Reflections on the present and future of upper limb prostheses

ABSTRACTObjectives: Novel connective and control methods between an upper limb prosthetic device and end-user have large potential rewards and risks, making economic evaluation difficult.Methods: We conducted an environmental scan to assess user perspectives on advancements in upper limb prosthetic device integration. The environmental scan consisted of a PubMed literature search, grey literature review, formation of a community advisory board (CAB) and key informant interviews. The CAB guided the study and was comprised of adults with personal or professional experiences with upper limb prostheses.Results: The environmental scan highlights 4 main types of integration in upper-limb prostheses: osseointegration, targeted muscle reinnervation, cortical integration and peripheral nerve/muscle integration. The PubMed literature search resulted in the greatest number of matches for ‘targeted muscle reinnervation upper limb’ (N = 65) and ‘upper limb osseointegration’ (N = 54). The grey literature review found targeted muscle reinnervation and peripheral nerve/muscle integration to be most discussed amongst end-users and regulators. Of these four methods, greater device control versus invasiveness of implant is a clear benefit-risk tradeoff.Conclusion: This scan highlights a gap in user-centered research in upper limb prosthetic devices. Future directions include the development of a stated-preference instrument incorporating these methods of integration.Expert opinion: Upper limb loss greatly impacts one’s productivity and quality of life. Despite a variety of prosthetic device options, high user dissatisfaction and rejection rates persist. Using community-based participatory research practices, we engaged end-users and identified the importance of option value. Many individuals said they would forego a present-day option to maintain the opportunity to adopt a device in the future, a point not addressed in the literature. Of the four emerging integration methods identified, targeted muscle reinnervation and peripheral nerve/muscle integration are highly promising, yet a better understanding of end-user preferences for these methods is still needed.

The goal of this study was to investigate the use of cognitive modeling to assess the usability of an upper-limb prosthesis with a focus on mental workload responses. Prior studies have investigated usability of upper-limb prostheses with subjective surveys and physiological measures. However, these approaches have limitations, including subject recall of conditions and physiological response contamination by head and body movements and user speech during task performance as well as sensitivity to physical fatigue and room lighting conditions. Cognitive modeling was used to assess mental workload in use of transradial upper-limb prosthesis. A case study was conducted with a participant with upper-limb amputation using two different types of electromyography-based control schemes, including conventional direct control (DC) and pattern recognition (PR) control in order to compare cognitive model outcomes with mental workload assessment using eye-tracking measures. Cognitive models time estimates were also compared with actual task completion time results from the case study to further assess the validity of cognitive modeling as an analytical tool for evaluating upper limb prosthesis usability. Findings of both the cognitive models and case study revealed the PR mode to be more intuitive, reduce cognitive load, and increase efficiency in prosthetic control as compared to the DC mode. Results of the present study revealed that cognitive modeling can be used as an analytical approach for assessing upper-limb prosthetic device usability in terms of workload outcomes. Future studies should validate the present findings with more precise time estimations and a larger user sample size.

This review article aims to provide an updated and comprehensive overview of the latest trends in adult upper limb prostheses, specifically targeting various amputation levels such as transradial, transmetacarpal, transcarpal, and transhumeral. A systematic search was conducted across multiple databases, including IEEE Xplore, MDPI, Scopus, Frontiers, and Espacenet, covering from 2018 to 2023. After applying exclusion criteria, 49 scientific articles (33 patents and 16 commercial prostheses) were meticulously selected for review. The article offers an in-depth analysis of several critical aspects of upper limb prostheses. It discusses the evolution and current state of input control mechanisms, the number of degrees of freedom, and the variety of grips available in prostheses, all tailored according to the level of amputation. Additionally, the review delves into the selection of materials used in developing these prostheses and examines the progression of technology readiness levels. A significant focus is also placed on the evolution of prosthesis weight over the years for different amputation levels. Moreover, the review identifies and explores critical technological challenges and prospects in upper limb prostheses. Finally, the article culminates with a conclusion that encapsulates the key findings and insights on the advancements and ongoing developments in this field.

To achieve the purpose of accurately grasping a random target with the upper limb prosthesis, the acquisition of target localization information is especially important. For this reason, a novel type of random target localization algorithm is proposed. Firstly, an initial localization algorithm (ILA) that uses two 3D attitude sensors and a laser range sensor to detect the target attitude and distance is presented. Secondly, an error correction algorithm where a multipopulation genetic algorithm (MPGA) optimizes backpropagation neural network (BPNN) is utilized to improve the accuracy of ILA. Thirdly, a general regression neural network (GRNN) algorithm is proposed to calculate the joint angles, which are used to control the upper limb prosthetic gripper to move to the target position. Finally, the proposed algorithm is applied to the 5‐DOF upper limb prosthesis, and the simulations and experiments are proved to demonstrate the validity of the proposed localization algorithm and inverse kinematics (IK) algorithm.

