Myoelectric Hand Research Articles

A Design of Biomimetic Prosthetic Hand

Many patients with upper limb defects desire myoelectric prosthetic hands, but they are still not used for some reasons. One of the most significant reasons is its external appearance, which has the discomfort caused by the structural difference between a human hand and a robotic link. The structure must be based on human anatomy to create a more natural-looking prosthesis. This study designed a biomimetic prosthetic hand with bones, ligaments, tendons, and multiple muscles based on the human musculoskeletal system. We verified the proposed prosthetic hand using the viscoelastic angle sensor to determine whether it works like a human hand. We also compared the finger force of the prosthetic hand with that of a human finger. It could be capable of controlling the angle and the stiffness of the joint by multiple extensor and flexor muscles, like humans.

Improving the Robustness of Human-Machine Interactive Control for Myoelectric Prosthetic Hand During Arm Position Changing.

Robust classification of natural hand grasp type based on electromyography (EMG) still has some shortcomings in the practical prosthetic hand control, owing to the influence of dynamic arm position changing during hand actions. This study provided a framework for robust hand grasp type classification during dynamic arm position changes, improving both the “hardware” and “algorithm” components. In the hardware aspect, co-located synchronous EMG and force myography (FMG) signals are adopted as the multi-modal strategy. In the algorithm aspect, a sequential decision algorithm is proposed by combining the RNN-based deep learning model with a knowledge-based post-processing model. Experimental results showed that the classification accuracy of multi-modal EMG-FMG signals was increased by more than 10% compared with the EMG-only signal. Moreover, the classification accuracy of the proposed sequential decision algorithm improved the accuracy by more than 4% compared with other baseline models when using both EMG and FMG signals.

Classification and simulation of process of linear change for grip force at different grip speeds by using supervised learning based on sEMG

Bionic prosthesis can help the disabled rebuild the athletic ability of their missing limbs. Biomimetic hand controlled by surface electromyography (sEMG) has been widely studied in recent years. However, a lot of researchers focus on the field of movement recognition, ignoring the precise control of grip speed and force in the design of the prosthesis. The study presents a new means to simulate the process of linear changes of grip force by using sEMG, which provides a simple and practicable way of precise control of the speed and strength of force for the myoelectric hand. We design a synchronous acquisition device for sEMG signal and grip force signal. A novel preprocessing methodology for the raw sEMG signal is introduced, which is adaptive filter based on the Normalized Least Mean Square (NLMS). Compared the traditional preprocessing method that uses a notch filter for removing power line interference (50 Hz), the presented method can effectively remove power line interference and its harmonic wave components, and it completely reserves useful information contained in the sEMG signal. The linear change process of grip force at five grip speeds are studied. A simple and efficient method for muscle contraction state detection is used. Six supervised classification algorithms are used to identify these grip patterns and achieve a highest recognition rate of over 99%. Five supervised regression algorithms are applied to predict the value of grip force and establish the mapping relationship between sEMG and force. The experimental results demonstate that the method can simulate the linear change process of grip force well, and it achieves a minimum standard deviation of 0.14272.

Research on EMG-based Classification of Hand Movements using Four Electrodes Arrangements on Forearm

Although various types of myoelectric prosthetic hands using surface electromyogram have been developed, the placement of electrodes to acquire myoelectric potential needs to be adjusted by a specialist, and myoelectric potential differs from individual to individuals, so it needs to be adjusted to the individual user. Also, it is not covered by health insurance in Japan, and there are technical fees for the product price and initial setup. We propose a method of electrode placement and pretreatment that does not require much expertise by placing electrodes at the mid-forearm in the form of a wristband to improve them. In this study, we report the effects of muscle potential measurements on the pattern discriminator and the estimation of the useful measurement points by fixing four electrodes arranged in the form of wristbands. In this proposed method, the accuracy of identification is more than 95 %.

