcomes. One possibility to enhance MPR is to employ transcranial direct current stimulation (tDCS) to facilitate motor learning. Twelve healthy able-bodied individuals participated in this crossover study to determine the effect of tDCS on MPR performance. Baseline training was followed by two sessions of either sham or anodal tDCS using the dominant and non-dominant arms. Assignments were randomized, and the MPR task consisted of 11 different hand/wrist movements, including rest or no movement. Surface electrodes were used to record EMG and the MPR open-source platform, BioPatRec, was used for decoding motor volition in real-time. The motion test was used to evaluate performance. We hypothesized that using anodal tDCS to increase the excitability of the primary motor cortex associated with non-dominant side in able-bodied individuals, will improve motor learning and thus MPR performance. Overall, we found that tDCS enhanced MPR performance, particularly in the non-dominant side. We were able to reject the null hypothesis and improvements in the motion test's completion rate during tDCS (28% change, p-value: 0.023) indicate its potential as an adjunctive tool to enhance MPR and motor learning. tDCS appears promising as a tool to enhance the learning phase of using assistive devices using MPR, such as myoelectric prostheses.

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.

Interaction force modeling and analysis of the human-machine kinematic chain based on the human-machine deviation

A mechanical model for a human-machine interaction force based on the man-machine kinematic chain is established. This is combined with screw theory and a virtual rigid body model for the human-machine interaction force is proposed. This model interprets the basic principle model of the human-machine contact force. The deviation of the human-machine kinematic chain is calculated using the virtual model. In order to carry out the calibration simulation for the model, a 6-sps parallel mechanism is taken as an example to illustrate the construction principle of the human-machine interaction virtual rigid body model. This is calibrated by introducing finite element software. Finally, using the knee exoskeleton as an example, a numerical simulation is introduced. This illustrates the relationship between the driving force of the exoskeleton, the human-machine deviation as well as the virtual stiffness. The modeling method of this paper provides theoretical reference for controller design of human-machine interaction forces in the future.

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.

Design trends in actuated lower-limb prosthetic systems: a narrative review

INTRODUCTION

Actuated lower limb prostheses, including powered (active) and semi-active (quasi-passive) joints, are endowed with controllable power and/or impedance, which can be advantageous to limb impairment individuals by improving locomotion mechanics and reducing the overall metabolic cost of ambulation. However, an increasing number of commercial and research-focused options have made navigating this field a daunting task for users, researchers, clinicians, and professionals.

AREAS COVERED

The present paper provides an overview of the latest trends and developments in the field of actuated lower-limb prostheses and corresponding technologies. Following a gentle summary of essential gait features, we introduce and compare various actuated prosthetic solutions in academia and the market designed to provide assistance at different levels of impairments. Correspondingly, we offer insights into the latest developments of sockets and suspension systems, before finally discussing the established and emerging trends in surgical approaches aimed at improving prosthetic experience through enhanced physical and neural interfaces.

EXPERT OPINION

The ongoing challenges and future research opportunities in the field are summarized for exploring potential avenues for development of next generation of actuated lower limb prostheses. In our opinions, a closer multidisciplinary integration can be found in the field of actuated lower-limb prostheses in the future.

Toward higher-performance bionic limbs for wider clinical use

Most prosthetic limbs can autonomously move with dexterity, yet they are not perceived by the user as belonging to their own body. Robotic limbs can convey information about the environment with higher precision than biological limbs, but their actual performance is substantially limited by current technologies for the interfacing of the robotic devices with the body and for transferring motor and sensory information bidirectionally between the prosthesis and the user. In this Perspective, we argue that direct skeletal attachment of bionic devices via osseointegration, the amplification of neural signals by targeted muscle innervation, improved prosthesis control via implanted muscle sensors and advanced algorithms, and the provision of sensory feedback by means of electrodes implanted in peripheral nerves, should all be leveraged towards the creation of a new generation of high-performance bionic limbs. These technologies have been clinically tested in humans, and alongside mechanical redesigns and adequate rehabilitation training should facilitate the wider clinical use of bionic limbs.

Neurostimulation artifact removal for implantable sensors improves signal clarity and decoding of motor volition

As the demand for prosthetic limbs with reliable and multi-functional control increases, recent advances in myoelectric pattern recognition and implanted sensors have proven considerably advantageous. Additionally, sensory feedback from the prosthesis can be achieved via stimulation of the residual nerves, enabling closed-loop control over the prosthesis. However, this stimulation can cause interfering artifacts in the electromyographic (EMG) signals which deteriorate the reliability and function of the prosthesis. Here, we implement two real-time stimulation artifact removal algorithms, Template Subtraction (TS) and ε-Normalized Least Mean Squares (ε-NLMS), and investigate their performance in offline and real-time myoelectric pattern recognition in two transhumeral amputees implanted with nerve cuff and EMG electrodes. We show that both algorithms are capable of significantly improving signal-to-noise ratio (SNR) and offline pattern recognition accuracy of artifact-corrupted EMG signals. Furthermore, both algorithms improved real-time decoding of motor intention during active neurostimulation. Although these outcomes are dependent on the user-specific sensor locations and neurostimulation settings, they nonetheless represent progress toward bi-directional neuromusculoskeletal prostheses capable of multifunction control and simultaneous sensory feedback.

