Myoelectric Research Articles

A bioinspired neuromuscular system enabled by flexible electro-optical N2200 nanowire synaptic transistor

Mimicking the functional traits of the muscle system evolves the development of the neuromorphic prosthetic limbs. Herein, a bioinspired neuromuscular system was constructed by connecting an information processor that uses a flexible electro-optical synaptic transistor (FNST) to an effector that uses artificial muscle fibers. In this system, the response of artificial muscle fibers, which imitate the movement of biological muscle fibers, is manipulated by neuromorphic synaptic devices. The FNST is regulated by light pulses and electrical spikes to emulate biological synaptic functions, and thereby applied in secure communication. The feasibility of n-type organic nanowires acting as the channels for neuromorphic devices was demonstrated. Attributing to the flexibility of the n-type organic semiconductor N2200 nanowires, the current of the FNST retains >85% of its initial value after the 5000 bending cycles to radius = 1 cm. The tolerance of bending of the FNST implies its potential applications in wearable electronics. This work offers an approach to potentially advancing electronic skin, neuro-controlled robots, and neuromorphic prosthetic limbs.

Dynamic gait stability and stability symmetry for people with transfemoral amputation: A case-series of 19 individuals with bone-anchored limbs

For some individuals with severe socket-related problems, prosthesis osseointegration directly connects a prosthesis to the residual limb creating a bone-anchored limb (BAL). We compared dynamic gait stability and between-limb stability symmetry, as measured by the Margin of Stability (MoS) and the Normalized Symmetry Index (NSI), for people with unilateral transfemoral amputation before and one-year after BAL implantation. The MoS provides a mechanical construct to assess dynamic gait stability and infer center of mass and limb control by relating the center of mass and velocity to the base of support. Before and one-year after BAL implantation, 19 participants walked overground at self-selected speeds. We quantified dynamic gait stability anteriorly and laterally at foot strike and at the minimum lateral MoS value. After implantation, we observed decreased lateral MoS at foot strike for the amputated (MoS mean(SD) %height; pre: 6.6(2.3), post: 5.9(1.3), d = 0.45) and intact limb (pre: 6.2(1.2), post: 5.8(1.0), d = 0.38) and increased between-limb MoS symmetry at foot strike (NSI mean(SD) %; anterior–pre: 10.3(7.3), post: 8.4(3.6), d = 0.23; lateral–pre: 18.8(12.4), post: 12.4(4.9), d = 0.47) and at minimum lateral stability (pre: 28.1(18.1), post: 19.2(6.8), d = 0.50). Center of mass control using a BAL resulted in dynamic gait stability more similar between limbs and may have reduced the adoption of functional asymmetries. We suggest that improved between-limb MoS symmetry after BAL implantation is likely due to subtle changes in individual limb MoS values at self-selected walking speeds resulting in an overall positive impact on fall risk through improved center of mass and prosthetic limb control.

Proximity Perception-Based Grasping Intelligence: Toward the Seamless Control of a Dexterous Prosthetic Hand

Proximity Perception-Based Grasping Intelligence: Toward the Seamless Control of a Dexterous Prosthetic Hand

Semi-Autonomous Grasping Control of Prosthetic Hand and Wrist Based on Motion Prior Field

Semi-Autonomous Grasping Control of Prosthetic Hand and Wrist Based on Motion Prior Field

Unveiling EMG semantics: a prototype-learning approach to generalizable gesture classification

Objective. Upper limb loss can profoundly impact an individual’s quality of life, posing challenges to both physical capabilities and emotional well-being. To restore limb function by decoding electromyography (EMG) signals, in this paper, we present a novel deep prototype learning method for accurate and generalizable EMG-based gesture classification. Existing methods suffer from limitations in generalization across subjects due to the diverse nature of individual muscle responses, impeding seamless applicability in broader populations. Approach. By leveraging deep prototype learning, we introduce a method that goes beyond direct output prediction. Instead, it matches new EMG inputs to a set of learned prototypes and predicts the corresponding labels. Main results. This novel methodology significantly enhances the model’s classification performance and generalizability by discriminating subtle differences between gestures, making it more reliable and precise in real-world applications. Our experiments on four Ninapro datasets suggest that our deep prototype learning classifier outperforms state-of-the-art methods in terms of intra-subject and inter-subject classification accuracy in gesture prediction. Significance. The results from our experiments validate the effectiveness of the proposed method and pave the way for future advancements in the field of EMG gesture classification for upper limb prosthetics.

