Search Results (200)

Approximately 185,000 people in the United states experience limb loss each year. There is a need for an intuitive neural interface that can offer high-fidelity control signals to optimize the advanced functionality of prosthetic devices. Regenerative peripheral nerve interface (RPNI) is a pioneering advancement in neuroengineering that combines surgical techniques with biocompatible materials to create an interface for individuals with limb loss. RPNIs are surgically constructed from autologous muscle grafts that are neurotized by the residual peripheral nerves of an individual with limb loss. RPNIs amplify neural signals and demonstrate long term stability. In this narrative review, the terms “Regenerative Peripheral Nerve Interface (RPNI)” and “RPNI surgery” are used interchangeably to refer to the same surgical and biological construct. This narrative review specifically focuses on RPNIs as a targeted approach to enhance prosthetic control through surgically created nerve–muscle interfaces. This area of research offers a promising solution to overcome the limitations of existing prosthetic control systems and could help improve the quality of life for people suffering from limb loss. It allows for multi-channel control and bidirectional communication, while enhancing the functionality of prosthetics through improved sensory feedback. RPNI surgery holds significant promise for improving the quality of life for individuals with limb loss by providing a more intuitive and responsive prosthetic experience. Full article

Upper extremity amputations significantly impact an individual’s physical capabilities, psychosocial well-being, and overall quality of life. The level at which an amputation is performed influences residual limb function, prosthetic compatibility, and long-term patient satisfaction. While surgical guidelines traditionally emphasize maximal limb preservation, prosthetists often advocate for amputation sites that optimize prosthetic fit and function, highlighting the need for a collaborative approach. This review examines the discrepancies between surgical and prosthetic recommendations for optimal amputation levels, from digit amputations to shoulder disarticulations, and explores their implications for prosthetic design, functionality, and patient outcomes. Various prosthetic options, including passive functional, body-powered, myoelectric, and hybrid devices, offer distinct advantages and limitations based on the level of amputation. Prosthetists emphasize the importance of residual limb length, not only for mechanical efficiency but also for achieving symmetry with the contralateral limb, minimizing discomfort, and enhancing control. Additionally, emerging technologies such as targeted muscle reinnervation (TMR) and advanced myoelectric prostheses are reshaping rehabilitation strategies, further underscoring the need for precise amputation planning. By integrating insights from both surgical and prosthetic perspectives, this review highlights the necessity of a multidisciplinary approach involving surgeons, prosthetists, rehabilitation specialists, and patients in the decision-making process. A greater emphasis on preoperative planning and interprofessional collaboration can improve prosthetic outcomes, reduce device rejection rates, and ultimately enhance the functional independence and well-being of individuals with upper extremity amputations. Full article

Although artificial intelligence technologies have significantly enhanced autonomous robots’ capabilities in perception, decision-making, and planning, their autonomy may still fail when faced with complex, dynamic, or unpredictable environments. Therefore, it is critical to enable users to take over robot control in real-time and efficiently through teleoperation. The lightweight, wearable myoelectric armband, due to its portability and environmental robustness, provides a natural human–robot gesture interaction interface. However, current myoelectric teleoperation gesture control faces two major challenges: (1) poor intuitiveness due to visual-motor misalignment; and (2) low efficiency from discrete, single-degree-of-freedom control modes. To address these challenges, this study proposes an integrated myoelectric teleoperation interface. The interface integrates the following: (1) a novel hybrid reference frame aimed at effectively mitigating visual-motor misalignment; and (2) a finite state machine (FSM)-based control logic designed to enhance control efficiency and smoothness. Four experimental tasks were designed using different end-effectors (gripper/dexterous hand) and camera viewpoints (front/side view). Compared to benchmark methods, the proposed interface demonstrates significant advantages in task completion time, movement path efficiency, and subjective workload. This work demonstrates the potential of the proposed interface to significantly advance the practical application of wearable myoelectric sensors in human–robot interaction. Full article

Gesture recognition based on conventional machine learning is the main control approach for advanced prosthetic hand systems. Its primary limitation is the need for feature extraction, which must meet real-time control requirements. On the other hand, deep learning models could potentially overfit when trained on small datasets. For these reasons, we propose a hybrid Linear Discriminant Analysis–convolutional neural network (LDA-CNN) framework to improve the gesture recognition performance of sEMG-based prosthetic hand control systems. Within this framework, 1D-CNN filters are trained to generate latent representation that closely approximates Fisher’s (LDA’s) discriminant subspace, constructed from handcrafted features. Under the train-one-test-all evaluation scheme, our proposed hybrid framework consistently outperformed the 1D-CNN trained with cross-entropy loss only, showing improvements from 4% to 11% across two public datasets featuring hand gestures recorded under various limb positions and arm muscle contraction levels. Furthermore, our framework exhibited advantages in terms of induced spectral regularization, which led to a state-of-the-art recognition error of 22.79% with the extended 23 feature set when tested on the multi-limb position dataset. The main novelty of our hybrid framework is that it decouples feature extraction in regard to the inference time, enabling the future incorporation of a more extensive set of features, while keeping the inference computation time minimal. Full article