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.

Implantable biosensors for musculoskeletal health

PURPOSE

A healthy musculoskeletal system requires complex functional integration of bone, muscle, cartilage, and connective tissues responsible for bodily support, motion, and the protection of vital organs. Conditions or injuries to musculoskeeltal tissues can devastate an individual's quality of life. Some conditions that are particularly disabling include severe bone and muscle injuries to the extremities and amputations resulting from unmanageable musculoskeletal conditions or injuries. Monitoring and managing musculoskeletal health is intricate because of the complex mechanobiology of these interconnected tissues.

METHODS

For this article, we reviewed literature on implantable biosensors related to clinical data of the musculoskeletal system, therapeutics for complex bone injuries, and osseointegrated prosthetics as example applications.

RESULTS

As a result, a brief summary of biosensors technologies is provided along with review of noteworthy biosensors and future developments needed to fully realize the translational benefit of biosensors for musculoskeletal health.

CONCLUSIONS

Novel implantable biosensors capable of tracking biophysical parameters in vivo are highly relevant to musculoskeletal health because of their ability to collect clinical data relevant to medical decisions, complex trauma treatment, and the performance of osseointegrated prostheses.

Enhancing osteoblast survival through pulsed electrical stimulation and implications for osseointegration

Electrical stimulation has been suggested as a means for promoting the direct structural and functional bonding of bone tissue to an artificial implant, known as osseointegration. Previous work has investigated the impact of electrical stimulation in different models, both in vitro and in vivo, using various electrode configurations for inducing an electric field with a wide range of stimulation parameters. However, there is no consensus on optimal electrode configuration nor stimulation parameters. Here, we investigated a novel approach of delivering electrical stimulation to a titanium implant using parameters clinically tested in a different application, namely peripheral nerve stimulation. We propose an in vitro model comprising of Ti6Al4V implants precultured with MC3T3-E1 preosteoblasts, stimulated for 72 h at two different pulse amplitudes (10 µA and 20 µA) and at two different frequencies (50 Hz and 100 Hz). We found that asymmetric charge-balanced pulsed electrical stimulation improved cell survival and collagen production in a dose-dependent manner. Our findings suggest that pulsed electrical stimulation with characteristics similar to peripheral nerve stimulation has the potential to improve cell survival and may provide a promising approach to improve peri-implant bone healing, particularly to neuromusculoskeletal interfaces in which implanted electrodes are readily available.

Understanding the Relationship Between Patient-Reported Function and Actual Function in the Upper Limb Prosthesis User Population: A Preliminary Study

Objective

To understand how perceived function relates to actual function at a specific stage in the rehabilitation process for the population using upper limb prostheses.

Design

Quantitative clinical descriptive study.

Setting

Clinical offices.

Participants

A sample of 61 participants (N=61; mean age, 43.0±12.8y; 51 male/10 female) with upper limb amputation who use a prosthetic device and were in the definitive stage of a prosthesis fitting process.

Interventions

Not applicable.

Main Outcome Measures

A patient-reported outcome measure, the Disabilities of the Arm, Shoulder, and Hand questionnaire (DASH), and 2 performance-based outcome measures, Box and Blocks Test (BBT) and Capacity Assessment of Prosthesis Performance for the Upper Limb (CAPPFUL), were used as variables in multiple linear regression models.

Results

The multiple linear regression models, which controlled for prosthesis type and amputation level, did not show evidence that changes in the independent variable (DASH) are significantly associated with changes in the dependent variables (log(BBT) (B=−0.007; 95% confidence interval [CI], −0.015 to 0.001; P=.0937) and CAPPFUL (B=−0.083, 95% CI, −0.374 to 0.208; P=.5623)). In both models, individuals with elbow, transhumeral (above elbow), and shoulder disarticulation showed a significant negative association with the dependent variable (CAPPFUL or logBBT). In the CAPPFUL model, there was a significant negative association with individuals using a hybrid prosthesis (B=−20.252; 95% CI, −36.562 to −3.942; P=.0170). In the logBBT model, there was a significant positive association with individuals using body-powered prostheses (B=0.430; 95% CI, 0.089-0.771; P=.0157).

Conclusions

Although additional data and analyses are needed to more completely assess the association between self-reported measures and performance-based measures of functional abilities, these preliminary results indicate that patient-reported outcomes alone may not provide a complete assessment of an upper limb prosthesis users’ functional ability and should be accompanied by population-specific performance-based measures.

A multi-channel peripheral nerve stimulator with integrate-and-fire encoding

Activation of peripheral nervous system (PNS) fibres to produce variable tactile and proprioceptive sensations in advanced bidirectional prosthetic limbs relies on neural stimulators with high spatial selectivity, dynamic range and resolution. A multi-channel application-specific integrated circuit (ASIC) is developed for PNS fibre activation using a wide dynamic range (10 nA-5 mA), high-resolution (30 nA step, 100 ns pulse accuracy) current stimulator, dissipating 0.73-2.75 mW at 3 V. The ASIC also enables encoding of external pressure signals via an integrate-and-fire methodology. Electrophysiological data of compound nerve action potentials were recorded for a range of stimulus amplitudes and pulse widths. This data was used to benchmark the performance of the ASIC with a known neural stimulator.

