Upper-limb Prostheses Research Articles

Cosmetic gloves from natural rubber latex for upper limb prostheses: Preparation and physicochemical, mechanical and biological characterization

Upper limb amputations are frequently the result of traumatic events, often associated with car accidents or incidents involving industrial machinery. The development of prosthetic technologies is essential to provide physical assistance to amputees and offer psychological support. In such circumstances, cosmetic gloves can be utilized to improve personal and social comfort by enhancing visual characteristics similar to those of a biological hand. In this work, we developed formulations based on natural rubber latex (NRL) with antibacterial properties by the incorporation of an additive consisting of zinc oxide coated with silver nanoparticles to manufacture cosmetic gloves designed for upper limb prostheses. The dispersion efficiency of the additive was investigated by microscopic techniques. Tensile and tear strength tests, Shore A hardness measurements, swelling indices, hydrophobicity and inhibition of bacterial growth were also conducted to ascertain the suitability and adaptability of the developed material for integration into the daily activities of patients. Moreover, processing parameters for the immersion molding production of cosmetic gloves were assessed and validated, highlighting the crucial relationship between manufacturing techniques and material properties. Overall, the results indicated that the combination of the additive with a high crosslinking density of NRL effectively improved the material mechanical properties, resulting in a tensile strength of up to 20.7 MPa and a tear strength of approximately 40 kN m−1. In addition, and according to the antibacterial tests, the gloves exhibited bacteriostatic effects against Staphylococcus aureus and bactericidal effects against Escherichia coli. As a conclusion, the dataset indicates that the manufactured cosmetic gloves can be used to covering upper limb prostheses. Additionally, it underscores the considerable potential for the cosmetic glove manufacturing industry through the use of appropriate formulations and processing techniques for NRL.

Investigating the Benefits of Multivariable Proprioceptive Feedback for Upper-Limb Prostheses

Investigating the Benefits of Multivariable Proprioceptive Feedback for Upper-Limb Prostheses

Identifying Users’ Needs to Design and Manufacture 3D-Printed Upper Limb Sockets: A Survey-Based Study

The development of prosthetic arms has increased in recent years, particularly with the growth of 3D printing technologies. However, one of the main weaknesses of 3D-printed prosthetics is the prosthetic socket, which commonly presents a generic adjustable design that may produce discomfort. In fact, the socket has always been a part that has frequently caused discomfort in traditionally manufactured prosthetics and, consequently, high rejection rates. Studies about improving the socket component in traditional and 3D-printed upper limb prostheses are scarce. Advancements in 3D printing and 3D scanning will offer a high potential to improve the design and manufacturing of 3D-printed sockets. Thus, to propose better designs and manufacturing protocols, this paper presents a questionnaire to assess the needs of upper limb prosthetics users or potential users, as well as a survey-based study with 18 respondents. The results reveal that users prioritize breathability and low cost, a stable fixing system, products without the need for shape adjustments, a light weight and comfort regarding the products they require. The results of this study provide insights into the key characteristics that sockets should accomplish according to users’ needs that are applicable to 3D-printed sockets and traditionally manufactured sockets, and they contribute to improving their design and manufacturing.

Using Serious Games to Measure Upper-Limb Myoelectric Pattern Recognition Prosthesis Control Performance in an At-Home Environment

Using Serious Games to Measure Upper-Limb Myoelectric Pattern Recognition Prosthesis Control Performance in an At-Home Environment

Design and Assessment of Bird-Inspired 3D-Printed Models to Evaluate Grasp Mechanics.

Adapting grasp-specialized biomechanical structures into current research with 3D-printed prostheses may improve robotic dexterity in grasping a wider variety of objects. Claw variations across various bird species lend biomechanical advantages for grasping motions related to perching, climbing, and hunting. Designs inspired by bird claws provide improvements beyond a human-inspired structure for specific grasping applications to offer a solution for mitigating a cause of the high rejection rate for upper-limb prostheses. This research focuses on the design and manufacturing of two robotic test devices with different toe arrangements. The first, anisodactyl (three toes at the front, one at the back), is commonly found in birds of prey such as falcons and hawks. The second, zygodactyl (two toes at the front, two at the back), is commonly found in climbing birds such as woodpeckers and parrots. The evaluation methods for these models included a qualitative variable-object grasp assessment. The results highlighted design features that suggest an improved grasp: a small and central palm, curved distal digit components, and a symmetrical digit arrangement. A quantitative grip force test demonstrated that the single digit, the anisodactyl claw, and the zygodactyl claw designs support loads up to 64.3 N, 86.1 N, and 74.1 N, respectively. These loads exceed the minimum mechanical load capabilities for prosthetic devices. The developed designs offer insights into how biomimicry can be harnessed to optimize the grasping functionality of upper-limb prostheses.