Simulation and experimental study on rope driven artificial hand and driven motor.

Prosthetic hands have the potential to replace human hands. Using prosthetic hands can help patients with hand loss to complete the necessary daily living actions. This paper studies the design of a bionic, compact, low-cost, and lightweight 3D printing humanoid hand. The five fingers are underactuated, with a total of 9 degrees of freedom. In the design of an underactuated hand, it is a basic element composed of an actuator, spring, rope, and guide system. A single actuator is providing power for five fingers. And the dynamic simulation is carried out to calculate the motion trajectory effect. In this paper, the driving structure of the ultrasonic motor was designed, and the structural size of the ultrasonic motor vibrator was determined by modal and transient simulation analysis, which replace the traditional brake, realize the lightweight design of prosthetic hand, improve the motion accuracy and optimize the driving performance of prosthetic hand. By replacing traditional actuators with new types of actuators, lightweight design of prosthetic hands can be achieved, improving motion accuracy and optimizing the driving performance of prosthetic hands.

Electromyography signal based hand gesture classification system using Hilbert Huang transform and deep neural networks

This research aims to provide the groundwork for smartly categorizing hand movements for use with prosthetic hands. The hand motions are classified using surface electromyography (sEMG) data. In reaction to a predetermined sequence of fibre activation, every single one of our muscles contracts. They could be useful in developing control protocols for bio-control systems, such human-computer interaction and upper limb prostheses. When focusing on hand gestures, data gloves and vision-based approaches are often used. The data glove technique requires tedious and unnatural user engagement, whereas the vision-based solution requires significantly more expensive sensors. This research offered a Deep Neural Network (DNN) automated hand gesticulation recognition system based on electromyography to circumvent these restrictions. This work primarily aims to augment the concert of the hand gesture recognition system via the use of an artificial classifier. To advance the recognition system's classification accuracy, this study explains how to build models of neural networks and how to use signal processing methods. By locating the Hilbert Huang Transform (HHT), one may get the essential properties of the signal. When training a DNN classifier, these characteristics are sent into it. The investigational results reveal that the suggested technique accomplishes a better categorization rate (98.5 % vs. the alternatives).

AI-powered Neuroprosthetics for brain-computer interfaces (BCIs)

Imagine a world where individuals with lost or impaired sensory or motor function can regain independence and control through technology. This is the promise of neuroprosthetics, a rapidly evolving field that bridges the gap between the nervous system and external devices. Neuroprosthetics encompass a range of implanted or external devices designed to: Substitute for a malfunctioning part of the nervous system. Assist in the recovery or enhancement of lost or impaired function. Augment existing capabilities, creating new possibilities. These devices interact with the nervous system using various methods, including: Electrical stimulation: Directly stimulating nerves or brain tissue to evoke desired responses. Recording brain activity: Capturing electrical signals generated by the brain for further processing and interpretation. Common applications of neuroprosthetics include: Cochlear implants: Restoring hearing in individuals with severe hearing loss. Deep brain stimulation (DBS): Treating movement disorders like Parkinson's disease and essential tremor. Bionic limbs: Providing control of prosthetic arms and legs for individuals with limb loss. Brain-computer interfaces (BCIs): Enabling communication and control of external devices using brain signals alone. Neuroprosthetics offer a glimpse into the future of medicine and technology. With ongoing advancements, these devices have the potential to revolutionize how we treat neurological conditions, restore lost abilities, and even enhance human potential. However, significant challenges remain, including ensuring long-term safety, improving accuracy and reliability, and addressing ethical considerations. As research continues, neuroprosthetics holds immense potential to improve the lives of millions and redefine what it means to be human. The integration of artificial intelligence (AI) with neuroprosthetics has marked a significant milestone in the development of brain-computer interfaces (BCIs). This emerging synergy aims to enhance the quality of life for individuals with disabilities by restoring lost sensory, motor, and cognitive functions. This review article explores the advancements in AI-powered neuroprosthetics for BCIs, focusing on their design, functionality, and the ethical considerations that accompany their integration into medical practice.