The rubber hand illusion is a fallible method to study ownership of prosthetic limbs

Enabling sensory feedback in limb prostheses can reverse a damaged body image caused by amputation. The rubber hand illusion (RHI) is a popular paradigm to study ownership of artificial limbs and potentially useful to assess sensory feedback strategies. We investigated the RHI as means to induce ownership of a prosthetic hand by providing congruent visual and tactile stimuli. We elicited tactile sensations via electric stimulation of severed afferent nerve fibres in four participants with transhumeral amputation. Contrary to our expectations, they failed to experience the RHI. The sensations we elicited via nerve stimulation resemble tapping as opposed to stroking, as in the original RHI. We therefore investigated the effect of tapping versus stroking in 30 able-bodied subjects. We found that either tactile modality equally induced ownership in two-thirds of the subjects. Failure to induce the RHI in the intact hand of our participants with amputation later confirmed that they form part of the RHI-immune population. Conversely, these participants use neuromusculoskeletal prostheses with neural sensory feedback in their daily lives and reported said prostheses as part of their body. Our findings suggest that people immune to the RHI can nevertheless experience ownership over prosthetic limbs when used in daily life and accentuates a significant limitation of the RHI paradigm.

Mechanical evaluation of the "Hüfner hand" prosthesis

BACKGROUND

Current body-powered hands have very low acceptance rates. They also require high activation forces. In the past, a high acceptance rate was reported for the then-available Hüfner hand, a hand which could be controlled by relatively low activation forces.

OBJECTIVE

The aim of this study was to measure and quantify the mechanical performance of the Hüfner hand.

STUDY DESIGN

Mechanical evaluation.

METHODS

Two versions of the Hüfner hand were tested using a mechanical test bench. Forces and displacements were measured under four different glove conditions (no glove, leather, polyvinyl chloride (PVC), silicone). The measured results were compared to data from currently available voluntary-closing hands.

RESULTS

The Hüfner hand required 132-170 Nmm of work and 78-104 N cable force to pinch 15 N. The overall mechanical performance of the Hüfner hands is better than currently available body-powered hands.

CONCLUSION

The mechanical performance of the Hüfner hand was measured and quantified. Mechanical testing results show that the Hüfner hand has better mechanical performance than current body-powered hands. This may have contributed to its reported high acceptance rates. The design of the Hüfner hand, combined with data presented in this study, can serve as guidelines for the design of a new generation of body-powered hands.

Chronic Use of a Sensitized Bionic Hand Does Not Remap the Sense of Touch

Electrical stimulation of tactile nerve fibers that innervated an amputated hand results in vivid sensations experienced at a specific location on the phantom hand, a phenomenon that can be leveraged to convey tactile feedback through bionic hands. Ideally, electrically evoked sensations would be experienced on the appropriate part of the hand: touch with the bionic index fingertip, for example, would elicit a sensation experienced on the index fingertip. However, the perceived locations of sensations are determined by the idiosyncratic position of the stimulating electrode in the nerve and thus are difficult to predict or control. This problem could be circumvented if perceived sensations shifted over time to become consistent with the position of the sensor that triggers them. We show that, after long-term use of a neuromusculoskeletal prosthesis that featured a mismatch between the sensor location and the resulting tactile experience, the perceived location of the touch did not change.

Lower limb prosthetic interfaces: Clinical and technological advancement and potential future direction

The human-prosthesis interface is one of the most complicated challenges facing the field of prosthetics, despite substantive investments in research and development by researchers and clinicians around the world. The journal of the International Society for Prosthetics and Orthotics, Prosthetics and Orthotics International, has contributed substantively to the growing body of knowledge on this topic. In celebrating the 50th anniversary of the International Society for Prosthetics and Orthotics, this narrative review aims to explore how human-prosthesis interfaces have changed over the last five decades; how research has contributed to an understanding of interface mechanics; how clinical practice has been informed as a result; and what might be potential future directions. Studies reporting on comparison, design, manufacturing and evaluation of lower limb prosthetic sockets, and osseointegration were considered. This review demonstrates that, over the last 50 years, clinical research has improved our understanding of socket designs and their effects; however, high-quality research is still needed. In particular, there have been advances in the development of volume and thermal control mechanisms with a few designs having the potential for clinical application. Similarly, advances in sensing technology, soft tissue quantification techniques, computing technology, and additive manufacturing are moving towards enabling automated, data-driven manufacturing of sockets. In people who are unable to use a prosthetic socket, osseointegration provides a functional solution not available 50 years ago. Furthermore, osseointegration has the potential to facilitate neuromuscular integration. Despite these advances, further improvement in mechanical features of implants, and infection control and prevention are needed.

Exploring augmented grasping capabilities in a multi-synergistic soft bionic hand

BACKGROUND

State-of-the-art bionic hands incorporate hi-tech devices which try to overcome limitations of conventional single grip systems. Unfortunately, their complexity often limits mechanical robustness and intuitive prosthesis control. Recently, the translation of neuroscientific theories (i.e. postural synergies) in software and hardware architecture of artificial devices is opening new approaches for the design and control of upper-limb prostheses.

