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Humans have a complex way of expressing their intuitive intentions in real gestures. That is why many gesture detection and recognition techniques have been studied and developed. There are many methods of human hand signal reading, such as those using electroencephalography, electrocorticography, and electromyography, as well as methods for gesture recognition. In this paper, we present a method based on real-time surface electroencephalography hand-based gesture recognition using a multilayer neural network. For this purpose, the sEMG signals have been amplified, filtered and sampled; then, the data have been segmented, feature extracted and classified for each gesture. To validate the method, 100 signals for three gestures with 64 samples each signal have been recorded from 2 users with OYMotion sensors and 100 signals for three gestures from 4 users with the MyWare sensors. These signals were used for feature extraction and classification using an artificial neuronal network. The model converges after 10 sessions, achieving 98% accuracy. As a result, an algorithm was developed that aimed to recognize two specific gestures (handling a bottle and pointing with the index finger) in real time with 95% accuracy. [ABSTRACT FROM AUTHOR]

The realization of hand function reengineering using a manipulator is a research hotspot in the field of robotics. In this paper, we propose a multimodal perception and control method for a robotic hand to assist the disabled. The movement of the human hand can be divided into two parts: the coordination of the posture of the fingers, and the coordination of the timing of grasping and releasing objects. Therefore, we first used a pinhole camera to construct a visual device suitable for finger mounting, and preclassified the shape of the object based on YOLOv8; then, a filtering process using multi-frame synthesized point cloud data from miniature 2D Lidar, and DBSCAN algorithm clustering objects and the DTW algorithm, was proposed to further identify the cross-sectional shape and size of the grasped part of the object and realize control of the robot's grasping gesture; finally, a multimodal perception and control method for prosthetic hands was proposed. To control the grasping attitude, a fusion algorithm based on information of upper limb motion state, hand position, and lesser toe haptics was proposed to realize control of the robotic grasping process with a human in the ring. The device designed in this paper does not contact the human skin, does not produce discomfort, and the completion rate of the grasping process experiment reached 91.63%, which indicates that the proposed control method has feasibility and applicability. [ABSTRACT FROM AUTHOR]

This study develops biomimetic strategies for slip prevention in prosthetic hand grasps. The biomimetic system is driven by a novel slip sensor, followed by slip perception and preventive control. Here, we show that biologically inspired sensorimotor pathways can be restored between the prosthetic hand and users. A Ruffini endings-like slip sensor is used to detect shear forces and identify slip events directly. The slip information and grip force are encoded into a bi-state sensory coding that evokes vibration and buzz tactile sensations in subjects with transcutaneous electrical nerve stimulation (TENS). Subjects perceive slip events under various conditions based on the vibration sensation and voluntarily adjust grip force to prevent further slipping. Additionally, short-latency compensation for grip force is also implemented using a neuromorphic reflex pathway. The reflex loop includes a sensory neuron and interneurons to adjust the activations of antagonistic muscles reciprocally. The slip prevention system is tested in five able-bodied subjects and two transradial amputees with and without reflex compensation. A psychophysical test for perception reveals that the slip can be detected effectively, with a success accuracy of 96.57%. A slip protection test indicates that reflex compensation yields faster grasp adjustments than voluntary action, with a median response time of 0.30 (0.08) s, a rise time of 0.26 (0.03) s, an execution time of 0.56 (0.07) s, and a slip distance of 0.39 (0.10) cm. Prosthetic grip force is highly correlated to that of an intact hand, with a correlation coefficient of 96.85% (2.73%). These results demonstrate that it is feasible to reconstruct slip biomimetic sensorimotor pathways that provide grasp stability for prosthetic users. [ABSTRACT FROM AUTHOR]

To address the prevalent issues of high cost and non-anthropomorphic appearance in current prosthetic hand design. A novel approach was proposed and tested based on the anatomical structure of the human hand, kinematic experiments, and simulation of prosthetic hand multi-rigid body models. Human hands were selected as the research subject, with three common hand activities (“grasping, gripping and pinching”) chosen for analysis. Kinematic data on hand joint movements were collected using the Vicon motion capture system, and joint motion angles were calculated and subjected to joint motion correlation analysis. Multi-rigid body models of prosthetic hands were developed based on human hand computed tomography(CT) images. Kinematic simulation of these multi-rigid body models was conducted using experimental kinematic data from human hands. Finally, 3D printed prototypes of the multi-rigid body models were created for grip performance testing. The results demonstrate that these anthropomorphic prosthetic hands are capable of executing the three common hand actions with an average single fingertip force of 1. 73 N and a dual fingertip force of 3. 92 N. The surface friction coefficient of the prosthetic hand is measured at 0. 79, with a gripping force reaching 4. 14 N. The designed anthropomorphic prosthetic hand exhibits both a human-like appearance and basic functionality while maintaining low manufacturing costs, thereby meeting both psychological needs and purchasing requirements for individuals with disabilities. The research results provide a methodological reference as well as practical insights into personalized prosthetic hand design. [ABSTRACT FROM AUTHOR]