Control of myoelectric prosthetic hand with a novel proximity-tactile sensor

Control of myoelectric prosthetic hand with a novel proximity-tactile sensor

Hardware Design of Low Cost Myoelectric Controlled Prosthetic Hand For Engineering Laboratory

This study presents a low cost, two-channel, on-off type, constant speed myo-electric controlled prosthetic hand project as an educational tool for Biomedical/Control/Mechatronic Engineering. Surface Electromyogram (sEMG) signals were recorded from the muscles of flexor carpi ulnaris and extensor digitorium on the forearm. Signal conditioning of these signals is performed in the analog stages of the system, before passing it on to a microcontroller for further filtering and implementation of control. The control logic is simplified to represent the muscles as being active or inactive, resulting in a very simple on/off control based on the two signals. The on/off control signals are used to drive a myo-electric controlled prosthetic hand-powered by a hobby RC servo motor. A printed circuit board has been designed for the analog stages of the system, and a simple Arduino Microcontroller is used for the digital stages. Other commercial off-the-shelf components were used to keep the cost of the hardware and the software components as low as possible. This project was used as a teaching aid for the final year undergraduate students to demonstrate the use of simple myo-electric signal processing and control techniques.

Improvements in hand functions and changes in proximal muscle activities in myoelectric prosthetic hand users at home: a case series.

Adaptation in proximal muscles for daily motor tasks after sustained use of a prosthetic hand has not been fully understood. This study aimed to investigate changes in hand functions and activities of proximal muscles after multiple weeks of using a myoelectric prosthetic hand at home. Repeated measures. Four people with traumatic upper-limb loss used a myoelectric prosthetic hand (bebionic) at home over the 6- to 8-week period. A user survey, Orthotics and Prosthetics User Survey for Upper Extremity Functional Status 2.0, was used to measure upper-limb functions and the degree of using the prosthetic hand each week. Their hand functions, muscle activities, and grip-specific neuromuscular effort were evaluated by the Southampton Hand Assessment Procedure at the preassessment and postassessment sessions (PRE and POST, respectively). All subjects increased Southampton Hand Assessment Procedure scores at PRE compared with POST with subject-specific changes in muscle activations. In a detail, at POST, subject 1 reduced the shoulder muscle activity compared with PRE, while at POST, subject 2 reduced biceps activity compared with PRE. At POST, greater pectoralis activity and reduced trapezius activity were observed in subject 3, and greater activity in those two muscles was found in subject 4 compared with PRE. After multiple weeks of using the myoelectric prosthetic hands, their hand functions during ADL tasks were improved and changes in the muscle activities were found.

A Haptic Sleeve as a Method of Mechanotactile Feedback Restoration for Myoelectric Hand Prosthesis Users.

Current myoelectric upper limb prostheses do not restore sensory feedback, impairing fine motor control. Mechanotactile feedback restoration with a haptic sleeve may rectify this problem. This randomised crossover within-participant controlled study aimed to assess a prototype haptic sleeve's effect on routine grasping tasks performed by eight able-bodied participants. Each participant completed 15 repetitions of the three tasks: Task 1—normal grasp, Task 2—strong grasp and Task 3—weak grasp, using visual, haptic, or combined feedback All data were collected in April 2021 in the Scottish Microelectronics Centre, Edinburgh, UK. Combined feedback correlated with significantly higher grasp success rates compared to the vision alone in Task 1 (p < 0.0001), Task 2 (p = 0.0057), and Task 3 (p = 0.0170). Similarly, haptic feedback was associated with significantly higher grasp success rates compared to vision in Task 1 (p < 0.0001) and Task 2 (p = 0.0015). Combined feedback correlated with significantly lower energy expenditure compared to visual feedback in Task 1 (p < 0.0001) and Task 3 (p = 0.0003). Likewise, haptic feedback was associated with significantly lower energy expenditure compared to the visual feedback in Task 1 (p < 0.0001), Task 2 (p < 0.0001), and Task 3 (p < 0.0001). These results suggest that mechanotactile feedback provided by the haptic sleeve effectively augments grasping and reduces its energy expenditure.

Development of Self-Powered 5-Finger Pneumatically Driven Hand Prosthesis Using Supination of Forearm

Recently, myoelectric hand prostheses produced by the combination of 3D-CAD and printer have gained attention. 3D printing of hand prosthesis has resulted in cost reduction. However, when an electric actuator with reduction gears is used as the driving source of the hand prosthesis, the joint rigidity becomes high; therefore, compliance control is required to grasp soft target objects. In this study, we propose a pneumatically driven hand prosthesis using a flexible bellows actuator. The hand prosthesis is lightweight and inexpensive because it is self-powered and generates compressed air through the supination motion of the user’s forearm instead of an external compressor, which is essential for conventional pneumatic systems. Stable flexible grasping of the target object was achieved by driving a five-finger hand using this system.