METHODS

Following these emerging principles, previous research on the SoftHand Pro, which embeds one physical synergy, showed promising results in terms of intuitiveness, robustness, and grasping performance. To explore these principles also in hands with augmented capabilities, this paper describes the SoftHand 2 Pro, a second generation of the device with 19 degrees-of-freedom and a second synergistic layer. After a description of the proposed device, the work explores a continuous switching control method based on a myoelectric pattern recognition classifier.

RESULTS

The combined system was validated using standardized assessments with able-bodied and, for the first time, amputee subjects. Results show an average improvement of more than 30% of fine grasp capabilities and about 10% of hand function compared with the first generation SoftHand Pro.

CONCLUSIONS

Encouraging results suggest how this approach could be a viable way towards the design of more natural, reliable, and intuitive dexterous hands.

Guiding the Training of Users With a Pattern Similarity Biofeedback to Improve the Performance of Myoelectric Pattern Recognition

Next generation prosthetics will rely massively on myoelectric "Pattern Recognition" (PR) based control approaches, to improve their users' dexterity. One major identified factor of successful functioning of these approaches lies in the training of amputees and in their understanding of how those prosthetics works. We thus propose here an intuitive pattern similarity biofeedback which can be easily used to train amputees and allow them to optimize their muscular contractions to improve their control performance. Experiments were conducted on twenty able-bodied participants and one transradial amputee. Their performance in controlling an interface through a myoelectric PR algorithm was evaluated; before and after a short automatic user training session consisting in using the proposed visual biofeedback for ten participants, and using a generic PR algorithm output feedback for the others ten. Participants who were trained with the proposed biofeedback increased their classification score for the retrained gesture (by 39.4%), without affecting the overall classification performance (which progressed by 10.2%) through over-training and increase of False Positive rate as observed in the control group. Additional analysis indicates a clear change in contraction strategy only in the group who used the proposed biofeedback. These preliminary results highlight the potential of this method which does not focus so much on over-optimizing the pattern recognition algorithm or on physically training the users, but on providing them simple and intuitive information to adapt or change their motor strategies to solve some misclassification issues.

A 3-Mbps, 802.11g-Based EMG Recording System With Fully Implantable 5-Electrode EMG Acquisition Device

We have developed a 5-electrode recording system that combines an implantable electromyography (EMG) device package with transcutaneous inductive power transmission, near-infrared (NIR) transcutaneous data telemetry and 3 Mbps Wi-Fi data acquisition for chronic EMG recording in vivo. This system comprises a hermetically-sealed single-chip, 5-electrode Implantable EMG Acquisition Device (IEAD), a custom external powering and Implant Telemetry Module (ITM), and a custom Wi-Fi-based Raspberry Pi-based Data Acquisition (RaspDAQ) and relay device. The external unit (ITM and RaspDAQ) is powered entirely by a single battery to achieve the objective of untethered EMG recording, for the convenience of clinicians and animal researchers. The IEAD acquires intramuscular EMG signals at 17.85 ksps/electrode while being powered transcutaneously by the ITM using 22 MHz near-field inductive coupling. The acquired EMG data is transmitted transcutaneously via NIR telemetry to the ITM, which in turn, transfers the data to the RaspDAQ for relaying to a laptop computer for display and storage. We have also validated the complete system by acquiring EMG signals from rodents for up to two months. Following the explantation of the devices, we have also conducted failure and histological analysis on the devices and the surrounding tissue, respectively.

Neuromusculoskeletal Arm Prostheses: Personal and Social Implications of Living With an Intimately Integrated Bionic Arm

People with limb loss are for the first time living chronically and uninterruptedly with intimately integrated neuromusculoskeletal prostheses. This new generation of artificial limbs are fixated to the skeleton and operated by bidirectionally transferred neural information. This unprecedented level of human-machine integration is bound to have profound psychosocial effects on the individuals living with these prostheses. Here, we examined the psychosociological impact on people as they integrate neuromusculoskeletal prostheses into their bodies and lives. Three people with transhumeral amputations participated in this study, all of whom had been living with neuromusculoskeletal prostheses in their daily lives between 2 and 6 years at the time of the interview. Direct neural sensory feedback had been enabled for 6 months to 2 years. Participants were interviewed about their experiences living with the neuromusculoskeletal prostheses in their home and professional daily lives. We analyzed these interviews to elucidate themes using an interpretive phenomenological approach that regards participants' own experiences as forms of expertise and knowledge-making. Our participant-generated results indicate that people adapted and integrated the technology into functional and social arenas of daily living, with positive psychosocial effects on self-esteem, self-image, and social relations intimately linked to improved trust of the prostheses. Participants expressed enhanced prosthetic function, increased and more diverse prosthesis use in tasks of daily living, and improved relationships between their prosthesis and phantom limb. Our interviews with patients also generated critiques of the language commonly used to describe human-prosthetic relations, including terms such as "embodiment," and the need for specificity surrounding the term "natural" with regard to control versus sensory feedback. Experiences living with neuromusculoskeletal prostheses were complex and subject-dependent, and therefore future research should consider human-machine interaction as a relationship that is constantly enacted, negotiated, and deeply contextualized.