Human muscles can generate force and stiffness during contraction. When in contact with objects, human hands can achieve compliant grasping by adjusting the grasping force and the muscle stiffness based on the object's characteristics. To realize humanoid-compliant grasping, most prosthetic hands obtain the stiffness parameter of the compliant controller according to the environmental stiffness, which may be inconsistent with the amputee's intention. To address this issue, this paper proposes a compliant grasp control method for an underactuated prosthetic hand that can directly obtain the control signals for compliant grasping from surface electromyography (sEMG) signals. First, an estimation method of the grasping force is established based on the Huxley muscle model. Then, muscle stiffness is estimated based on the muscle contraction principle. Subsequently, a relationship between the muscle stiffness of the human hand and the stiffness parameters of the prosthetic hand controller is established based on fuzzy logic to realize compliant grasp control for the underactuated prosthetic hand. Experimental results indicate that the prosthetic hand can adjust the desired force and stiffness parameters of the impedance controller based on sEMG, achieving a quick and stable grasp as well as a slow and gentle grasp on different objects. [ABSTRACT FROM AUTHOR]

In the field of biomechanical modeling, the development of a prosthetic hand with dexterity comparable to the human hand is a multidisciplinary challenge involving complex mechatronic systems, intuitive control schemes, and effective body interfaces. Most current commercial prostheses offer limited functionality, typically only one or two degrees of freedom (DoF), resulting in reduced user adoption due to discomfort and lack of functionality. This research aims to design a computationally efficient low-level control algorithm for prosthetic hand fingers to be able to (a) accurately manage finger positions, (b) anticipate future information, and (c) minimize power consumption. The methodology employed is known as model-based predictive control (MBPC) and starts with the application of linear identification techniques to model the system dynamics. Then, the identified model is used to implement a generalized predictive control (GPC) algorithm, which optimizes the control effort and system performance. A test bench is used for experimental validation, and the results demonstrate that the proposed control scheme significantly improves the prosthesis' dexterity and energy efficiency, enhancing its potential for daily use by people with hand loss. [ABSTRACT FROM AUTHOR]

Millions of individuals are living with upper extremity amputations, making them potential beneficiaries of hand and arm prostheses. While myoelectric prostheses have evolved to meet amputees' needs, challenges remain related to their control. This research leverages surface electromyography sensors and machine learning techniques to classify five fundamental hand gestures. By utilizing features extracted from electromyography data, we employed a nonlinear, multiple-kernel learning-based support vector machine classifier for gesture recognition. Our dataset encompassed eight young nondisabled participants. Additionally, our study conducted a comparative analysis of five distinct sensor placement configurations. These configurations capture electromyography data associated with index finger and thumb movements, as well as index finger and ring finger movements. We also compared four different classifiers to determine the most capable one to classify hand gestures. The dual-sensor setup strategically placed to capture thumb and index finger movements was the most effective—this dual-sensor setup achieved 90% accuracy for classifying all five gestures using the support vector machine classifier. Furthermore, the application of multiple-kernel learning within the support vector machine classifier showcases its efficacy, achieving the highest classification accuracy amongst all classifiers. This study showcased the potential of surface electromyography sensors and machine learning in enhancing the control and functionality of myoelectric prostheses for individuals with upper extremity amputations. [ABSTRACT FROM AUTHOR]

People use their hands for intricate tasks like playing musical instruments, employing myriad touch sensations to inform motor control. In contrast, current prosthetic hands lack comprehensive haptic feedback and exhibit rudimentary multitasking functionality. Limited research has explored the potential of upper limb amputees to feel, perceive, and respond to multiple channels of simultaneously activated haptic feedback to concurrently control the individual fingers of dexterous prosthetic hands. This study introduces a novel control architecture for three amputees and nine additional subjects to concurrently control individual fingers of an artificial hand using two channels of context-specific haptic feedback. Artificial neural networks (ANNs) recognize subjects' electromyogram (EMG) patterns governing the artificial hand controller. ANNs also classify the directions objects slip across tactile sensors on the robotic fingertips, which are encoded via the vibration frequency of wearable vibrotactile actuators. Subjects implement control strategies with each finger simultaneously to prevent or permit slip as desired, achieving a 94.49% ± 8.79% overall success rate. Although no statistically significant difference exists between amputees' and non-amputees' success rates, amputees require more time to respond to simultaneous haptic feedback signals, suggesting a higher cognitive load. Nevertheless, amputees can accurately interpret multiple channels of nuanced haptic feedback to concurrently control individual robotic fingers, addressing the challenge of multitasking with dexterous prosthetic hands. [ABSTRACT FROM AUTHOR]

