Search Results (258)

Four configuration laminates made of flax, glass, and basalt were fabricated via vacuum-assisted hand lay-up with added weight and tested under ASTM D3039 and D790. The flax–glass–flax lay-up exhibited the highest tensile strength and flexural strength. Orthotropic elastic properties were determined from remanufactured 90°-rotated specimens. A hexahedral-meshed finite element model using these inputs under a 5256 N load predicted the stress and strain within 1% and 5% of the experimental values. These findings demonstrate that flax–glass hybrids offer mechanical reliability, sustainability, and affordability for next-generation prosthetic applications. Full article

Upper-limb motor disabilities and amputation pose a significant burden on individuals, hindering their ability to perform daily activities independently. While various research studies aim to enhance the performance of current upper-limb prosthetic devices, electrically activated prostheses still face challenges in achieving optimal functionality. This paper explores the potential of utilizing electromyogram (EMG) and electroencephalogram (EEG) signals to not only decipher movement across multiple degrees of freedom (DOFs) but also offer a more intuitive means of control. In this study, six distinct control schemes for upper-limb prosthetic devices are proposed, each with different combinations of EEG and EMG signals. These schemes were designed to control multiple degrees-of-freedom movements, encompassing five different hand and forearm actions (hand-open, hand-close, wrist pronation, wrist supination, and rest-state). Using Linear Discriminant Analysis as a model results in classification accuracies of over 85% for combined EEG-EMG control schemes. The results suggest promising advancements in the field and show the potential for a more effective and user-friendly control interface for upper-limb prosthetic devices. Full article

This study presents the design, fabrication, and clinical validation of a lightweight, body-powered prosthetic index finger actuated via metacarpophalangeal (MCP) joint motion. The proposed system incorporates an underactuated, cable-driven mechanism combining rigid and compliant elements to achieve passive adaptability and embodied intelligence, supporting intuitive user interaction. Results indicate that the prosthesis successfully mimics natural finger flexion and adapts effectively to a variety of grasping tasks with minimal effort. This study was conducted in accordance with ethical standards and approved by the Institutional Review Board (IRB), Project No. 670161, titled “Biologically-Inspired Synthetic Finger: Design, Fabrication, and Application.” The findings suggest that the device offers a viable and practical solution for individuals with partial hand loss, particularly in settings where electrically powered systems are unsuitable or inaccessible. Full article

The research presented in this paper focuses on the utilization of 3D printing technology in the design and manufacture of a prosthetic hand, equipped with a digit replicator. The subject of this study was a young man who had undergone the amputation of two fingers on his right hand. The electronic control of the movement of the finger copy was developed using Arduino language. A concept and outline drawings were developed in ProCreate. Three-dimensional scan of the hand and forearm was made using an EinScan PRO HD SHINING 3D scanner. Using CAD software—Autodesk Inventor and Autodesk Meshmixer, the prosthesis was designed. Printing was carried out on a 3D printer of the i3 MK3 and MK3+ series using a PLA (polylactic acid) filament. It was determined that PLA is an optimal material for printing, as it is considered to be safe for future patients’ skin. Work on the electronic circuitry started in Autodesk TinkerCad simulation software, allowing the code to be verified and ensuring the safety of the control system. The prosthesis’s design demonstrates the potential to reach as many people in need as possible by using readily available, low-cost, and easy-to-use components. Full article

Background/Objectives: the precise application of torque during prosthetic screw tightening is essential to the long-term success and mechanical stability of implant-supported restorations. This study aimed to evaluate the influence of practitioner experience, glove material, screwdriver length, and hand moisture on the maximum torque value (MTV) generated during manual tightening. Methods: thirty participants, comprising 10 experienced professors and 20 senior dental students, performed tightening tasks under six hand conditions (nitrile gloves, latex gloves, and bare hands, each in dry and wet environments) using two screwdriver lengths (21 mm and 27 mm). The torque values were measured using a calibrated digital torque meter, and the results were analyzed using a linear mixed model. Results: professors applied significantly higher torque than students (16.92 Ncm vs. 15.03 Ncm; p = 0.008). Nitrile gloves yielded the highest torque (17.11 Ncm), surpassing bare hands significantly (p = 0.003). No statistically significant differences were found for screwdriver length (p = 0.12) or hand moisture (p = 0.11). Conclusions: these findings underscore the importance of clinical proficiency and glove material in torque delivery, providing evidence-based insights to enhance procedural reliability and training standards in implant prosthodontics. Full article