Neural feedback strategies to improve grasping coordination in neuromusculoskeletal prostheses

Conventional prosthetic arms suffer from poor controllability and lack of sensory feedback. Owing to the absence of tactile sensory information, prosthetic users must rely on incidental visual and auditory cues. In this study, we investigated the effect of providing tactile perception on motor coordination during routine grasping and grasping under uncertainty. Three transhumeral amputees were implanted with an osseointegrated percutaneous implant system for direct skeletal attachment and bidirectional communication with implanted neuromuscular electrodes. This neuromusculoskeletal prosthesis is a novel concept of artificial limb replacement that allows to extract control signals from electrodes implanted on viable muscle tissue, and to stimulate severed afferent nerve fibers to provide somatosensory feedback. Subjects received tactile feedback using three biologically inspired stimulation paradigms while performing a pick and lift test. The grasped object was instrumented to record grasping and lifting forces and its weight was either constant or unexpectedly changed in between trials. The results were also compared to the no-feedback control condition. Our findings confirm, in line with the neuroscientific literature, that somatosensory feedback is necessary for motor coordination during grasping. Our results also indicate that feedback is more relevant under uncertainty, and its effectiveness can be influenced by the selected neuromodulation paradigm and arguably also the prior experience of the prosthesis user.

Self-Contained Neuromusculoskeletal Arm Prostheses

We report the use of a bone-anchored, self-contained robotic arm with both sensory and motor components over 3 to 7 years in four patients after transhumeral amputation. The implant allowed for bidirectional communication between a prosthetic hand and electrodes implanted in the nerves and muscles of the upper arm and was anchored to the humerus through osseointegration, the process in which bone cells attach to an artificial surface without formation of fibrous tissue. Use of the device did not require formal training and depended on the intuitive intent of the user to activate movement and sensory feedback from the prosthesis. Daily use resulted in increasing sensory acuity and effectiveness in work and other activities of daily life. (Funded by the Promobilia Foundation and others.).

Myoelectric signals and pattern recognition from implanted electrodes in two TMR subjects with an osseointegrated communication interface

Permanent implantation of electrodes for prosthetic control is now possible using an osseointegrated implant as a long-term stable communication interface (e-OPRA). The number of myoelectric sites to host such electrodes can be increased by Targeted Muscle Reinnervation (TMR). Traditionally, patients need to wait several months before the TMR signals are strong enough to be recorded by electrodes placed over the skin. In this study, we report the evolution of the TMR myoelectric signals recorded from two subjects via implanted electrodes using e-OPRA, and monitored for up to 48 weeks after surgery. The signals were analyzed with regard to amplitude (signal-to-noise ratio), independence (cross-correlation) and myoelectric pattern recognition (classification accuracy). TMR signals appeared at the first follow-up, one month post-surgery, and developed around 20 dB by the last. Cross-correlation between signals decreased over time and converged to a few percentage points. Classification accuracies were over 97% by the last follow up. These preliminary results suggest that implanted electrodes via the e-OPRA interface allow for an earlier and more effective use of motor signals from TMR sites compared to conventional skin surface electrodes.

Continuous Description of Human 3D Motion Intent Through Switching Mechanism

Post-stroke motor recovery highly relies on voluntarily participating in active rehabilitation as early as possible for promoting the reorganization of the patient's brain. In this paper, a new method is proposed which manipulates cable-based rehabilitation robots to assist multi-joint body motions. This uses an electromyography (EMG) decoder for continuous estimation of voluntary motion intention to establish a cooperative human-machine interface for promoting the participation in rehabilitation exercises. In particular, for multi-joint complex tasks in three-dimensional space, a switching mechanism has been developed which can carve up tasks into separate simple motions. For each simple motion, a linear six-inputs and three-outputs time-invariant model is established respectively. The inputs are the processed muscle activations of six arm muscles, and the outputs are voluntary forces of participants when executing a multi-directional tracking task with visual feedback. The experiments for examining the decoder model and EMG-based controller include model training, testing and controller application phases with seven healthy participants. Experimental results demonstrate that the decoder model with the switching mechanism could effectively recognize arm movement intention and provide appropriate assistance to the participants. This study finds that the switching mechanism can improve both the model estimation accuracy and the completeness for executing complex tasks.

A Python-based laboratory course for image and video signal processing on embedded systems

The usage of embedded systems is omnipresent in our everyday life, e.g., in smartphones, tablets, or automotive devices. These devices are able to deal with challenging image processing tasks like real-time detection of faces or high dynamic range imaging. However, the size and computational power of an embedded system is a limiting demand. To help students understanding these challenges, a new lab course "Image and Video Signal Processing on Embedded Systems" has been developed and is presented in this paper. The Raspberry Pi 3 Model B and the open source programming language Python have been chosen, because of low hardware cost and free availability of the programming language. In this lab course the students learn handling both hard- and software, Python as an alternative to MATLAB, the image signal processing path, and how to develop an embedded image processing system, from the idea to implementation and debugging. At the beginning of the lab course an introduction to Python and the Raspberry Pi is given. After that, various experiments like the implementation of a corner detector and creation of a panorama image are prepared in the lab course. Students participating in the lab course develop a profound understanding of embedded image and video processing algorithms which is verified by comparing questionnaires at the beginning and the end of the lab course. Moreover, compared to a peer group attending an accompanying lecture with exercises, students having participated in this lab course outperform their peer group in the exam for the lecture by 0.5 on a five-point scale.

