Neuromimetic Event-Based Detection for Closed-Loop Tactile Feedback Control of Upper Limb Prostheses

Venus Flytrap-Inspired Data-Center-Free Fast-Responsive Soft Robots Enabled by 2D Ni3(HITP)2 MOF and Graphite

The rapid and responsive capabilities of soft robots in perceiving, assessing, and reacting to environmental stimuli are highly valuable. However, many existing soft robots, designed to mimic humans and other higher animals, often rely on data centers for the modulation of mechanoelectrical transduction and electromechanical actuation. This reliance significantly increases system complexity and time delays. Herein, drawing inspiration from Venus flytraps, a soft robot employing a power modulation strategy is presented for active stimulus reaction, eliminating the need for a data center. This robot achieves mechanoelectrical transduction through Ni3(2,3,6,7,10,11-hexaiminotriphenylene)2 (Ni3(HITP)2) metal-organic framework (MOF) with an ultralow time delay (256 ns) and electromechanical actuation via graphite. The Joule heating effect in graphite is effectively modulated by Ni3(HITP)2 before and after the presence of pressure, thus enabling the stimulus reaction of soft robots. As demonstrated, three soft robots are created: low-level edge tongue robots, Venus flytrap robots, and high-level nerve-center-controlled dragonfly robots. This power modulation strategy inspires designs of edge soft robots and high-level robots with a human-like effective fusion of conditioned and unconditioned reflexes.

A hybrid sensory feedback system for thermal nociceptive warning and protection in prosthetic hand

Background

Advanced prosthetic hands may embed nanosensors and microelectronics in their cosmetic skin. Heat influx may cause damage to these delicate structures. Protecting the integrity of the prosthetic hand becomes critical and necessary to ensure sustainable function. This study aims to mimic the sensorimotor control strategy of the human hand in perceiving nociceptive stimuli and triggering self-protective mechanisms and to investigate how similar neuromorphic mechanisms implemented in prosthetic hand can allow amputees to both volitionally release a hot object upon a nociceptive warning and achieve reinforced release via a bionic withdrawal reflex.

Methods

A steady-state temperature prediction algorithm was proposed to shorten the long response time of a thermosensitive temperature sensor. A hybrid sensory strategy for transmitting force and a nociceptive temperature warning using transcutaneous electrical nerve stimulation based on evoked tactile sensations was designed to reconstruct the nociceptive sensory loop for amputees. A bionic withdrawal reflex using neuromorphic muscle control technology was used so that the prosthetic hand reflexively opened when a harmful temperature was detected. Four able-bodied subjects and two forearm amputees randomly grasped a tube at the different temperatures based on these strategies.

Results

The average prediction error of temperature prediction algorithm was 8.30 ± 6.00%. The average success rate of six subjects in perceiving force and nociceptive temperature warnings was 86.90 and 94.30%, respectively. Under the reinforcement control mode in Test 2, the median reaction time of all subjects was 1.39 s, which was significantly faster than the median reaction time of 1.93 s in Test 1, in which two able-bodied subjects and two amputees participated. Results demonstrated the effectiveness of the integration of nociceptive sensory strategy and withdrawal reflex control strategy in a closed loop and also showed that amputees restored the warning of nociceptive sensation while also being able to withdraw from thermal danger through both voluntary and reflexive protection.

Conclusion

This study demonstrated that it is feasible to restore the sensorimotor ability of amputees to warn and react against thermal nociceptive stimuli. Results further showed that the voluntary release and withdrawal reflex can work together to reinforce heat protection. Nevertheless, fusing voluntary and reflex functions for prosthetic performance in activities of daily living awaits a more cogent strategy in sensorimotor control.

