Myoelectric Research Articles

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.

Study The Thermal Conductivity of Polymethyl Methacrylate Reinforcing with Cotton, Hair, and Burlap

This study includes the manufacture of two sets of composite materials consisting of polymethyl methacrylate reinforced with natural fibers. The first (R) group included eight layers of cotton with one layer of hair in the middle, and the second (O) group included six layers of cotton with three layers of burlap in the middle. The thermal conductivity of the two groups was checked using a Lee disc to identify the thermal conductivity (K), of group R 0.0771 w/m.c, and of group O 0.0754 w/m.c. These values are considered good as the material has good thermal insulation due to its low thermal conductivity values and is therefore suitable for use in prosthetic limbs as it can preserve the body temperature from external influences.

Deep Learning-based Thigh Muscle Investigation Using MRI For Prosthetic Development for Patients Undergoing Total Knee Replacement (TKR).

A prosthetic device is designed based on the quantitative analysis of muscle MRI which will improve the muscle control achieved with functional electrical stimulation/ guided robotic exoskeletons. Electromyography (EMG) provides muscle functionality information while MRI provides the physiological and functionality of muscles. The sensor feedbacks were used for the bionic prosthesis, but the length of muscle using image processing was not correlated. To investigate and perform qualitative and quantitative assessment of thigh muscle using MRI. The objective of the work is to improve the existing VAG signal classification method to diagnose abnormality using MRI for patients undergoing Total knee replacement (TKR). A deep learning method for qualitative and quantitative assessment of thigh muscle is done using MRI. In existing prosthetic devices, electrical measurements of a person's muscles are obtained using surface or implantable electrodes. Several methods were used for the classification and diagnostic processes. The existing methods have drawbacks in feature extraction and require experts to design the system. This work combines medical image processing and orthopaedic prosthetics to develop a therapeutic method. This design provides much more precise control of prosthetic limbs using the image processing technique. The hybrid CNN swarm-based method measures the muscle structure and functions. Along with the sensor readings, these details are combined for prosthetic control. The implementation was carried out in MATLAB, Sketchuppro, and Arduino IDE. A combined swarm intelligence and deep learning method were proposed for qualitative and quantitative assessment of thigh muscle. The prosthetic device choice was done from the scanned MRI image like Humerus-T prosthetics, segmental prosthesis and arthrodesis prosthesis. The investigation was done for the Total knee replacement (TKR) approach.

Selective intrafascicular stimulation of myelinated and unmyelinated nerve fibers through a longitudinal electrode: A computational study

Carbon nanotube (CNT) fiber electrodes have demonstrated exceptional spatial selectivity and sustained reliability in the context of intrafascicular electrical stimulation, as evidenced through rigorous animal experimentation. A significant presence of unmyelinated C fibers, known to induce uncomfortable somatosensory experiences, exists within peripheral nerves. This presence poses a considerable challenge to the excitation of myelinated Aβ fibers, which are crucial for tactile sensation. To achieve nuanced tactile sensory feedback utilizing CNT fiber electrodes, the selective stimulation of Aβ sensory afferents emerges as a critical factor.In confronting this challenge, the present investigation sought to refine and apply a rat sciatic-nerve model leveraging the capabilities of the COMSOL-NEURON framework. This approach enables a systematic evaluation of the influence exerted by stimulation parameters and electrode geometry on the activation dynamics of both myelinated Aβ and unmyelinated C fibers. The findings advocate for the utilization of current pulses featuring a pulse width of 600 μs, alongside the deployment of CNT fibers characterized by a diminutive diameter of 10 μm, with an exclusively exposed cross-sectional area, to facilitate reduced activation current thresholds. Comparative analysis under monopolar and bipolar electrical stimulation conditions revealed proximate activation thresholds, albeit with bipolar stimulation exhibiting superior fiber selectivity relative to its monopolar counterpart. Concerning pulse waveform characteristics, the adoption of an anodic-first biphasic stimulation modality is favored, taking into account the dual criteria of activation threshold and fiber selectivity optimization. Consequently, this investigation furnishes an efficacious stimulation paradigm for the selective activation of touch-related nerve fibers, alongside provisioning a comprehensive theoretical foundation for the realization of natural tactile feedback within the domain of prosthetic hand applications.