Chronic electrode implantation in the brain leads to the formation of a glial scar surrounding the implantation site. The glial scar, when formed, severely inhibits the long term in-vivo functionality of neural electrodes. Studies in the past have tried coating the neural electrodes using polymers with or without bioactive agents. However none report any success in alleviating the glial scar or show improvement in electrode recording capability. Previous studies have suggested the use of neurotrophin delivery from electrode coatings to promote neuron survival and improve electrode biocompatibility. In the current work, the feasibility of delivering Brain Derived Neurotrophic Factor (BDNF) from alginate thin film coatings on silicon electrodes was evaluated. Electrode coatings were manufactured via spinner coating using 0.45 [mu]m filtered 2% (w/v) or 4% (w/v) alginate solutions on silicon wafer substrates. To crosslink the alginate two different methods of crosslinking were evaluated, the fast CaCl2 crosslinking and the slow CaCO3-GDL crosslinking method. However CaCl2 crosslinking using an aerosolized spray produced consistent alginate coatings compared to the CaCO3-GDL crosslinking method and was subsequently used for all experiments. Thickness measurements using the profilometer showed that CaCl2 crosslinked alginate coatings in the micron level can be produced by spin coating 0.45 [mu]m filtered, 4% alginate solutions at 1000 rpm for 30-60 seconds. When multiple layers of alginate were applied on top of one another, coatings of thicknesses ranging from 5 [mu]m to 80 [mu]m were produced via spinner coating. To simulate BDNF release, all alginate films were loaded with either model proteins (lysozyme or chymotrypsinogen) or poloxamer (p-188), a polymer that has been shown to have membrane-sealing properties. In-vitro release studies from all loaded films were conducted in PBS (pH 7.4) at 37°C until all protein or poloxamer was released. Results from the release studies showed rapid release profiles for both model proteins and p-188. Complete release of proteins and p-188 took place within the first three hours. This is well short of the anticipated 3-4 day release period required for BDNF. In order to prolong release for BDNF, the effect of addition of a poly-L-ornithine (PLO) coating on the alginate layer was evaluated. PLO coatings at three different concentrations, 0.1(w/v) %, 0.5% and 1% were applied on the surface of alginate films. Subsequent release studies showed very similar release profiles between the PLO-coated and the uncoated films, and were probably due to the detachment of the film from the silicon surface early in the release. The results of this study clearly show the feasibility of manufacturing neurotrophin releasing alginate coatings for silicon electrodes. The versatile coating method can be used to deliver a number of different compounds including proteins like neurotrophins and polymers like p-188. However to achieve controlled release over longer periods of 3-4 days, alternate coating methods will need to be investigated.

Chapter 5 - Sensory feedback for upper limb prostheses

Electrotactile stimulation has been suggested as a modality for providing sensory feedback in upper limb prostheses. This study investigates the multiday variability of subdermal and surface stimulation. Electrical stimulation was delivered using either surface or fine wire electrodes placed right under the skin in eight amputees for seven consecutive days. The variability of psychophysical measurements, including detection threshold (DT), pain threshold (PT), dynamic range (DR), just noticeable difference (JND), Weber fraction (WF) and quality of evoked sensations, was evaluated using the coefficient of variation (CoV). In addition, the systematic change in the mean of the parameters across days was assessed in both stimulation modalities. In the case of DT, PT, DR, and perceived intensity at 100 Hz, the CoV of surface stimulation was significantly smaller than that of subdermal stimulation. Only PT showed a significant systematic change in the mean value across days for both modalities. The outcome of this study has implications for the choice of modality in delivering sensory feedback, though the significance of the quantified variability needs to be evaluated using usability tests with user feedback.

Non-invasive sensory feedback is a desirable goal for upper limb prostheses as well as in human robot interaction and other human machine interfaces. Yet many approaches have been studied, none has been broadly deployed in upper limb prostheses. Bone conduction has the potential to excite an effect known as osseoperception and therefore provides a novel sensory interface. This paper presents the preliminary results of our study into the temporal parameters of a sensory feedback interface utilizing vibrotactile stimulus onto the ulnar olecranon representing a non-invasive sensory feedback interface. Three different tests are performed to establish the characterizing parameters of the interface; perception threshold, sensation discrimination and reaction time. Our results are similar to the results obtained for invasive bone conduction. The perception threshold for lower frequencies is small and therefore allows using small transducers with low power consumption. The sensation discrimination shows comparable results as reported in existing literature as well as the reaction time for the amputee is within the same range.