Neuromorphic hardware for somatosensory neuroprostheses

In individuals with sensory-motor impairments, missing limb functions can be restored using neuroprosthetic devices that directly interface with the nervous system. However, restoring the natural tactile experience through electrical neural stimulation requires complex encoding strategies. Indeed, they are presently limited in effectively conveying or restoring tactile sensations by bandwidth constraints. Neuromorphic technology, which mimics the natural behavior of neurons and synapses, holds promise for replicating the encoding of natural touch, potentially informing neurostimulation design. In this perspective, we propose that incorporating neuromorphic technologies into neuroprostheses could be an effective approach for developing more natural human-machine interfaces, potentially leading to advancements in device performance, acceptability, and embeddability. We also highlight ongoing challenges and the required actions to facilitate the future integration of these advanced technologies.

Neuromorphic applications in medicine

In recent years, there has been a growing demand for miniaturization, low power consumption, quick treatments, and non-invasive clinical strategies in the healthcare industry. To meet these demands, healthcare professionals are seeking new technological paradigms that can improve diagnostic accuracy while ensuring patient compliance. Neuromorphic engineering, which uses neural models in hardware and software to replicate brain-like behaviors, can help usher in a new era of medicine by delivering low power, low latency, small footprint, and high bandwidth solutions. This paper provides an overview of recent neuromorphic advancements in medicine, including medical imaging and cancer diagnosis, processing of biosignals for diagnosis, and biomedical interfaces, such as motor, cognitive, and perception prostheses. For each section, we provide examples of how brain-inspired models can successfully compete with conventional artificial intelligence algorithms, demonstrating the potential of neuromorphic engineering to meet demands and improve patient outcomes. Lastly, we discuss current struggles in fitting neuromorphic hardware with non-neuromorphic technologies and propose potential solutions for future bottlenecks in hardware compatibility.

Flexible Magnetic Skin Sensor Array for Torsion Perception

Prosthetic hands help upper limb amputees and people who were born without hands. Currently, these prostheses are rather rudimentary and do not provide adequate sensing capabilities compared to a human hand. People use their natural hands to perceive complex tactile phenomena such as shear and torsion using thousands of mechanoreceptors in their fingertips. The capability to detect torsional loads at the fingertips is a notable gap in prosthetic hand sensation. Flexible tactile sensors are a promising new technology that would be ideal for prosthetic hands since they allow for stretching and movement like human skin without damage to the sensor. Therefore, the purpose of this study is to determine whether a flexible magnetic sensor array combined with an artificial neural network (ANN) can detect and classify torsion. The flexible magnetic sensor is designed as a 3×3 array of magnets embedded in a stretchable elastomer which are situated atop a corresponding array of Hall effect sensors. Torques applied to the soft magnetic skin caused displacement of the magnetic fields that were perceived by the nine Hall effect sensors. In this study, ten different values of torque were applied to the flexible magnetic sensor array using a robotic arm to ensure consistency. Data were used to train an ANN to classify the applied torques. The ANN was trained ten times and could predict the applied torque with an average training classification accuracy of 97.48% ± 0.33%. Given the results of this study, this novel sensor design could enable more refined sensations of touch for people who use prosthetic hands.

Haptic shared control improves neural efficiency during myoelectric prosthesis use

Clinical myoelectric prostheses lack the sensory feedback and sufficient dexterity required to complete activities of daily living efficiently and accurately. Providing haptic feedback of relevant environmental cues to the user or imbuing the prosthesis with autonomous control authority have been separately shown to improve prosthesis utility. Few studies, however, have investigated the effect of combining these two approaches in a shared control paradigm, and none have evaluated such an approach from the perspective of neural efficiency (the relationship between task performance and mental effort measured directly from the brain). In this work, we analyzed the neural efficiency of 30 non-amputee participants in a grasp-and-lift task of a brittle object. Here, a myoelectric prosthesis featuring vibrotactile feedback of grip force and autonomous control of grasping was compared with a standard myoelectric prosthesis with and without vibrotactile feedback. As a measure of mental effort, we captured the prefrontal cortex activity changes using functional near infrared spectroscopy during the experiment. It was expected that the prosthesis with haptic shared control would improve both task performance and mental effort compared to the standard prosthesis. Results showed that only the haptic shared control system enabled users to achieve high neural efficiency, and that vibrotactile feedback was important for grasping with the appropriate grip force. These results indicate that the haptic shared control system synergistically combines the benefits of haptic feedback and autonomous controllers, and is well-poised to inform such hybrid advancements in myoelectric prosthesis technology.