Ultrasensitive, Highly Stable, and Stretchable Strain Sensor Using Gated Liquid Metal Channel

AbstractDeveloping stretchable strain sensors with high sensitivity and stability is crucial for various applications such as prosthetic hands, human health monitoring, and human‐machine interactions. However, achieving these qualities simultaneously remains challenging. Here, an inherently stretchable strain sensor is presented that integrates ultrahigh sensitivity and robust stability, enabling stretch, press, or bend sensing capabilities. This sensor employs a softer elastomeric channel filled with liquid metal (LM) as the conductive path. A stiffer elastomer convex integrated into the channel serves as a strain‐manipulated gate, controlling opening gap of electrical current flow path. During deformation, the softer elastomer undergoes cross sectional reduction due to the Poisson effect, while the stiffer convex gate retains its geometry. This heterogeneous deformation behavior leads to significant contraction or closure of the LM channel, resulting in increased resistance and a remarkable enhancement in sensitivity by more than two orders of magnitude. The all‐soft design maintains exceptional stability even under extended or repetitive substantial deformations. With the ability to monitor subtle and large human body movements, detect grip actions of soft grippers reliably, and monitor the gradual and extended growth process of plants, this sensor holds significant potential for advancements in flexible electronics.

Cyborg insect repeatable self-righting locomotion assistance using bio-inspired 3D printed artificial limb

Cyborg insects have emerged as a promising solution for rescue missions, owing to their distinctive and advantageous mobility characteristics. These insects are outfitted with electronic backpacks affixed to their anatomical structures, which endow them with imperative communication, sensing, and control capabilities essential for effecting survivor retrieval. Nevertheless, the attachment of supplementary loads to the insect’s body can exert adverse effects on their intrinsic self-righting locomotion when confronted with fall or shock scenarios. To address this challenge, the present study introduces a bio-inspired 3D-printed artificial limb that serves to facilitate the maneuverability of cyborg insects amidst unpredictable conditions. Drawing inspiration from the natural self-righting motion exhibited by Coccinellidae, we have successfully identified a solution that can be transferred to the electronic backpack utilized by G. portentosa. Incorporation of the bio-inspired artificial wing-like limb has notably enabled the cyborg insect to achieve a remarkable tilting angle of 112°, thereby significantly amplifying the success ratio of self-righting under conditions closely emulating those prevalent in disaster areas. Moreover, we have replicated the expansion and contraction kinematics to ensure seamless motion progression within confined spaces. Importantly, the fabricated device proffered in this study has been meticulously designed for facile reproducibility employing commonly available tools, thereby serving as an inspirational catalyst for fellow researchers engaged in the advancement of 3D-printed limb development aimed at expanding the functional capacities of cyborg insects.

Bionic Forearm using Arduino

The robotic arm, a sophisticated mechanical device similar to a bionic or prosthetic limb, emulates human arm functions across industries like industry, medicine, and space exploration. Its key components—controller, actuators, end effector—endow it with precise, efficient movement essential for diverse tasks. The controller's structure facilitates seamless motion, while actuators powered by electric or hydraulic systems enable varied actions. The end effector, at the arm's end, interacts with objects, broadening its operational capabilities. Overall, robotic arms epitomize precision and adaptability, proving invaluable in sectors requiring intricate manipulation and task execution.

Assessment of self-esteem and quality of life in patients with transtibial amputations using an exoskeletal prosthesis.