Limb Prostheses: Industry 1.0 to 4.0: Perspectives on Technological Advances in Prosthetic Care.

Technological advances from Industry 1.0 to 4.0, have exercised an increasing influence on prosthetic technology and practices. This paper explores the historical development of the sector within the greater context of industrial revolution. Over the course of the first and up the midpoint of the second industrial revolutions, Industry 1.0 and 2.0, the production and provision of prosthetic devices was an ad hoc process performed by a range of craftspeople. Historical events and technological innovation in the mid-part of Industry 2.0 created an inflection point resulting in the emergence of prosthetists who concentrated solely on hand crafting and fitting artificial limbs as a professional specialty. The third industrial revolution, Industry 3.0, began transforming prosthetic devices themselves. Static or body powered devices began to incorporate digital technology and myoelectric control options and hand carved wood sockets transitioned to laminated designs. Industry 4.0 continued digital advancements and augmenting them with data bases which to which machine learning (M/L) could be applied. This made it possible to use modeling software to better design various elements of prosthetic componentry in conjunction with new materials, additive manufacturing processes and mass customization capabilities. Digitization also began supporting clinical practices, allowing the development of clinical evaluation tools which were becoming a necessity as those paying for devices began requiring objective evidence that the prosthetic technology being paid for was clinically and functionally appropriate and cost effective. Two additional disruptive dynamics emerged. The first was the use of social media tools, allowing amputees to connect directly with engineers and tech developers and become participants in the prosthetic design process. The second was innovation in medical treatments, from diabetes treatments having the potential to reduce the number of lower limb amputations to Osseointegration techniques, which allow for the direct attachment of a prosthesis to a bone anchored implant. Both have the potential to impact prosthetic clinical and business models. Questions remains as to how current prosthetic clinical practitioners will respond and adapt as Industry 4.0 as it continues to shape the sector.

Multichannel haptic feedback unlocks prosthetic hand dexterity

Loss of tactile sensations is a major roadblock preventing upper limb-absent people from multitasking or using the full dexterity of their prosthetic hands. With current myoelectric prosthetic hands, limb-absent people can only control one grasp function at a time even though modern artificial hands are mechanically capable of individual control of all five digits. In this paper, we investigated whether people could precisely control the grip forces applied to two different objects grasped simultaneously with a dexterous artificial hand. Toward that end, we developed a novel multichannel wearable soft robotic armband to convey artificial sensations of touch to the robotic hand users. Multiple channels of haptic feedback enabled subjects to successfully grasp and transport two objects simultaneously with the dexterous artificial hand without breaking or dropping them, even when their vision of both objects was obstructed. Simultaneous transport of the objects provided a significant time savings to perform the deliveries in comparison to a one-at-a-time approach. This paper demonstrated that subjects were able to integrate multiple channels of haptic feedback into their motor control strategies to perform a complex simultaneous object grasp control task with an artificial limb, which could serve as a paradigm shift in the way prosthetic hands are operated.

근전전동의수의 손동작 변환 방법에 대한 정량적 분석

A control algorithm is applied to commercial myoelectric hand prostheses for converting various hand gestures. However, since the conversion efficiency of these algorithms has never been quantitatively analyzed, it is necessary to do so. Therefore, herein, we quantitatively analyze conversion algorithms applied to the commercial myoelectric hand prosthesis and the recently proposed intuitive conversion algorithm to verify its usability. First, the structure of each mode conversion algorithm is described, as well as its association with the final hand gesture state for efficient conversion. Subsequently, the usability of the proposed algorithm is verified by comparing the number of conversions to the number of hand gestures used by each algorithm. According to the comparison results, the algorithm is easily used by upper limb amputees because the number of conversions is relatively reduced. From the experimental results, we demonstrated the usability of the proposed algorithm and its relevance to upper limb amputees.