Upper limb assistive exoskeletons help stroke patients by assisting arm movement in impaired individuals. However, effective control of these systems to help stroke survivors is a complex task. In this paper, a novel approach is proposed to enhance the control of upper limb assistive exoskeletons by using torque estimation and prediction in a proportional–integral–derivative (PID) controller loop to more optimally integrate the torque of the exoskeleton robot, which aims to eliminate system uncertainties. First, a model for torque estimation from Electromyography (EMG) signals and a predictive torque model for the upper limb exoskeleton robot for the elbow are trained. The trained data consisted of two-dimensional high-density surface EMG (HD-sEMG) signals to record myoelectric activity from five upper limb muscles (biceps brachii, triceps brachii, anconeus, brachioradialis, and pronator teres) during voluntary isometric contractions for twelve healthy subjects performing four different isometric tasks (supination/pronation and elbow flexion/extension) for one minute each, which were trained on long short-term memory (LSTM), bidirectional LSTM (BLSTM), and gated recurrent units (GRU) deep neural network models. These models estimate and predict torque requirements. Finally, the estimated and predicted torque from the trained network is used online as input to a PID control loop and robot dynamic, which aims to control the robot optimally. The results showed that using the proposed method creates a strong and innovative approach to greater independence and rehabilitation improvement. Full article

This paper presents a wearable, open-source system that combines electromyography (EMG) and functional electrical stimulation (FES) to restore hand function in individuals with disabilities caused by cervical spinal cord injuries or stroke. The device captures electrical signals produced during volitional muscle contractions and analyzes them to interpret the user’s intent to move. This information is then used to stimulate impaired muscles, promoting improved hand function and rehabilitation. We detail the design, prototyping, and testing of the system, emphasizing its modularity, affordability, and accessibility. Hardware and software, along with 3D-printable components, are shared via GitHub to enable replication and customization by professionals and makers. The system serves as both an orthotic device for enhancing grasping ability and a therapeutic tool for rehabilitating hemiparetic hands, with potential for broader applications. By addressing cost, customization, and accessibility barriers, this initiative promotes collaboration and further innovation in rehabilitation technologies, advancing the development of affordable, user-centered solutions for individuals with disabilities. Full article

Background: Lateral ankle sprains can result in adverse outcomes, including reinjuries or chronic ankle instability. The peroneal musculature plays a key role in stabilizing the ankle and preventing sudden ankle inversions that may lead to sprains. Objective: The purpose of the study is to investigate (1) inter-limb differences in peroneal myoelectrical activity in athletes with a history of ankle sprain during the past six months and (2) to investigate peroneal myoelectrical activity differences between athletes with and without a history of ankle sprain. Methods: Sixty-seven athletes (53% females, 46.3% males) were included in this observational cross-sectional study. Self-reported data regarding history of ankle sprain were collected. The peroneal myoelectrical activity was obtained during (1) isometric ankle eversion, (2) dynamic ankle eversions, (3) single leg squat, (4) unilateral and (5) bilateral drop jump test, (6) sprint, and (7) change of direction. Results: No significant differences in peroneal myoelectrical activity were observed between individuals with (n = 46) and without (n = 21) a history of ankle sprain in the past six months (p > 0.05). Additionally, no significant inter-limb differences were found within the previous ankle sprain group (p > 0.05). Conclusions: This study found no significant inter-limb differences in peroneal muscle activity among athletes with a history of ankle sprain during the past six months. Moreover, no differences were observed between athletes with and without a history of ankle sprain. This study has certain limitations, including the lack of data regarding the timing and severity of the ankle sprain, as well as the duration and specific characteristics of the rehabilitation process. Full article

In equine surface electromyography (sEMG), challenges related to the reliability and interpretability of data arise, among other factors, from methodological differences, including signal processing and analysis. The aim of this study is to demonstrate the filtering–induced changes in basic signal features in relation to the balance between signal loss and noise attenuation. Raw sEMG signals were collected from the quadriceps muscle of six horses during walk, trot, and canter and then filtered using eight filtering methods with varying cut–off frequencies (low–pass at 10 Hz, high–pass at 20 Hz and 40 Hz, and bandpass at 20–450 Hz, 40–450 Hz, 7–200 Hz, 15–500 Hz, and 30–500 Hz). For each signal variation, signal features—such as amplitude, root mean square (RMS), integrated electromyography (iEMG), median frequency (MF), and signal–to–noise ratio (SNR)—along with signal loss metrics and power spectral density (PSD), were calculated. High–pass filtering at 40 Hz and bandpass filtering at 40–450 Hz introduced significant filtering–induced changes in signal features while providing full attenuation of low–frequency noise contamination, with no observed differences in signal loss between these two methods. Other filtering methods led to only partial attenuation of low–frequency noise, resulting in lower signal loss and less consistent changes across gaits in signal features. Therefore, filtering–induced changes should be carefully considered when comparing signal features from studies using different filtering approaches. These findings may support cross-referencing in equine sEMG research related to training, rehabilitation programs, and the diagnosis of musculoskeletal diseases, and emphasize the importance of applying standardized filtering methods, particularly with a high–pass cut–off frequency set at 40 Hz. Full article