Influence of spatio-temporal filtering on hand kinematics estimation from high-density EMG signals.

Objective.Surface electromyography (sEMG) is a non-invasive technique that records the electrical signals generated by muscles through electrodes placed on the skin. sEMG is the state-of-the-art method used to control active upper limb prostheses because of the association between its amplitude and the neural drive sent from the spinal cord to muscles. However, accurately estimating the kinematics of a freely moving human hand using sEMG from extrinsic hand muscles remains a challenge. Deep learning has been recently successfully applied to this problem by mapping raw sEMG signals into kinematics. Nonetheless, the optimal number of EMG signals and the type of pre-processing that would maximize performance have not been investigated yet.Approach.Here, we analyze the impact of these factors on the accuracy in kinematics estimates. For this purpose, we processed monopolar sEMG signals that were originally recorded from 320 electrodes over the forearm muscles of 13 subjects. We used a previously published deep learning method that can map the kinematics of the human hand with real-time resolution.Main results.While myocontrol algorithms essentially use the temporal envelope of the EMG signal as the only EMG feature, we show that our approach requires the full bandwidth of the signal in the temporal domain for accurate estimates. Spatial filtering however, had a smaller impact and low-order spatial filters may be suitable. Moreover, reducing the number of channels by ablation resulted in large performance losses. The highest accuracy was reached with the highest number of available sensors (n = 320). Importantly and unexpected, our results suggest that increasing the number of channels above those used in this study may further enhance the accuracy in predicting the kinematics of the human hand.Significance.We conclude that full bandwidth high-density EMG systems of hundreds of electrodes are needed for accurate kinematic estimates of the human hand.

A scoping review on upper extremity prostheses: Satisfaction, barriers, and their implications in resource-limited countries.

The rates of prosthetic abandonment are not extensively understood especially in resource-limited countries. A scoping review was conducted to examine the literature on the satisfaction with and barriers to using specifically upper limb prostheses. A systematic search of the literature identified 425 studies. After reviewing the articles using predetermined inclusion and exclusion criteria, 7 cross-sectional studies were included in the final review. Barriers to the use of upper limb prostheses include the characteristics of the prosthesis (type, comfort, weight, functionality, price, and availability) and individual patient factors (recent prosthetic user, level of amputation, congenital and accidental limb loss, pain, and duration between amputation and prosthetic fitting). Considering tailored prosthetic design and funding may result in improved prosthetic adherence.

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

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.

Sex and gender differences in upper extremity prosthesis rejection: A review of literature.

Prosthesis rejection is a significant barrier to rehabilitation of persons with upper limb difference. Many individual factors can affect device rejection, including a person's sex or gender. The objective of this narrative review was to explore the reported differences between the sexes and genders in upper limb prosthesis rejection. This review considered peer-reviewed, published research studies in which the study population were adults (aged 18 and older) who had unilateral or bilateral limb difference (any level) of any etiology with current, past, or no history of prosthetic device usage. Using identified keywords, index terms, and a peer-reviewed search filter, the literature was searched in MEDLINE, Embase, and PsycInfo. The reasons for rejection, disuse, or abandonment of prosthetic devices were extracted, with the focus on reported differences between sex and genders. After searching, 29 articles were selected for full-text review and 15 were included. Only 5 of 15 articles examined differences between the sexes. Women tend to reject upper extremity prostheses more than men both before and after being fit with a device; device characteristics, such as weight and cosmesis, do not appear to be appropriately designed for women; and there may not be adequate consideration of the goals for women with limb difference(s). There is inadequate reporting of sex and gender in the literature on prosthesis rejection; future studies should report and explore these factors to determine whether the needs of the full population with limb loss are being met.