Object stiffness recognition and vibratory feedback without ad-hoc sensing on the Hannes prosthesis: A machine learning approach

Introduction

In recent years, hand prostheses achieved relevant improvements in term of both motor and functional recovery. However, the rate of devices abandonment, also due to their poor embodiment, is still high. The embodiment defines the integration of an external object - in this case a prosthetic device - into the body scheme of an individual. One of the limiting factors causing lack of embodiment is the absence of a direct interaction between user and environment. Many studies focused on the extraction of tactile information via custom electronic skin technologies coupled with dedicated haptic feedback, though increasing the complexity of the prosthetic system. Contrary wise, this paper stems from the authors' preliminary works on multi-body prosthetic hand modeling and the identification of possible intrinsic information to assess object stiffness during interaction.

Methods

Based on these initial findings, this work presents the design, implementation and clinical validation of a novel real-time stiffness detection strategy, without ad-hoc sensing, based on a Non-linear Logistic Regression (NLR) classifier. This exploits the minimum grasp information available from an under-sensorized and under-actuated myoelectric prosthetic hand, Hannes. The NLR algorithm takes as input motor-side current, encoder position, and reference position of the hand and provides as output a classification of the grasped object (no-object, rigid object, and soft object). This information is then transmitted to the user via vibratory feedback to close the loop between user control and prosthesis interaction. This implementation was validated through a user study conducted both on able bodied subjects and amputees.

Results

The classifier achieved excellent performance in terms of F1Score (94.93%). Further, the able-bodied subjects and amputees were able to successfully detect the objects' stiffness with a F1Score of 94.08% and 86.41%, respectively, by using our proposed feedback strategy. This strategy allowed amputees to quickly recognize the objects' stiffness (response time of 2.82 s), indicating high intuitiveness, and it was overall appreciated as demonstrated by the questionnaire. Furthermore, an embodiment improvement was also obtained as highlighted by the proprioceptive drift toward the prosthesis (0.7 cm).

Full-hand electrotactile feedback using electronic skin and matrix electrodes for high-bandwidth human-machine interfacing

Tactile feedback is relevant in a broad range of human-machine interaction systems (e.g. teleoperation, virtual reality and prosthetics). The available tactile feedback interfaces comprise few sensing and stimulation units, which limits the amount of information conveyed to the user. The present study describes a novel technology that relies on distributed sensing and stimulation to convey comprehensive tactile feedback to the user of a robotic end effector. The system comprises six flexible sensing arrays (57 sensors) integrated on the fingers and palm of a robotic hand, embedded electronics (64 recording channels), a multichannel stimulator and seven flexible electrodes (64 stimulation pads) placed on the volar side of the subject's hand. The system was tested in seven subjects asked to recognize contact positions and identify contact sliding on the electronic skin, using distributed anode configuration (DAC) and single dedicated anode configuration. The experiments demonstrated that DAC resulted in substantially better performance. Using DAC, the system successfully translated the contact patterns into electrotactile profiles that the subjects could recognize with satisfactory accuracy ([Formula: see text] for static and [Formula: see text] for dynamic patterns). The proposed system is an important step towards the development of a high-density human-machine interfacing between the user and a robotic hand. This article is part of the theme issue 'Advanced neurotechnologies: translating innovation for health and well-being'.