Improving Robotic Hand Prosthesis Control With Eye Tracking and Computer Vision: A Multimodal Approach Based on the Visuomotor Behavior of Grasping

The complexity and dexterity of the human hand make the development of natural and robust control of hand prostheses challenging. Although a large number of control approaches were developed and investigated in the last decades, limited robustness in real-life conditions often prevented their application in clinical settings and in commercial products. In this paper, we investigate a multimodal approach that exploits the use of eye-hand coordination to improve the control of myoelectric hand prostheses. The analyzed data are from the publicly available MeganePro Dataset 1, that includes multimodal data from transradial amputees and able-bodied subjects while grasping numerous household objects with ten grasp types. A continuous grasp-type classification based on surface electromyography served as both intent detector and classifier. At the same time, the information provided by eye-hand coordination parameters, gaze data and object recognition in first-person videos allowed to identify the object a person aims to grasp. The results show that the inclusion of visual information significantly increases the average offline classification accuracy by up to 15.61 ± 4.22% for the transradial amputees and of up to 7.37 ± 3.52% for the able-bodied subjects, allowing trans-radial amputees to reach average classification accuracy comparable to intact subjects and suggesting that the robustness of hand prosthesis control based on grasp-type recognition can be significantly improved with the inclusion of visual information extracted by leveraging natural eye-hand coordination behavior and without placing additional cognitive burden on the user.

Differences in Two Multiarticulating Myoelectric Hands for Facilitating Activities of Daily Living in Individuals with Transradial Amputation: A Cross-Sectional Study

ABSTRACT Introduction Research with multiarticulating prosthetic hands on patient-reported ease of activities of daily living (ADLs) and usefulness is still limited. This study aimed at comparing ease of ADL performance and usefulness of two common multiarticulating prosthetic hands. Methods Twenty subjects with transradial amputation wearing the bebionic (n = 10) or i-Limb (n = 10) hands were assessed with a hybrid Orthotics and Prosthetics User Survey–Upper Extremity Functional Status (OPUS-UEFS)/Prosthetic Upper Extremity Functional Index (PUFI) outcome measure previously used in a study with another multigrip prosthetic hand. Results There were no significant differences between the bebionic and i-Limb hands. However, the analysis of individual activities revealed that each multiarticulating hand had specific strengths and weaknesses compared with a historic control group with conventional myoelectric hands. Discussion Both multiarticulating hands may improve ease of performing ADLs compared with conventional myoelectric hands. However, more grip types available do not necessarily result in greater ease or usefulness compared with advanced hands with fewer grip types. Conclusions Clinicians must match the patients' functional needs with the differential functional profiles of the available multiarticulating hands. Clinical Relevance The present study is the first to provide comparative patient-reported outcomes on 3 multigrip prosthetic hands as well as standard myoelectric hands in 23 common ADLs. The distinct patient-reported ease and usefulness profiles of the different hands may inform and support clinicians' decision-making on hand selection for individual patients with transradial amputation.

A Mass-Producible Washable Smart Garment with Embedded Textile EMG Electrodes for Control of Myoelectric Prostheses: A Pilot Study.

Electromyography (EMG) is the resulting electrical signal from muscle activity, commonly used as a proxy for users’ intent in voluntary control of prosthetic devices. EMG signals are recorded with gold standard Ag/AgCl gel electrodes, though there are limitations in continuous use applications, with potential skin irritations and discomfort. Alternative dry solid metallic electrodes also face long-term usability and comfort challenges due to their inflexible and non-breathable structures. This is critical when the anatomy of the targeted body region is variable (e.g., residual limbs of individuals with amputation), and conformal contact is essential. In this study, textile electrodes were developed, and their performance in recording EMG signals was compared to gel electrodes. Additionally, to assess the reusability and robustness of the textile electrodes, the effect of 30 consumer washes was investigated. Comparisons were made between the signal-to-noise ratio (SNR), with no statistically significant difference, and with the power spectral density (PSD), showing a high correlation. Subsequently, a fully textile sleeve was fabricated covering the forearm, with 14 textile electrodes. For three individuals, an artificial neural network model was trained, capturing the EMG of 7 distinct finger movements. The personalized models were then used to successfully control a myoelectric prosthetic hand.

Electrotactile Feedback Improves Grip Force Control and Enables Object Stiffness Recognition While Using a Myoelectric Hand.