Exploring Attitude Change and Experiences of Undergraduate Students in a Goalball and Sitting Volleyball Focused Paralympic Skill Lab

ABSTRACT Rooted in contact theory, the Paralympic Skill Lab (PSL) program is designed to guide participants toward recognizing and understanding the experiences of disabled people to support new pathways of thought and changed attitudes. The purpose of this inquiry was to explore the experiences of college students in a goalball and sitting volleyball focused PSL program and their attitudes and perceptions about disability and disability sport. This study utilized an experiential qualitative approach. Participants were fifty-seven college students who took part in a PSL event. Data were collected via reflective writing responses. Written materials were analyzed inductively using a three-step process: immersion in the data, bracketing the data, and determining emergent themes. Analysis revealed three themes related to the participants’ experiences during the PSL event (a)“I realized that there was no need for having low expectations for Paralympic athletes:” Breaking down bias, ;(b) “It made them real people, with real thoughts and real beliefs:” Humanizing through contact, and (b) “My closed-minded thinking thought they all would have a prosthetic leg or have a mind disease:” Deconstructing assumptions. The findings support the value of PSL as a mechanism for learning and engagement through experience, with clear implications for practice. Future research should explore different types of Paralympic activities offered in the PSL program.

An enhancement of the Genium™ microprocessor-controlled knee improves safety and different aspects of the perceived prosthetic experience for unilateral and bilateral users.

Bilateral microprocessor-controlled prosthetic knee (MPK) users have unique needs in traversing environmental barriers compared to unilateral users. An enhancement to the Genium™/Genium X3™ MPK which included an updated ruleset, hydraulics, and new bilateral parameter presets was made to improve safety while stumbling and the smoothness of gait for all users while also improving the experience of bilateral users. The purpose of the study was to evaluate the effectiveness of the enhancements in a sample with unilateral and bilateral amputation. A convenience sample of MPK users was recruited from two sites in the USA in two phases. Assessments included the L-Test of Functional Mobility, Activity-specific Balance Confidence Scale, Prosthetic Limb User Survey of Mobility, a study-specific questionnaire, and the Comparative Activities of Daily Living (ADL) Questionnaire. Statistical significance of extracted data was tested with the Wilcoxon Rank-Sum Test for independent data and Wilcoxon Signed-Rank for paired data with an a priori significance level of p < 0.05. Unilateral subjects were age-matched to the group of bilateral subjects for between-groups and within-groups analyses. Twenty-six subjects (n = 26) were enrolled. Stumble frequency reduced 85% from 16.0 ± 39.7 to 2.4 ± 2.3 (p = 0.008) between baseline and final assessment overall. The bilateral group reported 50% (p = 0.009) and 57% (p = 0.009) greater relative improvement in patient-reported ease and safety, respectively, of completing ADLs compared to the unilateral group. The unilateral group reported residual limb pain and low back pain reduced from 2.3 to 1.4 (p = 0.020) and 3.8 to 1.8 (p = 0.027), respectively, whereas the bilateral group did not. Substantial reductions in stumbles, residual limb pain, and back pain were shown overall. These reductions were driven by the unilateral group who also showed improvements in comfort, exertion, and concentration while walking. The enhancements to the knee likely reduced some gait asymmetry for unilateral users. Improvements in patient-reported ease and safety of completing ADLs were shown overall and were driven by the bilateral group. This study shows further improvement in patient experience is achievable through innovation in MPK technology even for patients who appear to be functioning well.