Biomimetic sensory feedback through peripheral nerve stimulation improves dexterous use of a bionic hand

We describe use of a bidirectional neuromyoelectric prosthetic hand that conveys biomimetic sensory feedback. Electromyographic recordings from residual arm muscles were decoded to provide independent and proportional control of a six-DOF prosthetic hand and wrist-the DEKA LUKE arm. Activation of contact sensors on the prosthesis resulted in intraneural microstimulation of residual sensory nerve fibers through chronically implanted Utah Slanted Electrode Arrays, thereby evoking tactile percepts on the phantom hand. With sensory feedback enabled, the participant exhibited greater precision in grip force and was better able to handle fragile objects. With active exploration, the participant was also able to distinguish between small and large objects and between soft and hard ones. When the sensory feedback was biomimetic-designed to mimic natural sensory signals-the participant was able to identify the objects significantly faster than with the use of traditional encoding algorithms that depended on only the present stimulus intensity. Thus, artificial touch can be sculpted by patterning the sensory feedback, and biologically inspired patterns elicit more interpretable and useful percepts.

Grip control and motor coordination with implanted and surface electrodes while grasping with an osseointegrated prosthetic hand

Background

Replacement of a lost limb by an artificial substitute is not yet ideal. Resolution and coordination of motor control approximating that of a biological limb could dramatically improve the functionality of prosthetic devices, and thus reduce the gap towards a suitable limb replacement.

Methods

In this study, we investigated the control resolution and coordination exhibited by subjects with transhumeral amputation who were implanted with epimysial electrodes and an osseointegrated interface that provides bidirectional communication in addition to skeletal attachment (e-OPRA Implant System). We assessed control resolution and coordination in the context of routine and delicate grasping using the Pick and Lift and the Virtual Eggs Tests. Performance when utilizing implanted electrodes was compared with the standard-of-care technology for myoelectric prostheses, namely surface electrodes.

Results

Results showed that implanted electrodes provide superior controllability over the prosthetic terminal device compared to conventional surface electrodes. Significant improvements were found in the control of the grip force and its reliability during object transfer. However, these improvements failed to increase motor coordination, and surprisingly decreased the temporal correlation between grip and load forces observed with surface electrodes. We found that despite being more functional and reliable, prosthetic control via implanted electrodes still depended highly on visual feedback.

Conclusions

Our findings indicate that incidental sensory feedback (visual, auditory, and osseoperceptive in this case) is insufficient for restoring natural grasp behavior in amputees, and support the idea that supplemental tactile sensory feedback is needed to learn and maintain the motor tasks internal model, which could ultimately restore natural grasp behavior in subjects using prosthetic hands.

Analysis of Man-Machine Interfaces in Upper-Limb Prosthesis: A Review

This paper compiles and analyzes some of the most current works related to upper limb prosthesis with emphasis on man-machine interfaces. A brief introduction of the basic subjects is given to explain what a prosthesis is, what types of prostheses exist, what they serve for, how they communicate with the user (control and feedback), and what technologies are involved. The method used in this review is also discussed, as well as the cataloging process and analysis of articles for the composition of this review. Each article is analyzed individually and its results are presented in a succinct way, in order to facilitate future research and serve as a source for professionals related to the area of prosthesis, such as doctors, engineers, researchers, and anyone interested in this subject. Finally, the needs and difficulties of the current prostheses, as well as the negative and positive points in the results are analyzed, and the progress achieved so far is discussed.

Safety of long-term electrical peripheral nerve stimulation: review of the state of the art

BACKGROUND

Electrical stimulation of peripheral nerves is used in a variety of applications such as restoring motor function in paralyzed limbs, and more recently, as means to provide intuitive sensory feedback in limb prostheses. However, literature on the safety requirements for stimulation is scarce, particularly for chronic applications. Some aspects of nerve interfacing such as the effect of stimulation parameters on electrochemical processes and charge limitations have been reviewed, but often only for applications in the central nervous system. This review focuses on the safety of electrical stimulation of peripheral nerve in humans.

METHODS

We analyzed early animal studies evaluating damage thresholds, as well as more recent investigations in humans. Safety requirements were divided into two main categories: passive and active safety. We made the distinction between short-term (< 30 days) and chronic (> 30 days) applications, as well as between electrode preservation (biostability) and body tissue healthy survival (harmlessness). In addition, transferability of experimental results between different tissues and species was considered.

RESULTS

At present, extraneural electrodes have shown superior long-term stability in comparison to intraneural electrodes. Safety limitations on pulse amplitude (and consequently, charge injection) are dependent on geometrical factors such as electrode placement, size, and proximity to the stimulated fiber. In contrast, other parameters such as stimulation frequency and percentage of effective stimulation time are more generally applicable. Currently, chronic stimulation at frequencies below 30 Hz and percentages of effective stimulation time below 50% is considered safe, but more precise data drawn from large databases are necessary. Unfortunately, stimulation protocols are not systematically documented in the literature, which limits the feasibility of meta-analysis and impedes the generalization of conclusions. We therefore propose a standardized list of parameters necessary to define electrical stimulation and allow future studies to contribute to meta-analyses.

CONCLUSION

The safety of chronic continuous peripheral nerve stimulation at frequencies higher than 30 Hz has yet to be documented. Precise parameter values leading to stimulation-induced depression of neuronal excitability (SIDNE) and neuronal damage, as well as the transition between the two, are still lacking. At present, neural damage mechanisms through electrical stimulation remain obscure.