Electromyography (EMG) signals have diverse applications, ranging from prosthetic hands and assistive suits to rehabilitation devices. Nonetheless, their performance suffers from cross-subject generalization issues, electrode shifts, and daily variability. In a previous study, while transfer learning narrowed the classification performance gap to −1% in an eight-class scenario under electrode shift, they imposed the burden of additional data collection and re-training. To address this issue in real-time prediction, we investigated a sliding-window normalization (SWN) technique that merges z-score normalization with sliding-window processing to align the EMG amplitude across channels and mitigate the performance degradation caused by electrode displacement. We validated SWN using experimental data from a right-arm trajectory-tracking task involving three motion classes (rest, flexion, and extension of the elbow). Offline analysis revealed that SWN mitigated accuracy degradation to −1.0% without additional data for re-training or multi-condition training, a 6.6% improvement compared with the −7.6% baseline without normalization. The advantage of SWN is that it operates with data from a single electrode position for training, which eliminates both the collection of multi-position training data and the calibration of deep learning models before practical use in EMG applications. Moreover, combining SWN with multi-position training exceeded the classification accuracy of the no-shift condition by 2.4%. Full article

Assistive technologies, particularly multi-fingered robotic hands (MFRHs), are critical for enhancing the quality of life for individuals with upper-limb disabilities. However, achieving precise and stable control of such systems remains a significant challenge. This study proposes an Improved Grey Wolf Optimization (IGWO)-tuned Linear Quadratic Regulator (LQR) to enhance the control performance of an MFRH. The MFRH was modeled using Denavit–Hartenberg kinematics and Euler–Lagrange dynamics, with micro-DC motors selected based on computed torque requirements. The LQR controller, optimized via IGWO to systematically determine weighting matrices, was benchmarked against PID and PID-PSO controllers under diverse input scenarios. For step input, the IGWO-LQR achieved a settling time of 0.018 s with zero overshoot for Joint 1, outperforming PID (settling time: 0.0721 s; overshoot: 6.58%) and PID-PSO (settling time: 0.042 s; overshoot: 2.1%). Similar improvements were observed across all joints, with Joint 3 recording an IAE of 0.001334 for IGWO-LQR versus 0.004695 for PID. Evaluations under square-wave, sine, and sigmoid inputs further validated the controller’s robustness, with IGWO-LQR consistently delivering minimal tracking errors and rapid stabilization. These results demonstrate that the IGWO-LQR framework significantly enhances precision and dynamic response. Full article

Objective: The aim of this study was to assess the effects of vestibular stimulation (semicircular canals/utricles) compared to cochlear stimulation on phantom pain and depersonalization/derealization symptoms after ≥3 months since traumatic amputation of hand-finger(s). Methods: A total of 125 adults (38.2 ± 8.1 years old) with phantom pain after amputation of one to four fingers agreed to participate. None of them wore prosthetic devices or had history of otology/audiology/vestibular/neurology/rheumatology/orthopedic/psychiatry disorders or psychopharmacological treatment. After a preliminary assessment, in a random order, they were exposed to caloric stimulation (right/left 44 °C/30 °C), centrifuge (right/left), and transient evoked otoacoustic emissions (TOAEs, right/left) with a follow-up of three days in between. Immediately before and after each stimulus, they reported on their pain characteristics and depersonalization/derealization symptoms. Results: After vestibular stimulation, a decrease in pain intensity was reported by at least one-third of the participants, which persisted for at least one day in the majority of them. Less than one-sixth of the participants reported pain decrease after cochlear stimulation. No influence was observed based on the side of the stimulation or the temperature, but the stimuli sequence had an effect. The centrifuge and TOAE effects were related to anxiety/depression symptoms and mainly observed when they were the first stimulus used. After caloric stimulation, pain decrease was independent from the sequence of the stimuli, and it was related to reports of feeling estrangement from the body. Conclusions: Mild caloric vestibular stimulation, whether applied to the right or left side and using warm or cold temperature, can modulate phantom pain after amputation of hand-finger(s) in patients with altered bodily sensations. However, individual cofactors may influence one’s susceptibility to experiencing this effect. Full article