Neuro-Dynamic Control of an Above Knee Prosthetic Leg

Neuro-Dynamic Control of an Above Knee Prosthetic Leg

Non-equilibrium compression achieving high sensitivity and linearity for iontronic pressure sensors

Flexible pressure sensors with high sensitivity and linearity are highly desirable for robot sensing and human physiological signal detection. However, the current strategies for stabilizing axial microstructures (e.g., micro-pyramids) are mainly susceptible to structural stiffening during compression, thereby limiting the realization of high sensitivity and linearity. Here, we report a bending-induced non-equilibrium compression process that effectively enhances the compressibility of microstructures, thereby crucially improving the efficiency of interfacial area growth of electric double layer (EDL). Based on this principle, we fabricate an iontronic flexible pressure sensor with vertical graphene (VG) array electrodes. Ultra-high sensitivity (185.09 kPa−1) and linearity (R2 = 0.9999) are realized over a wide pressure range (0.49 Pa–66.67 kPa). It also exhibits remarkable mechanical stability during compression and bending. The sensor is successfully employed in a robotic gripping task to recognize the targets of different materials and shapes based on a multilayer perception (MLP) neural network. It opens the door to realizing haptic sensing capabilities for robotic hands and prosthetic limbs.

Automatic Stub Avoidance for a Powered Prosthetic Leg Over Stairs and Obstacles.

Passive prosthetic legs require undesirable compensations from amputee users to avoid stubbing obstacles and stairsteps. Powered prostheses can reduce those compensations by restoring normative joint biomechanics, but the absence of user proprioception and volitional control combined with the absence of environmental awareness by the prosthesis increases the risk of collisions. This article presents a novel stub avoidance controller that automatically adjusts prosthetic knee/ankle kinematics based on suprasensory measurements of environmental distance from a small, lightweight, low-power, low-cost ultrasonic sensor mounted above the prosthetic ankle. In a case study with two transfemoral amputee participants, this control method reduced the stub rate during stair ascent by 89.95% and demonstrated an 87.50% avoidance rate for crossing different obstacles on level ground. No thigh kinematic compensation was required to achieve these results. These findings demonstrate a practical perception solution for powered prostheses to avoid collisions with stairs and obstacles while restoring normative biomechanics during daily activities.

Investigation of Brain Activity While Listening to Music by Using Brain Control Interface (BCI)

There are five main types of brain waves: alpha, beta, delta, gamma, and theta, which are associated with different states of the mind1. Previous research has shown that music alters the ratio of brain waves in the brain and has significant effects on the brain and body in a clinical setting2. Bigliassi et al. (2015) study showed that calm music can lower vagal withdrawal through increased activation of the prefrontal cortex. Additionally, Nawaz et al. (2018) explained that stimulating and calm music increases the beta and alpha waves in the frontal and parietal regions of the brain, respectively. Although the effects of music on the brain are well studied, the quantification of these effects is not well documented in the current literature. Therefore, our study focuses on the quantification of these effects. We have used BCI technology, which creates a communication pathway between neural activity and external devices, such as drones or prosthetic arms, via neural signals. BCI technology requires recording of brain activity, which can be done invasively or non-invasively with electrical conductors5. The neural activity required for BCI is measured through electroencephalograms (EEGs), which are thought to be generated by cortical pyramidal neurons6. For our research, we used our BCI technology to accumulate and quantify EEG data to address the effects of music on brain waves.

Prosthetic Limb Attachment via Electromagnetic Attraction Through a Closed Skin Envelope.

Current socket-based methods of prosthetic limb attachment are responsible for many of the dominant problems reported by persons with amputation. In this work, we introduce a new paradigm for attachment via electromagnetic attraction between a bone-anchored ferromagnetic implant and an external electromagnet. Our objective was to develop a design framework for electromagnetic attachment, and to evaluate this framework in the context of transfemoral amputation. We first used inverse dynamics to calculate the forces required to suspend a knee-ankle-foot prosthesis during gait. We then conducted cadaveric dissections to inform implant geometry and design a surgical methodology for covering the implant. We also developed an in silico framework to investigate how electromagnet design affects system performance. Simulations were validated against benchtop testing of a custom-built electromagnet. The physical electromagnet matched simulations, with a root-mean-square percentage error of 4.2% between measured and predicted forces. Using this electromagnet, we estimate that suspension of a prosthesis during gait would require 33 W of average power. After 200 and 1000 steps of simulated walking, the temperature at the skin would increase 2.3 °C and 15.4 °C relative to ambient, respectively. Our design framework produced an implant and electromagnet that could feasibly suspend a knee-ankle-foot prosthesis during short walking bouts. Future work will focus on optimization of this system to reduce heating during longer bouts. This work demonstrates the initial feasibility of an electromagnetic prosthetic attachment paradigm that has the potential to increase comfort and improve residual limb health for persons with amputation.