Embedded Electrotactile Feedback System for Hand Prostheses Using Matrix Electrode and Electronic Skin

As the technology moves towards more human-like bionic limbs, it is necessary to develop a feedback system that provides active touch feedback to a user of a prosthetic hand. Most of the contemporary sensory substitution methods comprise simple position and force sensors combined with few discrete stimulation units, and hence they are characterized with a limited amount of information that can be transmitted by the feedback. The present study describes a novel system for tactile feedback integrating advanced multipoint sensing (electronic skin) and stimulation (matrix electrodes). The system comprises a flexible sensing array (16 sensors) integrated on the index finger of a Michelangelo prosthetic hand mockup, embedded interface electronics and multichannel stimulator connected to a flexible matrix electrode (24 pads). The developed system conveys contact information (binary detections) to the user. To demonstrate the feasibility, the system was tested in six able-bodied subjects who were asked to recognize static patterns (contact position) with two different spatial resolutions and dynamic movement patterns (i.e., sliding along and/or across the finger) presented on the electronic skin. The experiments demonstrated that the system successfully translated the mechanical interaction into electrotactile profiles, which the subjects could recognize with good performance. The success rates (mean ± standard deviation) for the static patterns were 91 ± 4% and 58 ± 10% for low and high spatial resolution, respectively, while the success rate for sliding touch was 94 ± 4%. These results demonstrate that the developed system is an important step towards a new generation of tactile feedback interfaces that can provide high-bandwidth connection between the user and his/her bionic limb. Such systems would allow mimicking spatially distributed natural feedback, thereby facilitating the control and embodiment of the artificial device into the user body scheme.

Hierarchical Tactile Sensation Integration from Prosthetic Fingertips Enables Multi-Texture Surface Recognition

Multifunctional flexible tactile sensors could be useful to improve the control of prosthetic hands. To that end, highly stretchable liquid metal tactile sensors (LMS) were designed, manufactured via photolithography, and incorporated into the fingertips of a prosthetic hand. Three novel contributions were made with the LMS. First, individual fingertips were used to distinguish between different speeds of sliding contact with different surfaces. Second, differences in surface textures were reliably detected during sliding contact. Third, the capacity for hierarchical tactile sensor integration was demonstrated by using four LMS signals simultaneously to distinguish between ten complex multi-textured surfaces. Four different machine learning algorithms were compared for their successful classification capabilities: K-nearest neighbor (KNN), support vector machine (SVM), random forest (RF), and neural network (NN). The time-frequency features of the LMSs were extracted to train and test the machine learning algorithms. The NN generally performed the best at the speed and texture detection with a single finger and had a 99.2 ± 0.8% accuracy to distinguish between ten different multi-textured surfaces using four LMSs from four fingers simultaneously. The capability for hierarchical multi-finger tactile sensation integration could be useful to provide a higher level of intelligence for artificial hands.

Sonomyography Combined with Vibrotactile Feedback Enables Precise Target Acquisition Without Visual Feedback

Upper limb prosthesis users currently lack haptic feedback from their terminal devices, which significantly limits their ability to meaningfully interact with their environment. Users therefore rely heavily on visual feedback when using terminal devices. Previously, it has been shown that force-related feedback from an end-effector or virtual environment can help the user minimize errors and improve performance. Currently, myoelectric control systems enable the user to control the velocity of terminal devices. We have developed a novel control method using ultrasound sensing, called sonomyography, that enables position control based on mechanical deformation of muscles. In this paper, we investigated whether the proprioceptive feedback from muscle deformation combined with vibrotactile haptic feedback can minimize the need for visual feedback. Able bodied subjects used sonomyography to control a virtual cursor, and performed a target acquisition task. The effect of visual and haptic feedback on performance of a target acquisition task was systematically tested. We found that subjects made large errors when they tried to reacquire a target without visual feedback, but in the presence of real-time haptic feedback, the precision of the target position improved, and were similar to when visual feedback was used for target acquisition. This result has implications for improving the performance of prosthetic control systems.

An Artificial Somatic Reflex Arc

The emulation of human sensation, perception, and action processes has become a major challenge for bioinspired intelligent robotics, interactive human-machine interfacing, and advanced prosthetics. Reflex actions, enabled through reflex arcs, are important for human and higher animals to respond to stimuli from environment without the brain processing and survive the risks of nature. An artificial reflex arc system that emulates the functions of the reflex arc simplifies the complex circuit design needed for "central-control-only" processes and becomes a basic electronic component in an intelligent soft robotics system. An artificial somatic reflex arc that enables the actuation of electrochemical actuators in response to the stimulation of tactile pressures is reported. Only if the detected pressure by the pressure sensor is above the stimulus threshold, the metal-organic-framework-based threshold controlling unit (TCU) can be activated and triggers the electrochemical actuators to complete the motion. Such responding mechanism mimics the all-or-none law in the human nervous system. As a proof of concept, the artificial somatic reflex arc is successfully integrated into a robot to mimic the infant grasp reflex. This work provides a unique and simplifying strategy for developing intelligent soft robotics, next-generation human-machine interfaces, and neuroprosthetics.

