Development of Upper Limb Prostheses: Current Progress and Areas for Growth

Recent trends and challenges of surface electromyography in prosthetic applications

Surface electromyography (sEMG) meets extensive applications in the field of prosthesis in the current period. The effectiveness of sEMG in prosthesis applications has been verified by numerous revolutionary developments and extensive research attempts. A large volume of research and literature works have explored and validated the vast use of these signals in prostheses as an assistive technology. The objective of this paper is to conduct a systematic review and offer a detailed overview of the work record in the prosthesis and myoelectric interfaces framework. This review utilized a systematic search strategy to identify published articles discussing the state-of-the-art applications of sEMG in prostheses (including upper limb prosthesis and lower limb prostheses). Relevant studies were identified using electronic databases such as PubMed, IEEE Explore, SCOPUS, ScienceDirect, Google Scholar and Web of Science. Out of 3791 studies retrieved from the databases, 188 articles were found to be potentially relevant (after screening of abstracts and application of inclusion-exclusion criteria) and included in this review. This review presents an investigative analysis of sEMG-based prosthetic applications to assist the readers in making further advancements in this field. It also discusses the fundamental advantages and disadvantages of using sEMG in prosthetic applications. It also includes some important guidelines to follow in order to improve the performance of sEMG-based prosthesis. The findings of this study support the widespread use of sEMG in prosthetics. It is concluded that sEMG-based prosthesis technology, still in its sprouting phase, requires significant explorations for further development. Supplementary investigations are necessary in the direction of making a seamless mechanism of biomechatronics for sEMG-based prosthesis by cohesive efforts of robotic researchers and biomedical engineers.

Impact of the choice of upper limb prosthesis mechanism on kinematics and dynamic quality

Upper limb prostheses can greatly improve the condition of amputees. However, prosthetic mechanisms have different topologies and there is no consensus on the choice of an appropriate mechanism. This paper evaluates the impact of prosthetic mechanism topology on the prosthesis' performance during daily tasks. The proposed multibody model is compared to four open-loop and one closed-loop existing mechanisms according to: (1) consumed energy, (2) global and local movement reconstruction errors during inverse kinematics, (3) movement smoothness, which reflects the dynamic appearance of the prosthesis, also called 'dynamic cosmesis'. Flexion-extension (FE) and pronation-supination (PS) tasks were studied in 15 healthy subjects. All parameters identified at least one group difference (p < 0.0001) in both tasks. Most closed-loop mechanisms (50% in FE and 100 % in PS) including the proposed model were among the most energy-efficient mechanisms. Out of all models, the proposed model was the most energy efficient in FE (2.07 ± 0.69 KJ) and in PS (0.25 ±  0.16 KJ). This model also reproduced the studied movements with the lowest errors (1.39 ± 0.2 mm in FE and 1.38 ± 0.25 mm in PS), especially at the forearm level. The results show that the wrist plays a major role in motion smoothness and that two series mechanisms have exhibited a poor dynamic cosmesis because of their higher jerk cost ((1.73 ± 0.30) × 10¹⁰) in FE and (9.29 ± 17) × 10¹³ in PS tasks)). Finally, the mechanism topology affects the performance of upper limb prostheses and represents a novel aspect in the prostheses design which can be applied to exoskeleton design.

Real-Time EMG Based Pattern Recognition Control for Hand Prostheses: A Review on Existing Methods, Challenges and Future Implementation

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.

Texture Discrimination using a Soft Biomimetic Finger for Prosthetic Applications

Soft robotic fingers have shown great potential for use in prostheses due to their inherent compliant, light, and dexterous nature. Recent advancements in sensor technology for soft robotic systems showcase their ability to perceive and respond to static cues. However, most of the soft fingers for use in prosthetic applications are not equipped with sensors which have the ability to perceive texture like humans can. In this work, we present a dexterous, soft, biomimetic solution which is capable of discrimination of textures. We fabricated a soft finger with two individually controllable degrees of freedom with a tactile sensor embedded at the fingertip. The output of the tac- tile sensor, as texture plates were palpated, was converted into spikes, mimicking the behavior of a biological mechanoreceptor. We explored the spatial properties of the textures captured in the form of spiking patterns by generating spatial event plots and analyzing the similarity between spike trains generated for each texture. Unique features representative of the different textures were then extracted from the spikes and input to a classifier. The textures were successfully classified with an accuracy of 94% when palpating at a rate of 42 mm/s. This work demonstrates the potential of providing amputees with a soft finger with sensing capabilities, which could potentially help discriminate between different objects and surfaces during activities of daily living (ADL) through palpation.