Interactive control algorithm for shoulder-amputated prosthesis and object based on reinforcement learning

As a prosthesis made to compensate for the residual loss of the amputee’s limb, the shoulder disarticulation upper limb prosthesis replaces the missing arm function of the shoulder amputee to a certain extent. However, the current upper limb prosthesis mainly interacts with the outside world through the prosthetic hand for grasping and gripping, and the interaction between other parts and the environment is often neglected, which is not in line with the use habits of the human arm. To address this problem, this paper proposes a reinforcement learning–based method for controlling the forearm interaction of a shoulder-disconnected upper limb prosthesis, and analyzes and solves the forces during the interaction, reducing the impact of uncertainty on interaction actions and accelerating training while ensuring the stability of handheld items. We evaluated the performance of the control method during the interaction between the upper limb prosthesis and the external environment through simulation experiments. After the training, the bionic arm was able to push the object into the target range for different objects and pushing distance requirements, which showed the good control effect of the method. Also, the control method can be applied to improve the interaction between the robotic arm and the environment.

Implications of EMG channel count: enhancing pattern recognition online prosthetic testing.

Myoelectric pattern recognition systems have shown promising control of upper limb powered prostheses and are now commercially available. These pattern recognition systems typically record from up to 8 muscle sites, whereas other control systems use two-site control. While previous offline studies have shown 8 or fewer sites to be optimal, real-time control was not evaluated. Six individuals with no limb absence and four individuals with a transradial amputation controlled a virtual upper limb prosthesis using pattern recognition control with 8 and 16 channels of EMG. Additionally, two of the individuals with a transradial amputation performed the Assessment for Capacity of Myoelectric Control (ACMC) with a multi-articulating hand and wrist prosthesis with the same channel count conditions. Users had significant improvements in control when using 16 compared to 8 EMG channels including decreased classification error (p = 0.006), decreased completion time (p = 0.019), and increased path efficiency (p = 0.013) when controlling a virtual prosthesis. ACMC scores increased by more than three times the minimal detectable change from the 8 to the 16-channel condition. The results of this study indicate that increasing EMG channel count beyond the clinical standard of 8 channels can benefit myoelectric pattern recognition users.

Understanding the capacity of children with congenital unilateral below-elbow deficiency to actuate their affected muscles

In recent years, commercially available dexterous upper limb prostheses for children have begun to emerge. These devices derive control signals from surface electromyography (measure of affected muscle electrical activity, sEMG) to drive a variety of grasping motions. However, the ability for children with congenital upper limb deficiency to actuate their affected muscles to achieve naturalistic prosthetic control is not well understood, as compared to adults or children with acquired hand loss. To address this gap, we collected sEMG data from 9 congenital one-handed participants ages 8–20 years as they envisioned and attempted to perform 10 different movements with their missing hands. Seven sEMG electrodes were adhered circumferentially around the participant’s affected and unaffected limbs and participants mirrored the attempted missing hand motions with their intact side. To analyze the collected sEMG data, we used time and frequency domain analyses. We found that for the majority of participants, attempted hand movements produced detectable and consistent muscle activity, and the capacity to achieve this was not dissimilar across the affected and unaffected sides. These data suggest that children with congenital hand absence retain a degree of control over their affected muscles, which has important implications for translating and refining advanced prosthetic control technologies for children.

A transhumeral prosthesis with an artificial neuromuscular system: Sim2real-guided design, modeling, and control

In this work we introduce a new type of human-inspired upper-limb prostheses. The Artificial Neuromuscular Prosthesis (ANP) imitates the human neuromuscular system in the sense of its compliance, backdrivability, natural motion, proprioceptive sensing, and kinesthetics. To realize this challenging goal, we introduce a novel human-inspired and simulation-based development paradigm to design the prosthesis mechatronics in correspondence to the human body. The ANP provides body awareness, contact awareness, and human-like contact response, realized via floating base rigid-body models, disturbance observers, and joint impedance control—concepts known from established state-of-the-art robotics. The ANP mechatronics is characterized by a four degrees of freedom (dof) torque-controlled human-like kinematics, a tendon-driven 2-dof wrist, and spatial orientation sensing at a weight of 1.7 kg (without hand and battery). The paper deals with the rigorous mathematical modeling, control, design and evaluation of this device type along initially defined requirements within a single prototype only. The proposed systemic and grasping capabilities are verified under laboratory conditions by an unimpaired user. Future work will increase the technology readiness level of the next generation device, where human studies with impaired users will be done.