Implantable Neural Interfaces and Wearable Tactile Systems for Bidirectional Neuroprosthetics Systems

Neuroprosthetics and neuromodulation represent a promising field for several related applications in the central and peripheral nervous system, such as the treatment of neurological disorders, the control of external robotic devices, and the restoration of lost tactile functions. These actions are allowed by the neural interface, a miniaturized implantable device that most commonly exploits electrical energy to fulfill these operations. A neural interface must be biocompatible, stable over time, low invasive, and highly selective; the challenge is to develop a safe, compact, and reliable tool for clinical applications. In case of anatomical impairments, neuroprosthetics is bound to the need of exploring the surrounding environment by fast-responsive and highly sensitive artificial tactile sensors that mimic the natural sense of touch. Tactile sensors and neural interfaces are closely interconnected since the readouts from the first are required to convey information to the neural implantable apparatus. The role of these devices is pivotal hence technical improvements are essential to ensure a secure system to be eventually adopted in daily life. This review highlights the fundamental criteria for the design and microfabrication of neural interfaces and artificial tactile sensors, their use in clinical applications, and future enhancements for the release of a second generation of devices.

Spatiotemporal characteristics of neural activity in tibial nerves with carbon nanotube yarn electrodes

BACKGROUND

Reliable interfacing with peripheral nervous system is essential to extract neural signals. Current implantable peripheral nerve electrodes cannot provide long-term reliable interfaces due to their mechanical mismatch with host nerves. Carbon nanotube (CNT) yarns possess excellent mechanical flexibility and electrical conductivity. It is of great necessity to investigate the selectivity of implantable CNT yarn electrodes.

NEW METHOD

Neural interfaces were fabricated with CNT yarn electrodes insulated with Parylene-C. Acute recordings were carried out on tibial nerves of rats, and compound nerve action potentials (CNAPs) were electrically evoked by biphasic current stimulation of four toes. Spatiotemporal characteristics of neural activity and spatial selectivity of the electrodes, denoted by selectivity index (SI), were analyzed in detail.

RESULTS

Conduction velocities of sensory afferent fibers recorded by CNT yarn electrodes varied between 4.25 m/s and 37.56 m/s. The SI maxima for specific toes were between 0.55 and 0.99 across seven electrodes. SIs for different CNT yarn electrodes are significantly different among varied toes.

COMPARISON WITH EXISTING METHODS

Most single CNT yarn electrode with a ∼ 500 μm exposed length can be sensitive to one or two specific toes in rodent animals. While, it is only possible to discriminate two non-adjacent toes by multisite TIME electrodes.

CONCLUSION

Single CNT yarn electrode exposed ∼ 500 μm showed SI values for different toes comparable to a multisite TIME electrode, and had high spatial selectivity for one or two specific toes. The electrodes with cross section exposed could intend to be more sensitive to one specific toe.

Computational and Psychophysical Experiments on the Pacinian Corpuscle's Ability to Discriminate Complex Stimuli

Recognizing and discriminating vibrotactile stimuli is an essential function of the Pacinian corpuscle. This function has been studied at length in both a computational and an experimental setting, but the two approaches have rarely been compared, especially when the computational model has a high level of structural detail. In this paper, we explored whether the predictions of a multiscale, multiphysical computational model of the Pacinian corpuscle can predict the outcome of a corresponding psychophysical experiment. The discrimination test involved either two simple stimuli with frequency in the 160-500 Hz range, or two complex stimuli formed by combining the waveforms for a 100-Hz stimulus with a second stimulus in the 160-500 Hz range. The subjects' ability to distinguish between the simple stimuli increased as the frequency increased, a result consistent with the model predictions for the same stimuli. The model also predicted correctly that subjects would find the complex stimuli more difficult to distinguish than the simple ones and also that the discriminability of the complex stimuli would show no trend with frequency difference.