The use of surface electromyography (sEMG) in equine locomotion research has increased significantly due to the essential role of balanced, symmetrical, and efficient movement in riding. However, variations in sEMG signal processing for forelimb extensor muscles across studies have made cross-study comparisons challenging. This study aims to compare the sEMG signal characteristics from carpal extensor muscles under different filtering methods: raw signal, low-pass filtering (10 Hz cut-off), and bandpass filtering (40–450 Hz cut-off and 7–200 Hz cut-off). sEMG signals were collected from four muscles of three horses during walking and trotting. The raw signals were normalized and filtered separately using a 4th-order Butterworth filter: low-pass 10 Hz, bandpass 40–450 Hz, or bandpass 7–200 Hz. For each filtered signal variant, eight activity bursts were annotated, and amplitude, root mean square (RMS), median frequency (MF), and signal-to-noise ratio (SNR) were extracted. Signal loss and residual signal were calculated to assess noise reduction and data retention. For m. extensor digitorum lateralis and m. extensor carpi ulnaris, bandpass filtering at 40–450 Hz resulted in the lowest signal loss and the highest amplitude, RMS, MF, and SNR after filtering. However, variations were observed for the other two carpal extensors. These findings support the hypotheses that the characteristics of myoelectric activity in equine carpal extensors vary depending on the filtering method applied and differ among individual muscles, thereby guiding future research on sEMG signal processing and, consequently, equine biomechanics. Since both noise and its reduction alter raw sEMG signals, potentially affecting data analysis, this study provides valuable insights for improving the reliability and reproducibility of equine biomechanics research across different sEMG studies. Full article

To improve the adaptability of the hand prosthesis, we propose a new smart control for a physiological finger prosthesis using the advantages of the deep deterministic policy gradient (DDPG) algorithm. A rigid body model was developed to represent the finger as a training environment. The geometric characteristics and physiological physical properties of the finger available in the literature were assumed, but the joint’s stiffness and damping were neglected. The standard DDPG algorithm was modified to train an artificial neural network (ANN) to perform two predetermined trajectories: linear and sinusoidal. The ANN was evaluated through the use of a computational model that simulated the functionality of the finger prosthesis. The model demonstrated the capacity to successfully execute both sinusoidal and linear trajectories, exhibiting a mean error of $3.984\pm 2.899$ mm for the sinusoidal trajectory and $3.220\pm 1.419$ mm for the linear trajectory. Observing the torques, it was found that the ANN used different strategies to control the movement in order to adapt to the different trajectories. Allowing the ANN to use a combination of both trajectories, our model was able to perform trajectories that differed from purely linear and sinusoidal, showing its ability to adapt to the movement of the physiological finger. The results showed that it was possible to develop a controller for multiple trajectories, which is essential to provide more integrated and personalized prostheses. Full article

Background: Upper gastrointestinal (GI) motility disorders, such as gastroparesis and functional dyspepsia (FD), contribute significantly to morbidity, especially in populations at risk for type 2 diabetes. However, the prevalence and clinical manifestations of these disorders in India, and associated gastric dysrhythmias, are not well studied within this population. Methods: This retrospective, cross-sectional study analyzed 3689 patients who underwent electrogastrography with water load satiety test (EGGWLST) testing across multiple motility clinics in India. The prevalence of gastroparesis and FD-like symptoms, symptom severity, and their association with diabetes and other comorbidities were evaluated. Symptom severity was assessed using the Gastroparesis Cardinal Symptom Index (GCSI). EGGWLST findings were documented, including the gastric myoelectric activity threshold (GMAT) scores. Results: The study population had a mean age of 43.18 years. GCSI scores indicated that patients had symptoms that were mild (55%), moderate (33%), and severe (8%). Compared with the non-diabetic population, diabetic subjects had significantly higher rates of early satiety (56% vs. 45%, p < 0.0001), bloating (73% vs. 67%, p = 0.005), and reflux (28% vs. 24%, p = 0.029). WLST data analysis revealed that significantly more diabetic subjects ingested <350 mL (16% vs. 12%, p = 0.000016). EGG analysis revealed gastric dysthymias in one-third (65%) of patients. Significantly more diabetic subjects (22% vs. 18% p = 0.015) had a GMAT score >0.59. Conclusions: Upper GI motility disorders are prevalent in India, particularly among diabetic patients. EGG is a valuable tool for characterizing these disorders, and may help in personalizing therapeutic approaches. Further research is required to optimize treatment strategies. Full article