Classification of hand and wrist movements via surface electromyogram using the random convolutional kernels transform

Prosthetic devices are vital for enhancing personal autonomy and the quality of life for amputees. However, the rejection rate for electric upper-limb prostheses remains high at around 30%, often due to issues like functionality, control, reliability, and cost. Thus, developing reliable, robust, and cost-effective human-machine interfaces is crucial for user acceptance. Machine learning algorithms using Surface Electromyography (sEMG) signal classification hold promise for natural prosthetic control. This study aims to enhance hand and wrist movement classification using sEMG signals, treated as time series data. A novel approach is employed, combining a variation of the Random Convolutional Kernel Transform (ROCKET) for feature extraction with a cross-validation ridge classifier. Traditionally, achieving high accuracy in time series classification required complex, computationally intensive methods. However, recent advances show that simple linear classifiers combined with ROCKET can achieve state-of-the-art accuracy with reduced computational complexity. The algorithm was tested on the UCI sEMG hand movement dataset, as well as on the Ninapro DB5 and DB7 datasets. We demonstrate how the proposed approach delivers high discrimination accuracy with minimal parameter tuning requirements, offering a promising solution to improve prosthetic control and user satisfaction.

A three degrees of freedom switchable impedance myoelectric prosthetic wrist

Wrist mobility contributes significantly to the execution of upper limb motor tasks. Despite this, current prosthetic wrists are far less advanced than other artificial joints. Typically, prosthetic wrists offer limited degrees of freedom, if any, which forces users to execute compensatory movements during task performance. This addition increases weight and complexity, two unwelcome factors in upper limb prostheses. This article presents the design of a 3-degree-of-freedom friction-lockable prosthetic wrist actuated by a single motor. The design features adaptable behavior when unlocked, promoting a gentle interaction with the environment, and enables users to adjust the hand configuration during pre-grasping phases. The proposed system was tested, combined with a hand prosthesis, and compared to a commercial rotational wrist during the execution of functional movements. Experiments involved nine able-bodied subjects and one prosthesis user. Participants also performed the experiments with their biological wrist (the intact wrist for the prosthesis user) as a control. Results showed that the lockable wrist was used actively 20% more often than the commercial solution without compromising users’ execution time. Interaction tests reveal that compensatory movements are reduced when using the proposed design, resulting in closer resemblance to the control wrist’s performance. The average satisfaction and usability scores were significantly higher for the proposed wrist, indicating its potential acceptance. Finally, the system was validated in a set of activities of daily living performed by the prosthesis user. The study contributes to the development of more intuitive and adaptable prostheses that can improve the quality of life of amputees.

A Circular, Wireless Surface-Electromyography Array.

Commercial, high-tech upper limb prostheses offer a lot of functionality and are equipped with high-grade control mechanisms. However, they are relatively expensive and are not accessible to the majority of amputees. Therefore, more affordable, accessible, open-source, and 3D-printable alternatives are being developed. A commonly proposed approach to control these prostheses is to use bio-potentials generated by skeletal muscles, which can be measured using surface electromyography (sEMG). However, this control mechanism either lacks accuracy when a single sEMG sensor is used or involves the use of wires to connect to an array of multiple nodes, which hinders patients' movements. In order to mitigate these issues, we have developed a circular, wireless s-EMG array that is able to collect sEMG potentials on an array of electrodes that can be spread (not) uniformly around the circumference of a patient's arm. The modular sEMG system is combined with a Bluetooth Low Energy System on Chip, motion sensors, and a battery. We have benchmarked this system with a commercial, wired, state-of-the-art alternative and found an r = 0.98 (p < 0.01) Spearman correlation between the root-mean-squared (RMS) amplitude of sEMG measurements measured by both devices for the same set of 20 reference gestures, demonstrating that the system is accurate in measuring sEMG. Additionally, we have demonstrated that the RMS amplitudes of sEMG measurements between the different nodes within the array are uncorrelated, indicating that they contain independent information that can be used for higher accuracy in gesture recognition. We show this by training a random forest classifier that can distinguish between 6 gestures with an accuracy of 97%. This work is important for a large and growing group of amputees whose quality of life could be improved using this technology.