Purpose: Upper limb prostheses are part of a rapidly changing market place. Despite development in device design, surveys report low levels of uptake and dissatisfaction with current prosthetic design. In this study, we present the results of a survey conducted with people with upper limb difference in Australia on their use of current prostheses and preferences in a prosthetic in order to inform future prosthetic hand design.Methods: An online survey was conducted on upper limb amputees, with 27 respondents that completed the survey. The survey was a mixture of open-ended questions, ranking design features and quantitative questions on problems experienced and desired attributes of future prosthesis designs.Results: Common key issues and concerns were isolated in the survey related to the weight, manipulation and dexterity, aesthetics, sensory feedback and financial cost; each of which could be addressed by additive manufacturing and soft robotics techniques.Conclusions: The adaptability of additive manufacturing and soft robotics to the highlighted concerns of participants shows that further research into these techniques is a feasible method to improve patient satisfaction and acceptance in prosthetic hands.Implications for rehabilitationEven with recent developments and advances in prosthetic design, the needs and desires of prosthetic users are not being met with current products.The desires and needs of those with upper limb difference are diverse.Using additive manufacturing to produce prosthetics allows for mass customization of prosthetics to meet these diverse needs while reducing costs.A soft robotic approach to prosthetics can help meet the desires of reducing weight and costs, while maintaining functionality.

Background and aim:In order to create more uniformity in the prescription of upper limb prostheses by Dutch rehabilitation teams, the development and implementation of a Prosthesis Prescription Protocol of the upper limb (PPP-Arm) was initiated. The aim was to create a national digital protocol to structure, underpin, and evaluate the prescription of upper limb prostheses for clients with acquired or congenital arm defects.Technique:Prosthesis Prescription Protocol of the Arm (PPP-Arm) was developed on the basis of the International Classification of Functioning and consisted of several layers. All stakeholders (rehabilitation teams, orthopedic workshops, patients, and insurance companies) were involved in development and implementation. A national project coordinator and knowledge brokers in each team were essential for the project.Discussion:PPP-Arm was successfully developed and implemented in nine Dutch rehabilitation teams. The protocol improved team collaboration, structure, and completeness of prosthesis prescriptions and treatment uniformity and might be interesting for other countries as well.Clinical relevanceA national protocol to prescribe upper limb prostheses can be helpful to create uniformity in treatment of patients with upper limb defects. Such a protocol improves quality of care for all patients in the country.

Upper limb amputation is a condition that significantly restricts the amputees from performing their daily activities. The myoelectric prosthesis, using signals from residual stump muscles, is aimed at restoring the function of such lost limbs seamlessly. Unfortunately, the acquisition and use of such myosignals are cumbersome and complicated. Furthermore, once acquired, it usually requires heavy computational power to turn it into a user control signal. Its transition to a practical prosthesis solution is still being challenged by various factors particularly those related to the fact that each amputee has different mobility, muscle contraction forces, limb positional variations and electrode placements. Thus, a solution that can adapt or otherwise tailor itself to each individual is required for maximum utility across amputees. Modified machine learning schemes for pattern recognition have the potential to significantly reduce the factors (movement of users and contraction of the muscle) affecting the traditional electromyography (EMG)-pattern recognition methods. Although recent developments of intelligent pattern recognition techniques could discriminate multiple degrees of freedom with high-level accuracy, their efficiency level was less accessible and revealed in real-world (amputee) applications. This review paper examined the suitability of upper limb prosthesis (ULP) inventions in the healthcare sector from their technical control perspective. More focus was given to the review of real-world applications and the use of pattern recognition control on amputees. We first reviewed the overall structure of pattern recognition schemes for myo-control prosthetic systems and then discussed their real-time use on amputee upper limbs. Finally, we concluded the paper with a discussion of the existing challenges and future research recommendations.