Soft robotic exoskeletons have emerged as a transformative solution for rehabilitation and assistance, offering greater adaptability and comfort than rigid designs. Myoelectric control, based on electromyography (EMG) signals, plays a key role in enabling intuitive and adaptive interaction between the user and the exoskeleton. This review analyzes recent advancements in myoelectric control strategies, emphasizing their integration into soft robotic exoskeletons. Unlike previous studies, this work highlights the unique challenges posed by the deformability and compliance of soft structures, requiring novel approaches to motion intention estimation and control. Key contributions include critically evaluating machine learning-based motion prediction, model-free adaptive control methods, and real-time validation strategies to enhance rehabilitation outcomes. Additionally, we identify persistent challenges such as EMG signal variability, computational complexity, and the real-time adaptability of control algorithms, which limit clinical implementation. By interpreting recent trends, this review highlights the need for improved EMG acquisition techniques, robust adaptive control frameworks, and enhanced real-time learning to optimize human-exoskeleton interaction. Beyond summarizing the state of the art, this work provides an in-depth discussion of how myoelectric control can advance rehabilitation by ensuring more responsive and personalized exoskeleton assistance. Future research should focus on refining control schemes tailored to soft robotic architectures, ensuring seamless integration into rehabilitation protocols. This review is a foundation for developing intelligent soft exoskeletons that effectively support motor recovery and assistive applications. Full article

This study is part of the research project Dep-Project: Design and Embodiment of Wearable Prostheses, funded by the Foundation for Science and Technology (FCT), whose main objective is the development of wearable myoelectric prostheses for upper limbs, which are economically accessible, socially accepted, and personalizable. In this context, the need arose to create an online platform with an intuitive interface, which would facilitate the access to persons with upper limb amputation to information about prosthetics and allow them to personalize their prosthesis, according to their aesthetic preferences. Thus, this work aims to demonstrate the importance of designing interfaces for greater inclusion, as well as demonstrating and describing the efficiency of the design process adopted with the aim of potentially being adopted in similar cases. The methodology adopted was Design Thinking, an approach centered on user needs. The development of the platform involved the creation of user personas, information architecture, user flows, wireframes, wireflows, and a design system. The interactive prototype underwent usability testing to evaluate the user experience and identify possible areas for improvement. The results, obtained through the System Usability Scale (SUS) post-test questionnaire, revealed a high success rate, which confirmed the efficiency of the designed solution. Full article

There have been significant breakthroughs in developing highly functional myoelectric prostheses, yet individuals who have experienced upper-limb loss consistently report low levels of satisfaction when performing daily tasks while using myoelectric prostheses. This research aims to evaluate the change in the user’s experience after completing a training program in which tasks are designed to facilitate adaptation to the myoelectric arm in performing the activities of daily living. One participant with a left transradial limb difference was recruited for this project. The user’s experience was evaluated by comparing task completion time, trunk and shoulder angles, object control, movement smoothness, vertical ground reaction forces, the center of pressure location, and selected muscle activation of her affected arm between baseline and post-training. The data collection was performed through a motion capture system, a pressure mat, and wireless EMG modules. While indications of potential improvements in balance, muscle efficiency, and functionality were present, the data were inconclusive as to the effectiveness of the training procedure. One outcome measure that showed improvement across most tasks was the task completion time, which, on average, for the targeted Box and Block Test (tBBT) task was reduced by 24.0 s, box lifting by 1.6 s, bottle pouring by 9.0 s, and the pulley task by 8.4 s. This project serves as part of a larger multi-visit study evaluating the effects of home-based functional training on facilitating users’ adaptation to the myoelectric arm. Full article

Background/Objectives: Individuals with transradial limb loss or absence often retain the ability to pro-supinate their forearm, but the traditional design of the prosthesis precludes this motion from being used for direct prosthesis control. Methods: A prosthetic arm was created for a single user that employed a novel split inner socket to allow pro-supination of the residuum to control a powered prosthetic wrist rotator. A total of 14 subjects (13 able-bodied subjects and one prosthesis user) performed the Refined Clothespin Relocation Test. The user performed the test with their own and a novel research prosthesis, which allowed independent hand and wrist function. Movements of limb segments were recorded using a motion capture system and an analysis of limb segment angles and compensatory motion was made. Results: The research prosthesis reduced compensation in the trunk and head and reduced pain in some joints, while the time to complete the test increased. Conclusions: This method has the potential to create additional intuitive control channels for transradial prostheses. Full article