Towards a Unified Approach for Continuously-Variable Impedance Control of Powered Prosthetic Legs over Walking Speeds and Inclines.

Research in powered prosthesis control has explored the use of impedance-based control algorithms due to their biomimetic capabilities and intuitive structure. Modern impedance controllers feature parameters that smoothly vary over gait phase and task according to a data-driven model. However, these recent efforts only use continuous impedance control during stance and instead utilize discrete transition logic to switch to kinematic control during swing, necessitating two separate models for the different parts of the stride. In contrast, this paper presents a controller that uses smooth impedance parameter trajectories throughout the gait, unifying the stance and swing periods under a single, continuous model. Furthermore, this paper proposes a basis model to represent intertask relationships in the impedance parameters-a strategy that has previously been shown to improve model accuracy over classic linear interpolation methods. In the proposed controller, a weighted sum of Fourier series is used to model the impedance parameters of each joint as continuous functions of gait cycle progression and task. Fourier series coefficients are determined via convex optimization such that the controller best reproduces the joint torques and kinematics in a reference able-bodied dataset. Experiments with a powered knee-ankle prosthesis show that this simpler, unified model produces competitive results when compared to a more complex hybrid impedance-kinematic model over varying walking speeds and inclines.

3D Printing for the Development of Palatal Defect Prosthetics.

Three-dimensional (3D) printing has emerged as a promising new technology for the development of surgical prosthetics. Research in orthopedic surgery has demonstrated that using 3D printed customized prosthetics results in more precise implant placements and better patient outcomes. However, there has been little research on implementing customized 3D printed prosthetics in otolaryngology. The program sought to determine whether computed tomography (CT) serves as feasible templates to construct 3D printed palatal obturator prosthetics for defects in patients who have been treated for head and neck cancers. A retrospective review of patients with palatal defects was conducted and identified 1 patient with high quality CTs compatible with 3D modeling. CTs of the patient's craniofacial anatomy were used to develop a 3D model and a Formlabs 3B+ printer printed the palatal prosthetic. We successfully developed and produced an individualized prosthetic using CTs from a veteran with head and neck deformities caused by cancer treatment who was previously treated at the Veterans Affairs Palo Alto Health Care System. This project was successful in printing patient-specific implants using CT reproductions of the patient's craniofacial anatomy, particularly of the palate. The program was a proof of concept and the implant we created was not used on the patient. Customized 3D printed implants may allow otolaryngologists to enhance the performance and efficiency of surgeries and better rehabilitate and reconstruct craniofacial deformities to restore appearance and function to patients. Additional research will strive to enhance the therapeutic potential of these prosthetics to serve as low-cost, patient-specific implants.

Recent Advances in Biomimetics for the Development of Bio-Inspired Prosthetic Limbs.

Recent advancements in biomimetics have spurred significant innovations in prosthetic limb development by leveraging the intricate designs and mechanisms found in nature. Biomimetics, also known as "nature-inspired engineering", involves studying and emulating biological systems to address complex human challenges. This comprehensive review provides insights into the latest trends in biomimetic prosthetics, focusing on leveraging knowledge from natural biomechanics, sensory feedback mechanisms, and control systems to closely mimic biological appendages. Highlighted breakthroughs include the integration of cutting-edge materials and manufacturing techniques such as 3D printing, facilitating seamless anatomical integration of prosthetic limbs. Additionally, the incorporation of neural interfaces and sensory feedback systems enhances control and movement, while technologies like 3D scanning enable personalized customization, optimizing comfort and functionality for individual users. Ongoing research efforts in biomimetics hold promise for further advancements, offering enhanced mobility and integration for individuals with limb loss or impairment. This review illuminates the dynamic landscape of biomimetic prosthetic technology, emphasizing its transformative potential in rehabilitation and assistive technologies. It envisions a future where prosthetic solutions seamlessly integrate with the human body, augmenting both mobility and quality of life.