Current myoelectric hands are limited in their ability to provide effective sensory feedback to the users, which highly affects their functionality and utility. Although the sensory information of a myoelectric hand can be acquired with equipped sensors, transforming the sensory signals into effective tactile sensations on users for functional tasks is a largely unsolved challenge. The purpose of this study aims to demonstrate that electrotactile feedback of the grip force improves the sensorimotor control of a myoelectric hand and enables object stiffness recognition. The grip force of a sensorized myoelectric hand was delivered to its users via electrotactile stimulation based on four kinds of typical encoding strategies, including graded (G), linear amplitude (LA), linear frequency (LF), and biomimetic (B) modulation. Object stiffness was encoded with the change of electrotactile sensations triggered by final grip force, as the prosthesis grasped the objects. Ten able-bodied subjects and two transradial amputees were recruited to participate in a dual-task virtual eggs test (VET) and an object stiffness discrimination test (OSDT) to quantify the prosthesis users' ability to handle fragile objects and recognize object stiffnesses, respectively. The quantified results showed that with electrotactile feedback enabled, the four kinds of encoding strategies allowed subjects to better able to handle fragile objects with similar performance, and the subjects were able to differentiate four levels of object stiffness at favorable accuracies (>86%) and high manual efficiency. Strategy LA presented the best stiffness discrimination performance, while strategy B was able to reduce the discrimination time but the discrimination accuracy was not better than the other three strategies. Electrotactile feedback also enhanced prosthesis embodiment and improved the users' confidence in prosthetic control. Outcomes indicate electrotactile feedback can be effectively exploited by the prosthesis users for grip force control and object stiffness recognition, proving the feasibility of functional sensory restoration of myoelectric prostheses equipped with electrotactile feedback.

Simultaneous sEMG Recognition of Gestures and Force Levels for Interaction With Prosthetic Hand.

The natural interaction between the prosthetic hand and the upper limb amputation patient is important and directly affects the rehabilitation effect and operation ability. Most previous studies only focused on the interaction of gestures but ignored the force levels. This paper proposes a simultaneous recognition method of gestures and forces for interaction with a prosthetic hand. The multitask classification algorithm based on a convolutional neural network (CNN) is designed to improve recognition efficiency and ensure recognition accuracy. The offline experimental results show that the algorithm proposed in this study outperforms other methods in both training speed and accuracy. To prove the effectiveness of the proposed method, a myoelectric prosthetic hand integrated with tactile sensors is developed, and surface electromyography (sEMG) datasets of healthy persons and amputees are built. The online experimental results show that the amputee can control the prosthetic hand to continuously make gestures under different force levels, and the effect of hand coordination on the hand perception of amputees is explored. The results show that gesture classification operation tasks with different force levels based on sEMG signals can be accurately recognized and comfortably interact with prosthetic hands in real time. It improves the amputees' operation ability and relieves their muscle fatigue.

A case report in which a myoelectric prosthetic hand was effective for a physiotherapist with an adult congenital left forearm deficiency

A case report in which a myoelectric prosthetic hand was effective for a physiotherapist with an adult congenital left forearm deficiency

Modular 3D Printed Prosthetic Hand – Amputee Tests with the Touch Hand 4

The popularity of myoelectric prosthetic hands increases as technological advances allows for the sophistication on prosthetic devices to become more affordable. A 3D printed myoelectric prosthetic hand was designed to ensure that it is cost-effective whilst having functionality similar to commercially available prosthetic hands. A three-fingered prosthetic hand was designed to grip objects of different shapes using one grip. An electromyography control system was investigated by performing tests on an able-bodied person and observing the results. The electromyography control system for the prosthetic hand was then designed. The prosthetic hand was then tested by an amputee by gripping objects of different shapes and sizes to determine its practicality of the prosthetic hand. The prosthetic hand was capable of picking up the object in 50% of its tests and showed a grip strength of 30.4 N and a response time of 4.5 seconds.

Effects of Single-handed and Dual-handed Tasks on Myoelectric Hand Prosthesis Operability of Unilateral Transradial Amputees

Effects of Single-handed and Dual-handed Tasks on Myoelectric Hand Prosthesis Operability of Unilateral Transradial Amputees