Bionic prosthetic hands hold the potential to replicate the functionality of human hands. The use of bionic limbs can assist amputees in performing everyday activities. This article systematically reviews the research progress on bionic prostheses, with a focus on control mechanisms, sensory feedback integration, and mechanical design innovations. It emphasizes the use of bioelectrical signals, such as electromyography (EMG), for prosthetic control and discusses the application of machine learning algorithms to enhance the accuracy of gesture recognition. Additionally, the paper explores advancements in sensory feedback technologies, including tactile, visual, and auditory modalities, which enhance user interaction by providing essential environmental feedback. The mechanical design of prosthetic hands is also examined, with particular attention to achieving a balance between dexterity, weight, and durability. Our contribution consists of compiling current research trends and identifying key areas for future development, including the enhancement of control system integration and improving the aesthetic and functional resemblance of prostheses to natural limbs. This work aims to inform and inspire ongoing research that seeks to refine the utility and accessibility of prosthetic hands for amputees, emphasizing user-centric innovations. [ABSTRACT FROM AUTHOR]

To analyze the structural characteristics of a human hand, data collection gloves were worn for typical grasping tasks. The hand manipulation characteristics, finger end pressure, and finger joint bending angle were obtained via an experiment based on the Feix grasping spectrum. Twelve types of tendon rope transmission paths were designed under the N + 1 type tendon drive mode, and the motion performance of these 12 types of paths applied to tendon-driven fingers was evaluated based on the evaluation metric. The experiment shows that the designed tendon path (d) has a good control effect on the fluctuations of tendon tension (within 0.25 N), the tendon path (e) has the best control effect on the joint angle of the tendon-driven finger, and the tendon path (l) has the best effect on reducing the friction between the tendon and the pulley. The obtained tendon-driven finger motion performance model based on 12 types of tendon paths is a good reference value for subsequent tendon-driven finger structure design and control strategies. [ABSTRACT FROM AUTHOR]

Prosthetic devices play an important role in rehabilitation of people with limb reduction. Although their functionality is getting closer to their human counterpart, there are some aspects regarding visual appeal that need more development. Our objective was to study human -- robot interaction and conclude a set of measures to aid in the design of prosthetics. Consequently, the design decisions must include a visual appeal aspect. We employed the method of conducting several surveys to find if there is a dependence between prosthetic device materials and visual appeal. The uncanny valley effect was also studied. We developed a methodology that can be used and further developed for design improvement towards considering the human factors in the process of design of prosthetic limbs. [ABSTRACT FROM AUTHOR]

IntroductionThe prosthetic hand has been aimed to restore hand functions by estimating the user’s intention via bio-signal and providing sensory feedback. Surface electromyogram (sEMG) is a widely used signal, and transcutaneous electrical nerve stimulation (TENS) is a promising method for sensory feedback. However, TENS currents can transmit through the skin and interfere as noise with the sEMG signals, referred to as “Artifact,” which degrades the performance of intention estimation.MethodIn this paper, we proposed an adaptive artifact removal method that can cancel artifacts separately across different frequencies and pulse widths of TENS. The modified least-mean-square adaptive filter uses the mean of previous artifacts as reference signals, and compensate using prior information of TENS system. Also temporal separation for artifact discrimination is applied to achieve high artifact removal efficiency. Four sEMG signals—two from flexor digitorum superficialis, flexor carpi ulnaris, extensor carpi ulnaris—was collected to validate signals both offline and online experiments.Results and DiscussionWe validated the filtering performance with twelve participants performing two experiments: artifact cancellation under variable conditions and a real-time hand control simulation called the target reaching experiment (TRE). The result showed that the Signal-to-Noise Ratio (SNR) increased by an average of 10.3dB, and the performance of four TRE indices recovered to the levels similar to those without TENS. The proposed method can significantly improve signal quality via artifact removal in the context of sensory feedback through TENS in prosthetic systems.

Current bionic hands lack the ability of fine force manipulation for grasping fragile objects due to missing human neuromuscular compliance in control. This incompatibility between prosthetic devices and the sensorimotor system has resulted in a high abandonment rate of hand prostheses. To tackle this challenge, we employed a neuromorphic modeling approach, biorealistic control, to regain human-like grasping ability. The biorealistic control restored muscle force regulation and stiffness adaptation using neuromorphic modeling of the neuromuscular reflex units, which was capable of real-time computing of model outputs. We evaluated the dexterity of the biorealistic control with a set of delicate grasp tasks that simulated varying challenging scenarios of grasping fragile objects in daily activities of life, including the box and block task, the glass box task, and the potato chip task. The performance of the biorealistic control was compared with that of proportional control. Results indicated that the biorealistic control with the compliance of the neuromuscular reflex units significantly outperformed the proportional control with more efficient grip forces, higher success rates, fewer break and drop rates. Post-task survey questionnaires revealed that the biorealistic control reduced subjective burdens of task difficulty and improved subjective confidence in control performance significantly. The outcome of the evaluation confirmed that the biorealistic control could achieve superior abilities in fine, accurate, and efficient grasp control for prosthetic users.