Targeted muscle reinnervation and prosthetic rehabilitation after limb loss

Over one million amputations occur annually world-wide. Often, amputation of the neoplastic limb is regarded as a surgical failure and the end of surgical care for the patient. Here, we highlight the advancements in extremity prostheses and surgical techniques that should change that mindset. Myoelectric prostheses, osseointegration, and targeted muscle reinnervation allow for more intuitive and easy to use devices, reduced pain, and greater quality of life for amputees.

Stable force-myographic control of a prosthetic hand using incremental learning

Force myography has been proposed as an appealing alternative to electromyography for control of upper limb prosthesis. A limitation of this technique is the non-stationary nature of the recorded force data. Force patterns vary under influence of various factors such as change in orientation and position of the prosthesis. We hereby propose an incremental learning method to overcome this limitation. We use an online sequential extreme learning machine where occasional updates allow continual adaptation to signal changes. The applicability and effectiveness of this approach is demonstrated for predicting the hand status from forearm muscle forces at various arm positions. The results show that incremental updates are indeed effective to maintain a stable level of performance, achieving an average classification accuracy of 98.75% for two subjects.

Forearm amputees' views of prosthesis use and sensory feedback

STUDY DESIGN

Qualitative descriptive.

INTRODUCTION

The lack of sensory feedback in today's hand prostheses has been in focus recently but the amputees' experiences need to be further investigated.

PURPOSE

To explore forearm amputees' views of prosthesis use and sensory feedback.

METHODS

Thirteen unilateral congenital or traumatic forearm amputees were interviewed. The transcribed text was subjected to content analysis.

RESULTS

Prostheses both facilitate and limit occupational performance. Appearance is important for identity and blending into society. The feeling of agency regarding the prostheses is present but not that of body ownership. Future expectations concerned improved mobility, cosmetics, and sensory feedback.

CONCLUSIONS

This study allows a deeper understanding of the complex relationship between a prosthetic device and the wearer. Today's prostheses allow the wearer to feel agency concerning the artificial limb but the lack of sensory feedback seems to be an important factor still blocking the achievement of body ownership of the prosthesis.

LEVEL OF EVIDENCE

Not applicable.

Regenerative Engineering and Bionic Limbs

Amputations of the upper extremity are severely debilitating, current treatments support very basic limb movement, and patients undergo extensive physiotherapy and psychological counselling. There is no prosthesis that allows the amputees near-normal function. With increasing number of amputees due to injuries sustained in accidents, natural calamities and international conflicts, there is a growing requirement for novel strategies and new discoveries. Advances have been made in technological, material and in prosthesis integration where researchers are now exploring artificial prosthesis that integrate with the residual tissues and function based on signal impulses received from the residual nerves. Efforts are focused on challenging experts in different disciplines to integrate ideas and technologies to allow for the regeneration of injured tissues, recording on tissue signals and feed-back to facilitate responsive movements and gradations of muscle force. A fully functional replacement and regenerative or integrated prosthesis will rely on interface of biological process with robotic systems to allow individual control of movement such as at the elbow, forearm, digits and thumb in the upper extremity. Regenerative engineering focused on the regeneration of complex tissue and organ systems will be realized by the cross-fertilization of advances over the past thirty years in the fields of tissue engineering, nanotechnology, stem cell science, and developmental biology. The convergence of toolboxes crated within each discipline will allow interdisciplinary teams from engineering, science, and medicine to realize new strategies, mergers of disparate technologies, such as biophysics, smart bionics, and the healing power of the mind. Tackling the clinical challenges, interfacing the biological process with bionic technologies, engineering biological control of the electronic systems, and feed-back will be the important goals in regenerative engineering over the next two decades.