Upper Limb Prostheses by the Level of Amputation: A Systematic Review

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.

Targeted muscle reinnervation (TMR) is a relatively recent surgical innovation that involves the coaptation of major peripheral nerves to a recipient motor branch that innervates an expendable muscle target. The original indication for TMR was augmentation and optimization of myoelectric signals in the amputated limb for use of myoelectric prosthetics. Incidentally, surgeons and patients discovered that the technique also could treat and prevent phantom and residual limb pain. TMR is performed at the time of amputation or delayed any time after the amputation, and TMR can also be performed at any level of amputation. In the upper extremity, studies have detailed the various techniques and coaptations possible at each amputation level to create intuitive myoelectric signals and treat neurogenic pain. Treatment of peripheral nerves in the amputee with TMR should be a consideration for all patients with major upper extremity amputations, especially at large institutions able to support multidisciplinary limb salvage teams. This review article summarizes the current literature and authors' techniques and recommendations surrounding TMR in the upper extremity amputee including techniques relevant to each level of upper extremity amputation.

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.

Activity and Participation, Bimanual Function, and Prosthesis Satisfaction are Strong Predictors of General Well-Being Among Upper Limb Prosthesis Users

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.

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.

ABSTRACTDespite progress in research and media attention on active upper limb prostheses, presently the most common commercial upper limb prosthetic devices are not fundamentally different from solutions offered almost one century ago. Limited information transfer for both control and sensory-motor integration and challenges in socket technology have been major obstacles. By analysing the present state-of-the-art and academic achievements, we provide our opinion on the future of upper limb prostheses. We believe that surgical procedures for muscle reinnervation and osseointegration will become increasingly clinically relevant; muscle electrical signals will remain the main clinical means for prosthetic control; and chronic electrode implants, first in muscles (control), then in nerves (sensory feedback), will become viable clinical solutions. After decades of suspended clinically relevant progress, it is foreseeable that a new generation of upper limb prostheses will enter the market in the near future based on such advances, thereby offering substantial clinical benefit for patients.

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

The article deals with the use of different types of upper limb prostheses in Slovenia. Four hundred and fourteen upper limb amputees were sent a questionnaire on the type of their prosthesis, its use and reasons for non-use, respectively. The replies were subject to statistical analysis. Most of the questioned upper limb amputees (70%) wear a prosthesis only for cosmesis. The use of a prosthesis depends on the level of upper limb amputation, loss of the dominant hand, and time from amputation. Prosthetic success appears to be unrelated to age at the time of amputation and the rehabilitation programme. The most frequent reason for not wearing a prosthesis is heat and consequent sweating of the stump. More than a third of amputees are dissatisfied with their prostheses.

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.

People who either use an upper limb prosthesis and/or have used services provided by a prosthetic rehabilitation centre, hereafter called users, are yet to benefit from the fast-paced growth in academic knowledge within the field of upper limb prosthetics. Crucially over the past decade, research has acknowledged the limitations of conducting laboratory-based studies for clinical translation. This has led to an increase, albeit rather small, in trials that gather real-world user data. Multi-stakeholder collaboration is critical within such trials, especially between researchers, users, and clinicians, as well as policy makers, charity representatives, and industry specialists. This paper presents a co-creation model that enables researchers to collaborate with multiple stakeholders, including users, throughout the duration of a study. This approach can lead to a transition in defining the roles of stakeholders, such as users, from participants to co-researchers. This presents a scenario whereby the boundaries between research and participation become blurred and ethical considerations may become complex. However, the time and resources that are required to conduct co-creation within academia can lead to greater impact and benefit the people that the research aims to serve.