Sonomyography (SMG) is a method of controlling upper-limb prostheses through an innovative human–machine interface by monitoring forearm muscle activity through ultrasonic imaging. Over the past two decades, SMG has shown promise, achieving over 90% accuracy in classifying hand gestures when combined with artificial intelligence, making it a viable alternative to electromyography (EMG). However, up to now, there are few reports of a system integrating SMG together with a prosthesis for testing on amputee subjects to demonstrate its capability in relation to daily activities. In this study, we developed a highly efficient human–machine interface algorithm for controlling a prosthetic hand with 6-DOF using a wireless and wearable ultrasound imaging probe. We first evaluated the accuracy of our model in classifying nine different hand gestures to determine its reliability and precision. The results from the offline study, which included ten healthy participants, indicated that nine different hand gestures could be classified with a success rate of 100%. Additionally, the developed controlling system was tested in real-time experiments on two amputees, using a variety of hand function test kits. The results from the hand function tests confirmed that the prosthesis, controlled by the SMG system, could assist amputees in performing a variety of hand movements needed in daily activities. Full article

Hands are central to nearly every aspect of daily life, so losing an upper limb due to amputation can severely affect a person’s independence. Robotic prostheses offer a promising solution by mimicking many of the functions of a natural arm, leading to an increasing need for advanced prosthetic designs. However, developing an effective robotic hand prosthesis is far from straightforward. It involves several critical steps, including creating accurate models, choosing materials that balance biocompatibility with durability, integrating electronic and sensory components, and perfecting control systems before final production. A key factor in ensuring smooth, natural movements lies in the method of control. One popular approach is to use electromyography (EMG), which relies on electrical signals from the user’s remaining muscle activity to direct the prosthesis. By decoding these signals, we can predict the intended hand and arm motions and translate them into real-time actions. Recent strides in machine learning have made EMG-based control more adaptable, offering users a more intuitive experience. Alongside this, researchers are exploring tactile sensors for enhanced feedback, materials resilient in harsh conditions, and mechanical designs that better replicate the intricacies of a biological limb. This review brings together these advancements, focusing on emerging trends and future directions in robotic upper-limb prosthesis development. 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

Integrating bioinspired design and additive manufacturing into engineering education fosters innovation to meet the growing demand for accessible, personalized assistive technologies. This paper presents the outcomes of an international course, “3D Prosthetics and Orthotics”, offered to undergraduate students in the Biomimetic program at Westfälische Hochschule (Germany), in collaboration with the 3D Orthotics and Prosthetics Laboratory at the Federal University of São Paulo—UNIFESP (Brazil). The course combined theoretical and hands-on modules covering digital modeling (CAD), simulation (CAE), and fabrication (CAM), enabling students to develop bioinspired assistive devices through a Project-based learning approach. Working in interdisciplinary teams, students addressed real-world rehabilitation challenges by translating biological mechanisms into engineered solutions using additive manufacturing. Resulting prototypes included a hand prosthesis based on the Fin Ray effect, a modular finger prosthesis inspired by tendon–muscle antagonism, and a cervical orthosis designed based on stingray morphology. Each device was digitally modeled, mechanically analyzed, and physically fabricated using open-source and low-cost methods. This initiative illustrates how biomimetic mechanisms and design can be integrated into education to generate functional outcomes and socially impactful health technologies. Grounded in the Mao3D open-source methodology, this experience demonstrates the value of combining nature-inspired principles, digital fabrication, Design Thinking, and international collaboration to advance inclusive, low-cost innovations in assistive technology. Full article