Lower limb amputation can have profound physical, psychological, and social effects on individuals. Assistive aids like prosthetic lower limbs can help an individual regain mobility and thereby affect their self-esteem and quality of life. To assess self-esteem and quality of life in patients with transtibial amputations who are using a transtibial prosthesis and explore the association between sociodemographic and amputation-related factors with self-esteem and prosthesis-related quality of life measures. A cross-sectional study. An orthotics and prosthetics center (Bhagwan Mahaveer Viklang Sahayata Samiti) in Jaipur, Rajasthan. Patients with transtibial amputations using exoskeletal prostheses between July and September 2022, in Jaipur, Rajasthan. The study used Rosenberg self-esteem questionnaire and Prosthesis Evaluation Questionnaire (PEQ) for self-esteem and quality of life assessment, respectively. Descriptive analysis was used to present the demographic details, and nonparametric tests examined the relationship between PEQ dimensions and sociodemographic variables. The study included 138 participants, primarily <40 years old (45.7%) and mostly engaged in high-activity occupations. Self-esteem mean score (SD) was 19.9 (3.9), with 89.1% exhibiting normal self-esteem. Prosthesis-related quality of life, showed high satisfaction across various domains, including ambulation, appearance, frustration, perceived response, residual limb health, social burden, sounds, utility, and well-being. No significant associations were found between sociodemographic factors, amputation-related variables, and self-esteem. However, PEQ scales showed associations with gender, age, occupation, type of amputation, and years of prosthesis use. These results highlight the potential advantages of exoskeletal prostheses in improving the standard of living for people with transtibial amputations. Further research is essential to develop targeted interventions for improving their overall quality of life.

Prosthetic Hand using EMG Signal

A prosthetic hand is a device used to enable disabled people to perform daily living activities. Low back pain is much more than a physical loss, it affects people's outlook on life and they begin to feel constantly disabled. The progression of these conditions can leave them mentally disabled or, if they can, cause personal death or worse, even if people remember their disability for a while. We create hand movements at the cheapest price to collaborate and enable disabled individuals to become more physically capable.

Research on Adaptive Grasping with a Prosthetic Hand Based on Perceptual Information on Hardness and Surface Roughness.

In order to solve the problems of methods that use a single form of sensing, the ease of causing deformation damage to the targets with a low hardness during grasping, and the slow sliding inhibition of a prosthetic hand when the grasping target slides, which are problems that exist in most current intelligent prosthetic hands, this study introduces an adaptive control strategy for prosthetic hands based on multi-sensor sensing. Using a force-sensing resistor (FSR) to collect changes in signals generated after contact with a target, a prosthetic hand can classify the target's hardness level and adaptively provide the desired grasping force so as to reduce the deformation of and damage to the target in the process of grasping. A fiber-optic sensor collects the light reflected by the object to identify its surface roughness, so that the prosthetic hand adaptively adjusts the sliding inhibition method according to the surface roughness information to improve the grasping efficiency. By integrating information on the hardness and surface roughness of the target, an adaptive control strategy for a prosthetic hand is proposed. The experimental results showed that the adaptive control strategy was able to reduce the damage to the target by enabling the prosthetic hand to achieve stable grasping; after grasping the target with an initial force and generating sliding, the efficiency of slippage inhibition was improved, the target could be stably grasped in a shorter time, and the hardness, roughness and weight ranges of targets that could be grasped by the prosthetic hand were enlarged, thus improving the success rate of stable grasping under extreme conditions.

Towards prosthetic limbs and assistive devices controlled via the myokinetic interface

Towards prosthetic limbs and assistive devices controlled via the myokinetic interface

Biomechanical effects of adding an articulating toe joint to a passive foot prosthesis for incline and decline walking.

Walking on sloped surfaces is challenging for many lower limb prosthesis users, in part due to the limited ankle range of motion provided by typical prosthetic ankle-foot devices. Adding a toe joint could potentially benefit users by providing an additional degree of flexibility to adapt to sloped surfaces, but this remains untested. The objective of this study was to characterize the effect of a prosthesis with an articulating toe joint on the preferences and gait biomechanics of individuals with unilateral below-knee limb loss walking on slopes. Nine active prosthesis users walked on an instrumented treadmill at a +5° incline and -5° decline while wearing an experimental foot prosthesis in two configurations: a Flexible toe joint and a Locked-out toe joint. Three participants preferred the Flexible toe joint over the Locked-out toe joint for incline and decline walking. Eight of nine participants went on to participate in a biomechanical data collection. The Flexible toe joint decreased prosthesis Push-off work by 2 Joules during both incline (p = 0.008; g = -0.63) and decline (p = 0.008; g = -0.65) walking. During incline walking, prosthetic limb knee flexion at toe-off was 3° greater in the Flexible configuration compared to the Locked (p = 0.008; g = 0.42). Overall, these results indicate that adding a toe joint to a passive foot prosthesis has relatively small effects on joint kinematics and kinetics during sloped walking. This study is part of a larger body of work that also assessed the impact of a prosthetic toe joint for level and uneven terrain walking and stair ascent/descent. Collectively, toe joints do not appear to substantially or consistently alter lower limb mechanics for active unilateral below-knee prosthesis users. Our findings also demonstrate that user preference for passive prosthetic technology may be both subject-specific and task-specific. Future work could investigate the inter-individual preferences and potential benefits of a prosthetic toe joint for lower-mobility individuals.