Design and Functional Evaluation of a Dexterous Myoelectric Hand Prosthesis With Biomimetic Tactile Sensor

This paper presents the design, tactile sensor, characterization, and control system of a new dexterous myoelectric hand prosthesis to overcome the limitations of state-of-the-art myoelectric prostheses (e.g., limited functionality, controllability, and sensory feedback). Our dexterous myoelectric hand allows independent finger movement and thumb abduction/adduction, with a motor for each finger and an additional motor for the thumb (i.e., six total motors). Each fingertip has a biomimetic tactile sensor with 13 tactile units, each of which can detect normal and tangential forces. The hand controller uses an electromyography pattern recognition controller and a tactile sensor feedback-based grasping controller to automatically and dynamically adjust the finger grasp force to prevent objects from slipping. This closed-loop controller structure will allow users to safely and effectively grasp complex objects with varying densities and shapes. In addition, the electronic hardware is integrated into the hand, and the pattern recognition controller can be implemented in the hand embedded system.

An adaptive socket with auto-adjusting air bladders for interfacing transhumeral prosthesis: A pilot study

Comfort is a critical aspect in the application of wearable device, such as rehabilitation robots and upper limb prostheses. As a physical interface between human body and prosthetic limb, the socket and its comfort largely contribute to the user's acceptance. Traditional sockets are static, lacking dynamic adjustment mechanism for the contact pressure. To ensure a reliable suspension during daily activities, the socket is usually designed to be tightly attached, with a large stress, on the residual limb, which may introduce considerable discomfort during long-term use. In this article, we present a novel adaptive transhumeral socket, in which we employ four independent bladders contacting with the stump. Not only can these bladders provide a necessary suspension for the device but also form an air cushion (soft body) that helps relieve the pressure concentration between the biological body and physical prosthesis. In real time, this adaptive socket continuously monitors the limb posture, the operating load, and the contacting pressure between the socket and the limb, and then dynamically adjusts the clamping force to ensure a reliable attachment during various daily activities. Since well adapting to the contours of the stump, the bladders can effectively accommodate the volume change of the stump, making a balanced load distribution on load-tolerant areas. Through modeling and numerical analysis, we established a dynamic strategy for estimating the external load and an automatic scheme for adjusting the bladders' air pressure. Finally, a close-loop control was constructed based on the contact pressure measured by our self-developed force sensors. Our preliminary experiments on one normal (i.e. non-amputee) subject verified the effectiveness of the proposed method, showing that the adaptive socket can considerably reduce the socket-limb contact pressure while sustaining a secure suspension on the upper arm.

Neuromorphic vision and tactile fusion for upper limb prosthesis control

A major issue with upper limb prostheses is the disconnect between sensory information perceived by the user and the information perceived by the prosthesis. Advances in prosthetic technology introduced tactile information for monitoring grasping activity, but visual information, a vital component in the human sensory system, is still not fully utilized as a form of feedback to the prosthesis. For able-bodied individuals, many of the decisions for grasping or manipulating an object, such as hand orientation and aperture, are made based on visual information before contact with the object. We show that inclusion of neuromorphic visual information, combined with tactile feedback, improves the ability and efficiency of both able-bodied and amputee subjects to pick up and manipulate everyday objects. We discovered that combining both visual and tactile information in a real-time closed loop feedback strategy generally decreased the completion time of a task involving picking up and manipulating objects compared to using a single modality for feedback. While the full benefit of the combined feedback was partially obscured by experimental inaccuracies of the visual classification system, we demonstrate that this fusion of neuromorphic signals from visual and tactile sensors can provide valuable feedback to a prosthetic arm for enhancing real-time function and usability.

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.