Background: Trapeziometacarpal (TM) joint arthritis is a common condition causing pain and functional limitations, particularly in activities requiring pinch and grip strength. Surgical options such as interposition trapeziectomy and prosthetic joint replacement have demonstrated varying degrees of success. However, the role of post-operative rehabilitation in optimizing outcomes for these procedures remains underexplored. Effective rehabilitation may be critical for restoring strength, range of motion (ROM), and overall hand function; yet, no consensus exists on the best approach for either surgical modality. This study aims to evaluate the impact of post-operative rehabilitation on functional and clinical outcomes in patients undergoing interposition trapeziectomy versus prosthetic replacement for TM joint arthritis. Methods: A retrospective cohort study was conducted on patients treated for TM joint arthritis between November 2023 and October 2024. Patients were divided into two groups based on the surgical procedure: interposition trapeziectomy and prosthetic replacement. Patients randomly followed post-operative rehabilitation protocols, auto-assisted exercises, or no type of rehabilitation. The outcomes assessed included pain (VAS), return to work or heavy activities, post-operative complications, hand function (DASH score), and patient satisfaction at 1 and 6 months after surgery. Results: The prosthesis group consisted of 30 patients, while 31 patients underwent interposition trapeziectomy. Patients in both groups showed good improvements in pain, ROM, and hand function post rehabilitation. The prosthetic group demonstrated a faster recovery of strength and higher early satisfaction scores, but in the long term, the results were overlapping. No significant differences were observed in long-term functional outcomes or patient satisfaction at 6 months. In the trapeziectomy group, for patients who followed a rehabilitation program, no significant differences were found. Conclusions: Post-operative rehabilitation finds its place in recovery after both interposition trapeziectomy and prosthetic replacement for TM joint arthritis. While prosthetic replacement allows for quicker functional recovery, interposition trapeziectomy offers comparable long-term results with a lower complication profile. Tailored rehabilitation protocols may enhance outcomes and should be considered an integral part of TM joint arthritis management in selected patients. Full article

A multitude of factors, including accidents, chronic illnesses, and conflicts, contribute to rising global amputation rates. The World Health Organization (WHO) estimates that 57.7 million people lived with traumatic limb amputations in 2017, with many lacking access to affordable prostheses. This study presents a preliminary framework for a low-cost, electromyography (EMG)-controlled upper limb prosthesis, integrating 3D printing and EMG sensors to enhance accessibility and functionality. Surface electrodes capture bioelectric signals from muscle contractions, processed via an Arduino Uno to actuate a one-degree-of-freedom (1-DoF) prosthetic hand. Preliminary results demonstrate reliable detection of muscle contractions (threshold = 7 ADC units, ~34 mV) and motor actuation with a response time of ~150 ms, offering a cost-effective alternative to commercial systems. While limited to basic movements, this design lays the groundwork for scalable, user-centered prosthetics. Future work will incorporate multi-DoF control, AI-driven signal processing, and wireless connectivity to improve precision and usability, advancing rehabilitation technology for amputees in resource-limited settings. Full article

This paper proposes a grasping method for prosthetic hands based on object geometry and symmetry axis. The method utilizes computer vision to extract the geometric shape, spatial position, and symmetry axis of target objects and selects appropriate grasping modes and postures through the extracted features. First, grasping patterns are classified based on the analysis of hand-grasping movements. A mapping relationship between object geometry and grasp patterns is established. Then, target object images are captured using binocular depth cameras, and the YOLO algorithm is employed for object detection. The SIFT algorithm is applied to extract the object’s symmetry axis, thereby determining the optimal grasp point and initial hand posture. An experimental platform is built based on a seven-degree-of-freedom (7-DoF) robotic arm and a multi-mode prosthetic hand to conduct grasping experiments on objects with different characteristics. Experimental results demonstrate that the proposed method achieves high accuracy and real-time performance in recognizing object geometric features. The system can automatically match appropriate grasp modes according to object features, improving grasp stability and success rate. Full article