Humans have a complex way of expressing their intuitive intentions in real gestures. That is why many gesture detection and recognition techniques have been studied and developed. There are many methods of human hand signal reading, such as those using electroencephalography, electrocorticography, and electromyography, as well as methods for gesture recognition. In this paper, we present a method based on real-time surface electroencephalography hand-based gesture recognition using a multilayer neural network. For this purpose, the sEMG signals have been amplified, filtered and sampled; then, the data have been segmented, feature extracted and classified for each gesture. To validate the method, 100 signals for three gestures with 64 samples each signal have been recorded from 2 users with OYMotion sensors and 100 signals for three gestures from 4 users with the MyWare sensors. These signals were used for feature extraction and classification using an artificial neuronal network. The model converges after 10 sessions, achieving 98% accuracy. As a result, an algorithm was developed that aimed to recognize two specific gestures (handling a bottle and pointing with the index finger) in real time with 95% accuracy.

BACKGROUND: 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. OBJECTIVE: 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. METHODS: 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. RESULTS: 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. CONCLUSIONS: 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. [ABSTRACT FROM AUTHOR]

Gripping objects firmly and quickly is an important function of the human hand for everyday life. Prosthetic devices face significant challenges in replicating these capabilities, particularly in achieving a delicate balance between swift grasping and substantial grip strength while adhering to weight and form-factor constraints. To address these challenges, this study introduces a novel posture-dependent variable transmission (PDVT) that mimics the human hand's behavior by employing a spiral-shaped spool. The PDVT's spiral-shaped spool replicates the human hand's quick and gentle pre-contact movements followed by a stronger force application after contact with the object. Additionally, a compressive series elastic spring enhances tendon tension across a wide range of finger postures. The manufacturing method of PDVT, utilizing both 3D printing and metal processing, enables the creation of complex spiral shapes. The PDVT demonstrates improvements in both speed and grip strength compared to conventional rigid spool mechanisms. The PDVT has the potential to be applied to various robotic grasping systems. [ABSTRACT FROM AUTHOR]

In this article, 3D‐printed anthropomorphic hands for prosthetic or robotic applications are reviewed as 3D printing has transformed manufacturing by enabling the creation of intricate structures layer by layer, offering design freedom and efficiency. This review categorizes 3D‐printed anthropomorphic hands based on actuation, transmission, joint, and functional features like sensing and grasp patterns. It also assesses the level of anthropomorphism and validation methods by presenting criteria in prosthetic and robotic applications. Then, the article discusses 3D printing technologies in their usage and types, highlighting the advantages of multi‐material capabilities and integration of different hand components. Future directions on structural components, anthropomorphism, validation, and use of 3D printing are discussed, focusing on trends that only 3D printing technology can achieve in anthropomorphic hand development. [ABSTRACT FROM AUTHOR]

Background: Advanced prosthetic hands may embed nanosensors and microelectronics in their cosmetic skin. Heat influx may cause damage to these delicate structures. Protecting the integrity of the prosthetic hand becomes critical and necessary to ensure sustainable function. This study aims to mimic the sensorimotor control strategy of the human hand in perceiving nociceptive stimuli and triggering self-protective mechanisms and to investigate how similar neuromorphic mechanisms implemented in prosthetic hand can allow amputees to both volitionally release a hot object upon a nociceptive warning and achieve reinforced release via a bionic withdrawal reflex. Methods: A steady-state temperature prediction algorithm was proposed to shorten the long response time of a thermosensitive temperature sensor. A hybrid sensory strategy for transmitting force and a nociceptive temperature warning using transcutaneous electrical nerve stimulation based on evoked tactile sensations was designed to reconstruct the nociceptive sensory loop for amputees. A bionic withdrawal reflex using neuromorphic muscle control technology was used so that the prosthetic hand reflexively opened when a harmful temperature was detected. Four able-bodied subjects and two forearm amputees randomly grasped a tube at the different temperatures based on these strategies. Results: The average prediction error of temperature prediction algorithm was 8.30 ± 6.00%. The average success rate of six subjects in perceiving force and nociceptive temperature warnings was 86.90 and 94.30%, respectively. Under the reinforcement control mode in Test 2, the median reaction time of all subjects was 1.39 s, which was significantly faster than the median reaction time of 1.93 s in Test 1, in which two able-bodied subjects and two amputees participated. Results demonstrated the effectiveness of the integration of nociceptive sensory strategy and withdrawal reflex control strategy in a closed loop and also showed that amputees restored the warning of nociceptive sensation while also being able to withdraw from thermal danger through both voluntary and reflexive protection. Conclusion: This study demonstrated that it is feasible to restore the sensorimotor ability of amputees to warn and react against thermal nociceptive stimuli. Results further showed that the voluntary release and withdrawal reflex can work together to reinforce heat protection. Nevertheless, fusing voluntary and reflex functions for prosthetic performance in activities of daily living awaits a more cogent strategy in sensorimotor control. [ABSTRACT FROM AUTHOR]