Phantom-Mobility-Based Prosthesis Control in Transhumeral Amputees Without Surgical Reinnervation: A Preliminary Study

Transhumeral amputees face substantial difficulties in efficiently controlling their prosthetic limb, leading to a high rate of rejection of these devices. Actual myoelectric control approaches make their use slow, sequential and unnatural, especially for these patients with a high level of amputation who need a prosthesis with numerous active degrees of freedom (powered elbow, wrist, and hand). While surgical muscle-reinnervation is becoming a generic solution for amputees to increase their control capabilities over a prosthesis, research is still being conducted on the possibility of using the surface myoelectric patterns specifically associated to voluntary Phantom Limb Mobilization (PLM), appearing naturally in most upper-limb amputees without requiring specific surgery. The objective of this study was to evaluate the possibility for transhumeral amputees to use a PLM-based control approach to perform more realistic functional grasping tasks. Two transhumeral amputated participants were asked to repetitively grasp one out of three different objects with an unworn eight-active-DoF prosthetic arm and release it in a dedicated drawer. The prosthesis control was based on phantom limb mobilization and myoelectric pattern recognition techniques, using only two repetitions of each PLM to train the classification architecture. The results show that the task could be successfully achieved with rather optimal strategies and joint trajectories, even if the completion time was increased in comparison with the performances obtained by a control group using a simple GUI control, and the control strategies required numerous corrections. While numerous limitations related to robustness of pattern recognition techniques and to the perturbations generated by actual wearing of the prosthesis remain to be solved, these preliminary results encourage further exploration and deeper understanding of the phenomenon of natural residual myoelectric activity related to PLM, since it could possibly be a viable option in some transhumeral amputees to extend their control abilities of functional upper limb prosthetics with multiple active joints without undergoing muscular reinnervation surgery.

Multiday Evaluation of Techniques for EMG-Based Classification of Hand Motions

Currently, most of the adopted myoelectric schemes for upper limb prostheses do not provide users with intuitive control. Higher accuracies have been reported using different classification algorithms but investigation on the reliability over time for these methods is very limited. In this study, we compared for the first time the longitudinal performance of selected state-of-the-art techniques for electromyography (EMG) based classification of hand motions. Experiments were conducted on ten able-bodied and six transradial amputees for seven continuous days. Linear discriminant analysis (LDA), artificial neural network (ANN), support vector machine (SVM), K-nearest neighbor (KNN), and decision trees (TREE) were compared. Comparative analysis showed that the ANN attained highest classification accuracy followed by LDA. Three-way repeated ANOVA test showed a significant difference (P < 0.001) between EMG types (surface, intramuscular, and combined), days (1-7), classifiers, and their interactions. Performance on the last day was significantly better (P < 0.05) than the first day for all classifiers and EMG types. Within-day, classification error (WCE) across all subject and days in ANN was: surface (9.12 ± 7.38%), intramuscular (11.86 ± 7.84%), and combined (6.11 ± 7.46%). The between-day analysis in a leave-one-day-out fashion showed that the ANN was the optimal classifier (surface (21.88 ± 4.14%), intramuscular (29.33 ± 2.58%), and combined (14.37 ± 3.10%). Results indicate that within day performances of classifiers may be similar but over time, it may lead to a substantially different outcome. Furthermore, training ANN on multiple days might allow capturing time-dependent variability in the EMG signals and thus minimizing the necessity for daily system recalibration.

Characterizing the SEMG patterns with myofascial pain using a multi-scale wavelet model through machine learning approaches

In this paper, we introduce a newly developed multi-scale wavelet model for the interpretation of surface electromyography (SEMG) signals and validate the model's capability to characterize changes in neuromuscular activation in cases with myofascial pain syndrome (MPS) via machine learning methods. The SEMG data collected from normal (N = 30; 27 women, 3 men) and MPS subjects (N = 26; 22 women, 4 men) were adopted for this retrospective analysis. SMEGs were measured from the taut-band loci on both sides of the trapezius muscle on the upper back while he/she conducted a cyclic bilateral backward shoulder extension movement within 1 min. Classification accuracy of the SEMG model to differentiate MPS patients from normal subjects was 77% using template matching and 60% using K-means clustering. Classification consistency between the two machine learning methods was 87% in the normal group and 93% in the MPS group. The 2D feature graphs derived from the proposed multi-scale model revealed distinct patterns between normal subjects and MPS patients. The classification consistency using template matching and K-means clustering suggests the potential of using the proposed model to characterize interference pattern changes induced by MPS.