Vision-aided grasp classification: design and evaluation of compact CNN for prosthetic hands

Powered prosthetic hands capable of executing various grasp patterns are highly sought-after solutions for upper limb amputees. A crucial requirement for such prosthetic hands is the accurate identification of the intended grasp pattern and subsequent activation of the prosthetic digits accordingly. Vision-based grasp classification techniques offer improved coordination between amputees and prosthetic hands without physical contact. Deep learning methods, particularly Convolutional Neural Networks (CNNs), are utilized to process visual information for classification. The key challenge lies in developing a model that can effectively generalize across various object shapes and accurately classify grasp classes. To address this, a compact CNN model named GraspCNet is proposed, specifically designed for grasp classification in prosthetic hands. The use of separable convolutions reduces the computational burden, making it potentially suitable for real-time applications on embedded systems. The GraspCNet model is designed to learn and generalize from object shapes, allowing it to effectively classify unseen objects beyond those included in the training dataset. The proposed model was trained and tested using various standard object data sets. A cross-validation strategy has been adopted to perform better in seen and unseen object class scenarios. The average accuracy achieved was 82.22% and 75.48% in the case of seen, and unseen object classes respectively. In computer-based real-time experiments, the GraspCNet model achieved an accuracy of 69%. A comparative analysis with state-of-the-art techniques revealed that the proposed GraspCNet model outperformed most benchmark techniques and demonstrated comparable performance with the DcnnGrasp method. The compact nature of the GraspCNet model suggests its potential for integration with other sensing modalities in prosthetic hands.

The significance of frontal plane static alignment in anticipating dynamic knee moment among transtibial prosthesis users: A cross-sectional study

BackgroundIn the process of transtibial prosthetic fitting, alignment is the process of positioning the prosthetic foot relative to the residual limb. Changes in frontal plane alignment can impact knee moments during walking, which can either cause or, when aligned properly, prevent injuries. However, clinical evaluation of dynamic knee moments is challenging, limiting prosthetists' insights into dynamic joint loading. Typically, knee joint loading is assessed in static stance using the knee moment arm as a proxy for subsequent dynamic alignment. It remains uncertain if static alignment accurately represents actual dynamics during walking. Research questionIs the frontal knee moment arm in stance predictive for the knee moment arm and external knee adduction moment during gait in transtibial bone-anchored prosthesis users? MethodsIn this cross-sectional study, twenty-seven unilateral transtibial bone-anchored prosthesis users underwent data acquisition on the M-Gait instrumented treadmill. Static and dynamic measurements were conducted, and knee moment arm and external knee adduction moment were calculated. Pearson's correlation and linear regression analyses were performed to examine relationships between static and dynamic knee moment arms and external knee adduction moments. ResultsThe static knee moment arm showed significant associations with dynamic knee moment arm at the ground reaction force peaks (First: r=0.60, r2=35%, p<0.001; Second: r=0.62, r2=38%, p=0.001) and knee adduction moment (First: r=0.42, r2=17%, p=0.030; Second: r=0.59, r2=35%, p=0.001). A 1 mm between-subject difference in static knee moment arm corresponded, on average, with a 0.9% difference in knee adduction moment at the first peak and a 1.5% difference at the second peak of the ground reaction force. SignificanceWhile static alignment is important to optimize adduction moments during stance it may only partly mitigate excessive moments during gait. The fair correlation and limited percentage of explained variance underscores the importance of dynamic alignment in optimizing the body's dynamic load during walking.