Denoising is an essential step in data mining. It makes an effort to remove noise from the picture without sacrificing any of the important elements. The primary method utilized in this project to assess the effectiveness of phocomelia images is Kmeans clustering. The PSNR and MSE values of the denoised image are computed. In the end, the best technique for denoising medical images is chosen based on the PSNR values from the collection of patient data necessary for the fitting of upper limb devices. Using image processing, the bone health of phocomelia patients are evaluated and specific information extracted from MRI scans. Electromyography (EMG) sensors pick up electrical impulses, convert them into hand movements that the user wants, and flex the muscles in the residual limb directly below the elbow. The same muscles that enable hand function in humans are also felt by the system using microcontrollers. Through the integration of sophisticated sensor technologies with responsive prosthetic design idea, this newly developed technique enhances feedback from the proprioceptive system for patients with phocomelia, hence promoting natural movement and improving functional outcomes. As a result, it provides phocomelia patients a special method for acquiring superior result from biofeedback system. [ABSTRACT FROM AUTHOR]

Prosthetic devices play an important role in rehabilitation of people with limb reduction. Although their functionality is getting closer to their human counterpart, there are some aspects regarding visual appeal that need more development. Our objective was to study human – robot interaction and conclude a set of measures to aid in the design of prosthetics. Consequently, the design decisions must include a visual appeal aspect. We employed the method of conducting several surveys to find if there is a dependence between prosthetic device materials and visual appeal. The uncanny valley effect was also studied. We developed a methodology that can be used and further developed for design improvement towards considering the human factors in the process of design of prosthetic limbs.

Highly impaired individuals stand to benefit greatly from cutting-edge bionic technology, however concurrent functional deficits may complicate the adaptation of such technology. Here, we present a case in which a visually impaired individual with bilateral burn injury amputation was provided with a novel transradial neuromusculoskeletal prosthesis comprising skeletal attachment via osseointegration and implanted electrodes in nerves and muscles for control and sensory feedback. Difficulties maintaining implant hygiene and donning and doffing the prosthesis arose due to his contralateral amputation, ipsilateral eye loss, and contralateral impaired vision necessitating continuous adaptations to the electromechanical interface. Despite these setbacks, the participant still demonstrated improvements in functional outcomes and the ability to control the prosthesis in various limb positions using the implanted electrodes. Our results demonstrate the importance of a multidisciplinary, iterative, and patient-centered approach to making cutting-edge technology accessible to patients with high levels of impairment.

Intuitive regression control of prostheses relies on training algorithms to correlate biological recordings to motor intent. The quality of the training dataset is critical to run-time regression performance, but accurately labeling intended hand kinematics after hand amputation is challenging. In this study, we quantified the accuracy and precision of labeling hand kinematics using two common training paradigms: 1) mimic training, where participants mimic predetermined motions of a prosthesis, and 2) mirror training, where participants mirror their contralateral intact hand during synchronized bilateral movements. We first explored this question in healthy non-amputee individuals where the ground-truth kinematics could be readily determined using motion capture. Kinematic data showed that mimic training fails to account for biomechanical coupling and temporal changes in hand posture. Additionally, mirror training exhibited significantly higher accuracy and precision in labeling hand kinematics. These findings suggest that the mirror training approach generates a more faithful, albeit more complex, dataset. Accordingly, mirror training resulted in significantly better offline regression performance when using a large amount of training data and a non-linear neural network. Next, we explored these different training paradigms online, with a cohort of unilateral transradial amputees actively controlling a prosthesis in real-time to complete a functional task. Overall, we found that mirror training resulted in significantly faster task completion speeds and similar subjective workload. These results demonstrate that mirror training can potentially provide more dexterous control through the utilization of task-specific, user-selected training data. Consequently, these findings serve as a valuable guide for the next generation of myoelectric and neuroprostheses leveraging machine learning to provide more dexterous and intuitive control.