OBT-Trace: An approach to trace and recognition object motion through prosthetic hand gestures

The development of prosthetic hands has advanced significantly in recent years, aiming to provide more intuitive and functional solutions for individuals with upper limb amputations. This research presents a novel approach to prosthetic hand control by integrating 3D hand gesture recognition with object manipulation and recognition capabilities. Our proposed system utilizes a pre-trained object recognition model, based on transfer learning, to enable the prosthetic hand to perceive and identify objects in its vicinity. The model leverages a vast dataset of objects, enabling the prosthetic hand to recognize a wide array of everyday items, thus enhancing its versatility.In addition, the prosthetic hand incorporates a sophisticated 3D hand gesture recognition system, allowing users to control the hand's movements and actions seamlessly. By recognizing specific gestures, such as grasping, lifting, and releasing, users can intuitively interact with their environment and perform various tasks with ease. This research leverages the synergy between gesture recognition and object recognition, creating a powerful framework for prosthetic hand control. The system's adaptability and versatility make it suitable for a broad range of applications, from assisting with daily tasks to enhancing the quality of life for individuals with upper limb amputations.The results of this study demonstrate the feasibility and effectiveness of combining 3D hand gesture recognition with pre-trained object recognition through transfer learning. This approach opens up new possibilities for enhancing prosthetic hand functionality and usability, ultimately improving the lives of those who rely on these devices for daily living. The proposed model combines the features of YOLO V7 object detection with pre-trained models. The proposed model achieves 99.8% of accuracy compared to the existing models.

Mapping Method of Human Arm Motion Based on Surface Electromyography Signals.

This paper investigates a method for precise mapping of human arm movements using sEMG signals. A multi-channel approach captures the sEMG signals, which, combined with the accurately calculated joint angles from an Inertial Measurement Unit, allows for action recognition and mapping through deep learning algorithms. Firstly, signal acquisition and processing were carried out, which involved acquiring data from various movements (hand gestures, single-degree-of-freedom joint movements, and continuous joint actions) and sensor placement. Then, interference signals were filtered out through filters, and the signals were preprocessed using normalization and moving averages to obtain sEMG signals with obvious features. Additionally, this paper constructs a hybrid network model, combining Convolutional Neural Networks and Artificial Neural Networks, and employs a multi-feature fusion algorithm to enhance the accuracy of gesture recognition. Furthermore, a nonlinear fitting between sEMG signals and joint angles was established based on a backpropagation neural network, incorporating momentum term and adaptive learning rate adjustments. Finally, based on the gesture recognition and joint angle prediction model, prosthetic arm control experiments were conducted, achieving highly accurate arm movement prediction and execution. This paper not only validates the potential application of sEMG signals in the precise control of robotic arms but also lays a solid foundation for the development of more intuitive and responsive prostheses and assistive devices.

Application of ultrasound to monitor in vivo residual bone movement within transtibial prosthetic sockets

Transtibial prosthetic users do often struggle to achieve an optimal prosthetic fit, leading to residual limb pain and stump-socket instability. Prosthetists face challenges in objectively assessing the impact of prosthetic adjustments on residual limb loading. Understanding the mechanical behaviour of the pseudo-joint formed by the residual bone and prosthesis may facilitate prosthetic adjustments and achieving optimal fit. This study aimed to assess the feasibility of using B-mode ultrasound to monitor in vivo residual bone movement within a transtibial prosthetic socket during different stepping tasks. Five transtibial prosthesis users participated, and ultrasound images were captured using a Samsung HM70A system during five dynamic conditions. Bone movement relative to the socket was quantified by tracking the bone contour using Adobe After-Effect. During the study a methodological adjustment was made to improve data quality, and the first two participants were excluded from analysis. The remaining three participants exhibited consistent range of motion, with a signal to noise ratio ranging from 1.12 to 2.59. Medial–lateral and anterior–posterior absolute range of motion varied between 0.03 to 0.88 cm and 0.14 to 0.87 cm, respectively. This study demonstrated that it is feasible to use B-mode ultrasound to monitor in vivo residual bone movement inside an intact prosthetic socket during stepping tasks.