Case Studies in Neuroscience: Sensations elicited and discrimination ability from nerve cuff stimulation in an amputee over time

The present case study details sensations elicited by electrical stimulation of peripheral nerve axons using an implanted nerve cuff electrode, in a participant with a transhumeral amputation. The participant uses an osseointegrated electromechanical interface, which enables skeletal attachment of the prosthesis and long-term, stable, bidirectional communication between the implanted electrodes and prosthetic arm. We focused on evoking somatosensory percepts, where we tracked and quantified the evolution of perceived sensations in the missing hand, which were evoked from electrical stimulation of the nerve, for over 2 yr. These sensations included small, pointlike areas of either vibration or pushing, to larger sensations over wider areas, indicating the recruitment of a few and many afferents, respectively. Furthermore, we used a two-alternative forced choice paradigm to measure the level of discrimination between trains of brief electrical stimuli, to gauge what the participant could reliably distinguish between. At best, the participant was able to distinguish a 0.5-Hz difference and on average acquired a 3.8-Hz just-noticeable difference at a more stringent psychophysical level. The current work shows the feasibility for long-term sensory feedback in prostheses, via electrical axonal stimulation, where small and relatively stable percepts were felt that may be used to deliver graded sensory feedback. This opens up opportunities for signaling feedback during movements (e.g., for precision grip), but also for conveying more complex cutaneous sensations, such as texture. NEW & NOTEWORTHY We demonstrate the long-term stability and generation of sensations from electrical peripheral nerve stimulation in an amputee, through an osseointegrated implant. We find that perceived tactilelike sensations could be generated for over 2 yr, in the missing hand. This is useful for prosthetic development and the implementation of feedback in artificial body parts.

Assessing the quality of supplementary sensory feedback using the crossmodal congruency task

Advanced neural interfaces show promise in making prosthetic limbs more biomimetic and ultimately more intuitive and useful for patients. However, approaches to assess these emerging technologies are limited in scope and the insight they provide. When outfitting a prosthesis with a feedback system, such as a peripheral nerve interface, it would be helpful to quantify its physiological correspondence, i.e. how well the prosthesis feedback mimics the perceived feedback in an intact limb. Here we present an approach to quantify this aspect of feedback quality using the crossmodal congruency effect (CCE) task. We show that CCE scores are sensitive to feedback modality, an important characteristic for assessment purposes, but are confounded by the spatial separation between the expected and perceived location of a stimulus. Using data collected from 60 able-bodied participants trained to control a bypass prosthesis, we present a model that results in adjusted-CCE scores that are unaffected by percept misalignment which may result from imprecise neural stimulation. The adjusted-CCE score serves as a proxy for a feedback modality's physiological correspondence or 'naturalness'. This quantification approach gives researchers a tool to assess an aspect of emerging augmented feedback systems that is not measurable with current motor assessments.

An Alternative Myoelectric Pattern Recognition Approach for the Control of Hand Prostheses: A Case Study of Use in Daily Life by a Dysmelia Subject

The functionality of upper limb prostheses can be improved by intuitive control strategies that use bioelectric signals measured at the stump level. One such strategy is the decoding of motor volition via myoelectric pattern recognition (MPR), which has shown promising results in controlled environments and more recently in clinical practice. Moreover, not much has been reported about daily life implementation and real-time accuracy of these decoding algorithms. This paper introduces an alternative approach in which MPR allows intuitive control of four different grips and open/close in a multifunctional prosthetic hand. We conducted a clinical proof-of-concept in activities of daily life by constructing a self-contained, MPR-controlled, transradial prosthetic system provided with a novel user interface meant to log errors during real-time operation. The system was used for five days by a unilateral dysmelia subject whose hand had never developed, and who nevertheless learned to generate patterns of myoelectric activity, reported as intuitive, for multi-functional prosthetic control. The subject was instructed to manually log errors when they occurred via the user interface mounted on the prosthesis. This allowed the collection of information about prosthesis usage and real-time classification accuracy. The assessment of capacity for myoelectric control test was used to compare the proposed approach to the conventional prosthetic control approach, direct control. Regarding the MPR approach, the subject reported a more intuitive control when selecting the different grips, but also a higher uncertainty during proportional continuous movements. This paper represents an alternative to the conventional use of MPR, and this alternative may be particularly suitable for a certain type of amputee patients. Moreover, it represents a further validation of MPR with dysmelia cases.

An Embedded, Eight Channel, Noise Canceling, Wireless, Wearable sEMG Data Acquisition System With Adaptive Muscle Contraction Detection

Wearable technology has gained increasing popularity in the applications of healthcare, sports science, and biomedical engineering in recent years. Because of its convenient nature, the wearable technology is particularly useful in the acquisition of the physiological signals. Specifically, the (surface electromyography) sEMG systems, which measure the muscle activation potentials, greatly benefit from this technology in both clinical and industrial applications. However, the current wearable sEMG systems have several drawbacks including inefficient noise cancellation, insufficient measurement quality, and difficult integration to customized applications. Additionally, none of these sEMG data acquisition systems can detect sEMG signals (i.e., contractions), which provides a valuable environment for further studies such as human machine interaction, gesture recognition, and fatigue tracking. To this end, we introduce an embedded, eight channel, noise canceling, wireless, wearable sEMG data acquisition system with adaptive muscle contraction detection. Our design consists of two stages, which are the sEMG sensors and the multichannel data acquisition unit. For the first stage, we propose a low cost, dry, and active sEMG sensor that captures the muscle activation potentials, a data acquisition unit that evaluates these captured multichannel sEMG signals and transmits them to a user interface. In the data acquisition unit, the sEMG signals are processed through embedded, adaptive methods in order to reject the power line noise and detect the muscle contractions. Through extensive experiments, we demonstrate that our sEMG sensor outperforms a widely used commercially available product and our data acquisition system achieves 4.583 dB SNR gain with accuracy in the detection of the contractions.