Prosthetic hands have significant potential to restore the manipulative capabilities and self-confidence of amputees and enhance their quality of life. However, incompatibility between prosthetic devices and residual limbs can lead to secondary injuries such as skin pressure ulcers and restricted joint motion, contributing to a high prosthesis abandonment rate. To address these challenges, this study introduces a data-driven design framework (D3Frame) utilizing a multi-index optimization method. By incorporating motion/ pressure data, as well as clinical criteria such as pain threshold/ tolerance, from various anatomical sites on the residual limbs of amputees, this framework aims to optimize the structural design of the prosthetic socket, including the Antecubital Channel (AC), Lateral Epicondylar Region Contour (LC), Medial Epicondylar Region Contour (MC), Olecranon Region Contour (OC), Lateral Flexor/ Extensor Region (LR), and Medial Flexor/ Extensor Region (MR). Experiments on five forearm amputees verified the improved adaptability of the optimized socket compared to traditional sockets under three load conditions. The experimental results revealed a modest score enhancement on standard clinical scales and reduced muscle fatigue levels. Specifically, the percent effort of muscles and slope value of mean/ median frequency decreased by 19%, 70%, and 99% on average, respectively, and the average values of mean/ median frequency in the motion cycle both increased by approximately 5%. The proposed D3Frame in this study was applied to optimize the structural aspects of designated regions of the prosthetic socket, offering the potential to aid prosthetists in prosthesis design and, consequently, augmenting the adaptability of prosthetic devices.

Sensory feedback provides critical interactive information for the effective use of hand prostheses. Non-invasive neural interfaces allow convenient access to the sensory system, but they communicate a limited amount of sensory information. This study examined a novel approach that leverages a direct and natural sensory afferent pathway, and enables an evoked tactile sensation (ETS) of multiple digits in the projected finger map (PFM) of participants with forearm amputation non-invasively. A bidirectional prosthetic interface was constructed by integrating the non-invasive ETS-based feedback system into a commercial prosthetic hand. The pressure information of five fingers was encoded linearly by the pulse width modulation range of the buzz sensation. We showed that simultaneous perception of multiple digits allowed participants with forearm amputation to identify object length and compliance by using information about contact patterns and force intensity. The ETS enhanced the grasp-and-transport performance of participants with and without prior experience of prosthetic use. The functional test of transport-and-identification further revealed improved execution in classifying object size and compliance using ETS-based feedback. Results demonstrated that the ETS is capable of communicating somatotopically compatible information to participants efficiently, and improves sensory discrimination and closed-loop prosthetic control. This non-invasive sensory interface may establish a viable way to restore sensory ability for prosthetic users who experience the phenomenon of PFM.

In this article, 3D‐printed anthropomorphic hands for prosthetic or robotic applications are reviewed as 3D printing has transformed manufacturing by enabling the creation of intricate structures layer by layer, offering design freedom and efficiency. This review categorizes 3D‐printed anthropomorphic hands based on actuation, transmission, joint, and functional features like sensing and grasp patterns. It also assesses the level of anthropomorphism and validation methods by presenting criteria in prosthetic and robotic applications. Then, the article discusses 3D printing technologies in their usage and types, highlighting the advantages of multi‐material capabilities and integration of different hand components. Future directions on structural components, anthropomorphism, validation, and use of 3D printing are discussed, focusing on trends that only 3D printing technology can achieve in anthropomorphic hand development.

For people who have experienced a spinal cord injury or an amputation, the recovery of sensation and motor control could be incomplete despite noteworthy advances with invasive neural interfaces. Our objective is to explore the feasibility of a novel biohybrid robotic hand model to investigate aspects of tactile sensation and sensorimotor integration with a pre-clinical research platform. Our new biohybrid model couples an artificial hand with biological neural networks (BNN) cultured in a multichannel microelectrode array (MEA). We decoded neural activity to control a finger of the artificial hand that was outfitted with a tactile sensor. The fingertip sensations were encoded into rapidly adapting (RA) or slowly adapting (SA) mechanoreceptor firing patterns that were used to electrically stimulate the BNN. We classified the coherence between afferent and efferent electrodes in the MEA with a convolutional neural network (CNN) using a transfer learning approach. The BNN exhibited the capacity for functional specialization with the RA and SA patterns, represented by significantly different robotic behavior of the biohybrid hand with respect to the tactile encoding method. Furthermore, the CNN was able to distinguish between RA and SA encoding methods with 97.84% ± 0.65% accuracy when the BNN was provided tactile feedback, averaged across three days in vitro (DIV). This novel biohybrid research platform demonstrates that BNNs are sensitive to tactile encoding methods and can integrate robotic tactile sensations with the motor control of an artificial hand. This opens the possibility of using biohybrid research platforms in the future to study aspects of neural interfaces with minimal human risk. [ABSTRACT FROM AUTHOR]