An Interactive Digital-Twin Model for Virtual Reality Environments to Train in the Use of a Sensorized Upper-Limb Prosthesis

In recent years, the boost in the development of hardware and software resources for building virtual reality environments has fuelled the development of tools to support training in different disciplines. The purpose of this work is to discuss a complete methodology and the supporting algorithms to develop a virtual reality environment to train the use of a sensorized upper-limb prosthesis targeted at amputees. The environment is based on the definition of a digital twin of a virtual prosthesis, able to communicate with the sensors worn by the user and reproduce its dynamic behaviour and the interaction with virtual objects. Several training tasks are developed according to standards, including the Southampton Hand Assessment Procedure, and the usability of the entire system is evaluated, too.

РОЛЬ И ВЛИЯНИЕ РАЗВИТИЯ ПРОТЕЗОСТРОЕНИЯ ВЕРХНИХ И НИЖНИХ КОНЕЧНОСТЕЙ НА ПОЛНОМАСШТАБНУЮ СОЦИАЛЬНУЮ ИНТЕГРАЦИЮ ТРУДОСПОСОБНЫХ ИНВАЛИДОВ

The research touches upon the issues of legal and financial accessibility of upper and lower limb prostheses (hereinafter - ULP) provided at the expense of budgetary funds to disabled people of working age, their functional capabilities for domestic and labor activities, as well as the introduction of innovative technologies in the production of ULP and the impact of the market of assistive products on increasing the motivation of disabled people to be involved in labor inclusion, including by expanding the range of employment opportunities. The aim of the research is to highlight the importance of the development of domestic scientific and technologically innovative production of technical rehabilitation devices and prosthetic and orthopedic products (TSR and prosthetic devices), as well as a wide market for their provision, including as mandatory and compensatory tools to increase the participation of disabled people in the creation of products and services in the actively developing labor market. The object of the research is scientific and technological achievements, industries, and clusters involved in the development and creation of technologically advanced rehabilitation aids to increase the inclusion of disabled people. The subject of the study is the impact of assistive technologies on the reintegration of disabled people into society. The scientific hypothesis is an attempt to prove that with the development of domestic scientific and technical production of assistive technologies, including prosthetics, the level of inclusion of persons with disabilities in society and the labor market should increase, and the financial accessibility of their suspension should be ensured. In the process of the research, the method of analysis and structuring of domestic and foreign experience in the development and creation of multifunctional rehabilitation devices as an instrument of inclusive mechanism that helps persons with disabilities to socialize and realize their labor potential in the labor market was used. The following conclusions were made based on the results of the research: state guarantees of accessibility and quality of rehabilitation equipment products, and their constant innovative modernization are essential for the disabled, which has a positive impact on the reintegration of persons with disabilities into the actively developing society and labor market; authorities at all levels need immediately begin to create a labor market for the disabled in connection with the actively advancing robotization and digitalization, and the use of artificial intelligence.

Whole-Body Safety-Critical Control Design of an Upper Limb Prosthesis for Vision-Based Manipulation and Grasping

Whole-Body Safety-Critical Control Design of an Upper Limb Prosthesis for Vision-Based Manipulation and Grasping

An Efficient Framework for Personalizing EMG-Driven Musculoskeletal Models Based on Reinforcement Learning.

This study aimed to develop a novel framework to quickly personalize electromyography (EMG)-driven musculoskeletal models (MMs) as efferent neural interfaces for upper limb prostheses. Our framework adopts a generic upper-limb MM as a baseline and uses an artificial neural network-based policy to fine-tune the model parameters for MM personalization. The policy was trained by reinforcement learning (RL) to heuristically adjust the MM parameters to maximize the accuracy of estimated hand and wrist motions from EMG inputs. Our present framework was compared to the baseline MM and a widely used MM parameter optimization method: simulated annealing (SA). An offline evaluation was performed to first quantify the time required for MM personalization and the kinematics estimation accuracy of personalized MMs based on data collected from able-bodied subjects. Then, in an online evaluation, additional human subjects, including an individual with a transradial amputation, performed a virtual hand posture matching task using generic and personalized MMs. Results showed that compared to the baseline generic MM, personalized MMs estimated joint motion with lower error in both offline (p<0.05) and online tests (p=0.014), demonstrating the benefit of MM personalization. The RL-based framework performed model optimization in under one second on average in cases that took SA over 13 minutes and yielded comparable kinematics estimations both offline and online. Hence, our present personalization framework can be a practical solution for the daily use of EMG-driven MMs in prostheses or other assistive devices.