Thanks to the progress in the robotic and mechatronic fields, upper limb prostheses became more and more dexterous, getting closer to closing the gap between the natural limb and the medical device replacing it. However, the standard prosthesis controller spread on the market is still limited in the number of functions it can efficiently control and constitute a bottleneck in prosthesis use. Two leading solutions are currently under research to tackle this discrepancy: machine learning algorithms and autonomous controllers. Myo-controllers based on machine learning algorithms enable the user to control a high but still limited number of functions directly. On the other hand, autonomous controllers are based on a diversification of the sensor modalities to integrate the context and the user's intention in control and automatise part of the grasping process, relieving the user from the physical and potentially cognitive workload associated with it. This thesis focuses on the impact of the combination of these two solutions. Therefore, two studies investigated the gain of performance gain and the range of applicability of an association of a semi-autonomous system to a machine learning myo-controller for upper limb prostheses. This dissertation introduces first a method to preshape the prosthesis based on the prediction of the user’s intended grasping strategy. This method system supports the user in real-time by preshaping the prosthetic device's hand and wrist during a reaching phase of a prehensile action. This result is achieved by merging data from inertial measurement units, computer vision, and positions and pressure sensors to reproduce artificial proprioception, artificial exteroception, and short-term memory. The autonomous controller developed was designed to support the user in dynamic objects-crowded conditions. In a second phase, it has been assessed whether a semi-autonomous system associated with a machine learning myo-controller could improve the performance compared to the same machine learning controller alone. To assess this system, eight able-bodied and two amputee participants performed a newly developed test featuring a sequence of re-localisation tasks in a scene with multiple objects. The two amputee participants completed a standard rehabilitation test as well. The results revealed that the semi-autonomous system increased the time performance and reduced the physical effort for the total duration of the trial, and more specifically, in the preshaping phase of the task. The semi-autonomous system also reduced the need to control the prosthetic wrist manually. Eventually, the last part of this dissertation focuses on the interactions between the autonomous controller and the user. It investigates the impact, through the addition of artificial error on the autonomous system output, of different shared control modalities which define the application, according to the user’s commands, of the decisions of the autonomous controller on the prosthesis. Ten able-bodied participants performed a dual-task combining a reach-and-grasp task and an auditory reaction task. Time performance, physical and cognitive workload were recorded. This test implemented different control-sharing modalities at different levels of error. The results revealed that the shared control modalities significantly impact the task performance and the physical effort required to complete the task. The effect of the level of added errors on the three different outcomes varies between the control-sharing modalities. These results, therefore, provide valuable information to design and compare semi-autonomous upper limb prosthesis systems. In conclusion, by demonstrating the benefit of semi-autonomous systems and the investigation of their application conditions, this thesis advances the development of a new generation of prosthesis controllers that automatize parts of the actuation of the prosthesis. This research enlarges the current control bottleneck, which prevents prosthesis users from taking full advantage of their highly developed device. Therefore, it can contribute to increasing the support and the autonomy that upper limb prostheses can provide to their users in daily life.

Surface Electromyography (EMG)-based pattern recognition methods have been investigated over the past years as a means of controlling upper limb prostheses. Despite the very good reported performance of myoelectric controlled prosthetic hands in lab conditions, real-time performance in everyday life conditions is not as robust and reliable, explaining the limited clinical use of pattern recognition control. The main reason behind the instability of myoelectric pattern recognition control is that EMG signals are non-stationary in real-life environments and present a lot of variability over time and across subjects, hence affecting the system's performance. This can be the result of one or many combined changes, such as muscle fatigue, electrode displacement, difference in arm posture, user adaptation on the device over time and inter-subject singularity. In this paper an extensive literature review is performed to present the causes of the drift of EMG signals, ways of detecting them and possible techniques to counteract for their effects in the application of upper limb prostheses. The suggested techniques are organized in a table that can be used to recognize possible problems in the clinical application of EMG-based pattern recognition methods for upper limb prosthesis applications and state-of-the-art methods to deal with such problems.

Objective.Sensory feedback restoration represents a fundamental need for upper limb prosthesis users because it permits to feel somatic sensations during interactions with the environment. Considering the artificial sensory transduction, neuroprotheses should take advantage of effective encoding algorithms, which have the essential role, in the sensory feedback process, of coding the intended perception to the individual with the amputation. This paper presents a literature systematic review of the encoding algorithms employed for somatotopically restoring somatic sensations in upper limb of individuals with the intact arm or with an amputation.Approach.The methodologies adopted for the development of the encoding algorithms were deeply analyzed to describe what is the current state of the art on this topic. Encoding algorithms validated in literature on upper limb were grouped into three main categories (Function-based,Bio-inspiredandHybrid) and then compared and described.Main results.Function-based Algorithmsprovide the user with high sensitivity, whereas if the verisimilitude to natural sensation and complexity are the most desirable features for sensory feedback, aBio-inspiredstrategy would be the most suitable to implement. However,Hybridsolutions both evoked realistic sensations and enhanced discrimination capabilities.Significance.The conducted analysis represents a guide for understanding which type of encoding to choose, making a compromise between the characteristics of the elicited sensations and the achieved performance. This critical analysis will give the reader important information for understanding the potentiality of the encoding strategies to elicit different sensations for a specific application and for developing novel sensory restoration approaches.

This paper presents an upper limb prosthesis called IRR, which consists of a 7 DOF fully-actuated arm and a 15-DOF underactuated hand. The prosthesis is aimed to grasp a wide range of objects and perfectly reproduce the movement of the human upper limb. The main development is focused on the requirements of small size, light weight, high functionality and good cosmetic appearance. First, mechanisms of the prosthesis have been designed. Then, highly reliable embedded electronics, effective control architecture are devised to manage complexity of the system. Finally, multi-mode grasping and drinking water experiments have been done to test the ability of the prosthesis to reproduce human activities of daily lives.