The routine use of prosthetic hands significantly enhances amputees' daily lives, yet it often introduces cognitive load and reduces reaction speed. To address this issue, we introduce a wearable semi-autonomous hierarchical control framework tailored for amputees. Drawing inspiration from the visual processing stream in humans, a fully autonomous bionic controller is integrated into the prosthetic hand control system to offload cognitive burden, complemented by a Human-in-the-Loop (HIL) control method. In the ventral-stream phase, the controller integrates multi-modal information from the user's hand–eye coordination and biological instincts to analyze the user's movement intention and manipulate primitive switches in the variable domain of view. Transitioning to the dorsal-stream phase, precise force control is attained through the HIL control strategy, combining feedback from the prosthetic hand's sensors and the user's electromyographic (EMG) signals. The effectiveness of the proposed interface is demonstrated by the experimental results. Our approach presents a more effective method of interaction between a robotic control system and the human. [ABSTRACT FROM AUTHOR]

This study offers a concise overview of classifying hand movements based on their kinetic and myoelectric characteristics. In this work, we propose utilizing Electromyography (EMG) signals to distinguish these movements, especially for applications like wheelchair guidance and prosthetic control. Unlike prior research on forearmderived EMG signals, this study employs a multi-channel surface Electromyography (sEMG) signal to effectively categorize distinct movements, crucial for prosthetic control. To extract informative signal features, a two-step process is deployed, beginning with the transformation of raw EMG data using Discrete Wavelet Transform (DWT) for feature extraction. The ensuing classification task employs an Artificial Neural Network (ANN), overseen by the generation of corresponding confusion matrices (CMs) based on input features. The efficacy of our approach is validated using a human hand EMG signal dataset sourced from the UCI Machine Learning Repository. This dataset encompasses recordings from 36 subjects across 8 channels (sensors), spanning multiple days. The suggested algorithm utilizes unprocessed bipolar EMG data for both training and evaluating the performance of the neural network-based classifier. Significantly, when assessing the algorithm's performance offline, it becomes evident that the use of Frequency Domain (FD) features in sequential signal processing outperforms Standard Linear Discriminant Analysis (LDA) algorithms. The combination of the DWT and ANN results in significantly improved performance and sustained robustness of the classification algorithm. Empirical findings prove the effectiveness of this approach, achieving an accuracy of 89.9%in classifying seven distinct hand movement categories accurately. Additionally, the analysis shows an increasing classification accuracy as the dataset size increases. [ABSTRACT FROM AUTHOR]

The ability of a novel biorealistic hand prosthesis for grasp force control reveals improved neural compatibility between the human-prosthetic interaction. The primary purpose here was to validate a virtual training platform for amputee subjects and evaluate the respective roles of visual and tactile information in fundamental force control tasks. We developed a digital twin of tendon-driven prosthetic hand in the MuJoCo environment. Biorealistic controllers emulated a pair of antagonistic muscles controlling the index finger of the virtual hand by surface electromyographic (sEMG) signals from amputees’ residual forearm muscles. Grasp force information was transmitted to amputees through evoked tactile sensation (ETS) feedback. Six forearm amputees participated in force tracking and holding tasks under different feedback conditions or using their intact hands. Test results showed that visual feedback played a predominant role than ETS feedback in force tracking and holding tasks. However, in the absence of visual feedback during the force holding task, ETS feedback significantly enhanced motor performance compared to feedforward control alone. Thus, ETS feedback still supplied reliable sensory information to facilitate amputee’s ability of stable grasp force control. The effects of tactile and visual feedback on force control were subject-specific when both types of feedback were provided simultaneously. Amputees were able to integrate visual and tactile information to the biorealistic controllers and achieve a good sensorimotor performance in grasp force regulation. The virtual platform may provide a training paradigm for amputees to adapt the biorealistic hand controller and ETS feedback optimally. [ABSTRACT FROM AUTHOR]

Sensory feedback provides critical interactive information for the effective use of hand prostheses. Non-invasive neural interfaces allow convenient access to the sensory system, but they communicate a limited amount of sensory information. This study examined a novel approach that leverages a direct and natural sensory afferent pathway, and enables an evoked tactile sensation (ETS) of multiple digits in the projected finger map (PFM) of participants with forearm amputation non-invasively. A bidirectional prosthetic interface was constructed by integrating the non-invasive ETS-based feedback system into a commercial prosthetic hand. The pressure information of five fingers was encoded linearly by the pulse width modulation range of the buzz sensation. We showed that simultaneous perception of multiple digits allowed participants with forearm amputation to identify object length and compliance by using information about contact patterns and force intensity. The ETS enhanced the grasp-and-transport performance of participants with and without prior experience of prosthetic use. The functional test of transport-and-identification further revealed improved execution in classifying object size and compliance using ETS-based feedback. Results demonstrated that the ETS is capable of communicating somatotopically compatible information to participants efficiently, and improves sensory discrimination and closed-loop prosthetic control. This non-invasive sensory interface may establish a viable way to restore sensory ability for prosthetic users who experience the phenomenon of PFM. [ABSTRACT FROM AUTHOR]