Transhumeral prosthesis use affects upper body kinematics and kinetics

BackgroundTranshumeral (TH) limb loss leads to loss of body mass and reduced shoulder range of motion. Despite most owning a prosthesis, prosthesis abandonment is common. The consequence of TH limb loss and prosthesis use and disuse during gait may be compensation in the upper body, contributing to back pain or injury. Understanding the impact of not wearing a TH prosthesis on upper body asymmetries and spatial-temporal aspects of gait will inform how TH prosthesis use and disuse affects the body. Research questionDoes TH limb loss alter upper body asymmetries and spatial-temporal parameters during gait when wearing and not wearing a prosthesis compared to able-bodied controls? MethodsEight male TH limb loss participants and eight male control participants completed three gait trials at self-selected speeds. The TH limb loss group performed trials with and without their prosthesis. Arm swing, trunk angular displacement, trunk-pelvis moment, and spatial-temporal aspects were compared using non-parametric statistical analyses. ResultsBoth TH walking conditions showed greater arm swing in the intact limb compared to the residual (p≤0.001), resulting in increased asymmetry compared to the control group (p≤0.001). Without the prosthesis, there was less trunk flexion and lateral flexion compared to the control group (p≤0.001). Maximum moments between the trunk and pelvis were higher in the TH group than the control group (p≤0.05). Spatial-temporal parameters of gait did not differ between the control group and either TH limb loss condition. SignificanceProsthesis use affects upper body kinematics and kinetics, but does not significantly impact spatial-temporal aspects of gait, suggesting these are compensatory actions. Wearing a prosthesis helps achieve more normative upper body kinematics and kinetics than not wearing a prosthesis, which may help limit back pain. These findings emphasize the importance of encouraging at least passive use of prostheses for individuals with TH limb loss.

Evaluating the Ability of Brachial Plexus-Injured Patients to Control an Externally Powered (Myoelectric) Hand Prosthesis.

Restoration of hand function after traumatic brachial plexus injury (BPI) remains a formidable challenge. Traditional methods such as nerve or free muscle transfers yield suboptimal results. Advancements in myoelectric prostheses, characterized by novel signal acquisition and improved material technology, show promise in restoring functional grasp. This study evaluated the ability of adults with a BPI injury to control an externally powered prosthetic hand using nonintuitive signals, simulating the restoration of grasp with a myoelectric prosthesis. It also assessed the effectiveness of a comprehensive multidisciplinary evaluation in guiding treatment decisions. A multidisciplinary brachial plexus team assessed adults with compromised hand function due to BPI. The feasibility of amputation coupled with fitting of a myoelectric prosthesis for grasp reconstruction was evaluated. Participants' ability to control a virtual or model prosthetic hand using surface electromyography (EMG) as well as with contralateral shoulder motion-activated linear transducer signals was tested. The patient's input and injury type, along with the information from the prosthetic evaluation, were used to determine the reconstructive plan. The study also reviewed the number of participants opting for amputation and a myoelectric prosthetic hand for grasp restoration, and a follow-up survey was conducted to assess the impact of the initial evaluation on decision-making. Of 58 subjects evaluated, 47 (81%) had pan-plexus BPI and 42 (72%) received their initial assessment within 1 year post-injury. Forty-seven patients (81%) could control the virtual or model prosthetic hand using nonintuitive surface EMG signals, and all 58 could control it with contralateral uniscapular motion via a linear transducer and harness. Thirty patients (52%) chose and pursued amputation, and 20 (34%) actively used a myoelectric prosthesis for grasp. The initial evaluation was informative and beneficial for the majority of the patients, especially in demonstrating the functionality of the myoelectric prosthesis. The study indicates that adults with traumatic BPI can effectively operate a virtual or model myoelectric prosthesis using nonintuitive control signals. The simulation and multidisciplinary evaluation influenced informed treatment choices, with a high percentage of patients continuing to use the myoelectric prostheses post-amputation, highlighting its long-term acceptance and viability. Therapeutic Level IV. See Instructions for Authors for a complete description of levels of evidence.