Thumb Amputations Treated With Osseointegrated Percutaneous Prostheses With Up to 25 Years of Follow-up

Introduction

Implantation of an osseointegrated percutaneous prosthesis provides a reconstruction alternative for thumb amputation without sacrificing donor tissues.

Methods

Thirteen thumb amputees received osseointegrated prostheses (1990 to 2014). The treatments were started with custom-designed implants. Since 2005, standardized implant components and structured rehabilitation protocols were introduced. The median follow-up period was 9.5 years.

Results

Six patients were lost to follow-up. Seven patients (including all six after the introduction of the standardized protocol) had good osseoperception, grip strength (Jamar) was 28.3 kg on the operated side versus 40.4 kg in the unaffected hand (70%), and key grip strength was 6 versus 9.1 kg. Hand function was 94% of the normal hand. The most common complications were mechanical failures necessitating changes of components (eight times in three patients) and superficial infections (seven times in five patients). Five patients had no complications. The refined implant design and new standardized treatment protocol achieved a 100% cumulative success rate with 9.5 years of follow-up so far.

Discussion

Treatment of thumb amputees using bone-anchored percutaneous prostheses seems to be a safe, durable method with excellent short- and medium-long follow-up results. Severe adverse events are few except for implant loosening which occurred only in the early custom-designed group.

Dynamically Mapping Socket Loading Conditions During Real Time Operation of an Upper Limb Prosthesis

A continuing problem faced by amputees is that extended use of a prosthesis leads to discomfort along the residual limb. In this work, we use a novel pressure sensor array and an inertial measuring unit to monitor the changes in the pressure distribution within an upper limb socket in response to its position and the real time performance of a grasping task. These experiments illustrate that the operation of a prosthetic hand produces distinct features in the time derivative and spatial component of the sensor outputs, which correspond to the orientation and task-dependent changes in loading conditions within the socket. The significance of this study is that it highlights the use of a combined pressure sensor array and inertial measuring unit as a way to characterize the loading conditions within a prosthesis based on both temporal and spatial information during movement. This method of real time pressure sensing in prosthetic sockets will be useful for adaptive socket technology aimed towards decreasing the discomfort caused by long-term use of a prosthesis.

The SoftHand Pro: Functional evaluation of a novel, flexible, and robust myoelectric prosthesis

Roughly one quarter of active upper limb prosthetic technology is rejected by the user, and user surveys have identified key areas requiring improvement: function, comfort, cost, durability, and appearance. Here we present the first systematic, clinical assessment of a novel prosthetic hand, the SoftHand Pro (SHP), in participants with transradial amputation and age-matched, limb-intact participants. The SHP is a robust and functional prosthetic hand that minimizes cost and weight using an underactuated design with a single motor. Participants with limb loss were evaluated on functional clinical measures before and after a 6-8 hour training period with the SHP as well as with their own prosthesis; limb-intact participants were tested only before and after SHP training. Participants with limb loss also evaluated their own prosthesis and the SHP (following training) using subjective questionnaires. Both objective and subjective results were positive and illuminated the strengths and weaknesses of the SHP. In particular, results pre-training show the SHP is easy to use, and significant improvement in the Activities Measure for Upper Limb Amputees in both groups following a 6-8 hour training highlights the ease of learning the unique features of the SHP (median improvement: 4.71 and 3.26 and p = 0.009 and 0.036 for limb loss and limb-intact groups, respectively). Further, we found no difference in performance compared to participant's own commercial devices in several clinical measures and found performance surpassing these devices on two functional tasks, buttoning a shirt and using a cell phone, suggesting a functional prosthetic design. Finally, improvements are needed in the SHP design and/or training in light of poor results in small object manipulation. Taken together, these results show the promise of the SHP, a flexible and adaptive prosthetic hand, and pave a path forward to ensuring higher functionality in future.

Designing Feedback Controllers for Human-Prosthetic Systems Using H∞ Model Matching

Prosthetic hands are important tools for improving the lives of upper limb amputees, yet most devices lack the ability to provide a sense of touch back to the user. Recent improvements have been made in electromyography (EMG) prosthesis control as well as in biologically relevant tactile sensors to provide sensory feedback to amputees through nerve stimulation. However, sensory feedback has been designed heuristically, which can lead to either unnatural sensations or to excessive feedback that bothers the user. In this study, we apply optimal control techniques to synthesize sensory feedback to the user, and to synthesize the conversion from EMG to an actuation command to the prosthesis. Specifically, we construct a feedback control system architecture and solve the $H_{\infty }$ model matching problem to make the closed-loop user-prosthetic system to behave like a pre-specified ideal system in response to elemental inputs (e.g. impulse, step, etc). We design feedback controllers assuming that human and prosthetic components behave in a linear fashion as a proof-of-concept, and the closed-loop system is able to match ideal systems that are slow, fast and that have both slow and fast dynamics (like healthy humans).

Prosthesis with neuromorphic multilayered e-dermis perceives touch and pain

The human body is a template for many state-of-the-art prosthetic devices and sensors. Perceptions of touch and pain are fundamental components of our daily lives that convey valuable information about our environment while also providing an element of protection from damage to our bodies. Advances in prosthesis designs and control mechanisms can aid an amputee's ability to regain lost function but often lack meaningful tactile feedback or perception. Through transcutaneous electrical nerve stimulation (TENS) with an amputee, we discovered and quantified stimulation parameters to elicit innocuous (non-painful) and noxious (painful) tactile perceptions in the phantom hand. Electroencephalography (EEG) activity in somatosensory regions confirms phantom hand activation during stimulation. We invented a multilayered electronic dermis (e-dermis) with properties based on the behavior of mechanoreceptors and nociceptors to provide neuromorphic tactile information to an amputee. Our biologically inspired e-dermis enables a prosthesis and its user to perceive a continuous spectrum from innocuous to noxious touch through a neuromorphic interface that produces receptor-like spiking neural activity. In a Pain Detection Task (PDT), we show the ability of the prosthesis and amputee to differentiate non-painful or painful tactile stimuli using sensory feedback and a pain reflex feedback control system. In this work, an amputee can use perceptions of touch and pain to discriminate object curvature, including sharpness. This work demonstrates possibilities for creating a more natural sensation spanning a range of tactile stimuli for prosthetic hands.

A bioinspired flexible organic artificial afferent nerve

The distributed network of receptors, neurons, and synapses in the somatosensory system efficiently processes complex tactile information. We used flexible organic electronics to mimic the functions of a sensory nerve. Our artificial afferent nerve collects pressure information (1 to 80 kilopascals) from clusters of pressure sensors, converts the pressure information into action potentials (0 to 100 hertz) by using ring oscillators, and integrates the action potentials from multiple ring oscillators with a synaptic transistor. Biomimetic hierarchical structures can detect movement of an object, combine simultaneous pressure inputs, and distinguish braille characters. Furthermore, we connected our artificial afferent nerve to motor nerves to construct a hybrid bioelectronic reflex arc to actuate muscles. Our system has potential applications in neurorobotics and neuroprosthetics.

Targeted Transcutaneous Electrical Nerve Stimulation for Phantom Limb Sensory Feedback

In this work, we investigated the use of noninvasive, targeted transcutaneous electrical nerve stimulation (TENS) of peripheral nerves to provide sensory feedback to two amputees, one with targeted sensory reinnervation (TSR) and one without TSR. A major step in developing a closed-loop prosthesis is providing the sense of touch back to the amputee user. We investigated the effect of targeted nerve stimulation amplitude, pulse width, and frequency on stimulation perception. We discovered that both subjects were able to reliably detect stimulation patterns with pulses less than 1 ms. We utilized the psychophysical results to produce a subject specific stimulation pattern using a leaky integrate and fire (LIF) neuron model from force sensors on a prosthetic hand during a grasping task. For the first time, we show that TENS is able to provide graded sensory feedback at multiple sites in both TSR and non-TSR amputees while using behavioral results to tune a neuromorphic stimulation pattern driven by a force sensor output from a prosthetic hand.