Prosthetic hands have significant potential to restore the manipulative capabilities and self-confidence of amputees and enhance their quality of life. However, incompatibility between prosthetic devices and residual limbs can lead to secondary injuries such as skin pressure ulcers and restricted joint motion, contributing to a high prosthesis abandonment rate. To address these challenges, this study introduces a data-driven design framework (D3Frame) utilizing a multi-index optimization method. By incorporating motion/ pressure data, as well as clinical criteria such as pain threshold/ tolerance, from various anatomical sites on the residual limbs of amputees, this framework aims to optimize the structural design of the prosthetic socket, including the Antecubital Channel (AC), Lateral Epicondylar Region Contour (LC), Medial Epicondylar Region Contour (MC), Olecranon Region Contour (OC), Lateral Flexor/ Extensor Region (LR), and Medial Flexor/ Extensor Region (MR). Experiments on five forearm amputees verified the improved adaptability of the optimized socket compared to traditional sockets under three load conditions. The experimental results revealed a modest score enhancement on standard clinical scales and reduced muscle fatigue levels. Specifically, the percent effort of muscles and slope value of mean/ median frequency decreased by 19%, 70%, and 99% on average, respectively, and the average values of mean/ median frequency in the motion cycle both increased by approximately 5%. The proposed D3Frame in this study was applied to optimize the structural aspects of designated regions of the prosthetic socket, offering the potential to aid prosthetists in prosthesis design and, consequently, augmenting the adaptability of prosthetic devices. [ABSTRACT FROM AUTHOR]

Intuitive regression control of prostheses relies on training algorithms to correlate biological recordings to motor intent. The quality of the training dataset is critical to run-time regression performance, but accurately labeling intended hand kinematics after hand amputation is challenging. In this study, we quantified the accuracy and precision of labeling hand kinematics using two common training paradigms: 1) mimic training, where participants mimic predetermined motions of a prosthesis, and 2) mirror training, where participants mirror their contralateral intact hand during synchronized bilateral movements. We first explored this question in healthy non-amputee individuals where the ground-truth kinematics could be readily determined using motion capture. Kinematic data showed that mimic training fails to account for biomechanical coupling and temporal changes in hand posture. Additionally, mirror training exhibited significantly higher accuracy and precision in labeling hand kinematics. These findings suggest that the mirror training approach generates a more faithful, albeit more complex, dataset. Accordingly, mirror training resulted in significantly better offline regression performance when using a large amount of training data and a non-linear neural network. Next, we explored these different training paradigms online, with a cohort of unilateral transradial amputees actively controlling a prosthesis in real-time to complete a functional task. Overall, we found that mirror training resulted in significantly faster task completion speeds and similar subjective workload. These results demonstrate that mirror training can potentially provide more dexterous control through the utilization of task-specific, user-selected training data. Consequently, these findings serve as a valuable guide for the next generation of myoelectric and neuroprostheses leveraging machine learning to provide more dexterous and intuitive control. [ABSTRACT FROM AUTHOR]

Highly impaired individuals stand to benefit greatly from cutting-edge bionic technology, however concurrent functional deficits may complicate the adaptation of such technology. Here, we present a case in which a visually impaired individual with bilateral burn injury amputation was provided with a novel transradial neuromusculoskeletal prosthesis comprising skeletal attachment via osseointegration and implanted electrodes in nerves and muscles for control and sensory feedback. Difficulties maintaining implant hygiene and donning and doffing the prosthesis arose due to his contralateral amputation, ipsilateral eye loss, and contralateral impaired vision necessitating continuous adaptations to the electromechanical interface. Despite these setbacks, the participant still demonstrated improvements in functional outcomes and the ability to control the prosthesis in various limb positions using the implanted electrodes. Our results demonstrate the importance of a multidisciplinary, iterative, and patient-centered approach to making cutting-edge technology accessible to patients with high levels of impairment. [ABSTRACT FROM AUTHOR]

Aiming at the control of a powered prosthetic hand, this paper compares methods for the classification of intended hand motions using muscle bulging patterns caused by muscle contraction. Two sheets of Polyvinylidene Difluoride (PVDF) film were used as sensors to detect the muscle bulging on the forearm caused by intended hand motions. A neural network had been successfully trained for the classification of six types of hand motions using the muscle bulging pattern detected by the two PVDF sensors. In this paper, we further studied the motion classification methods of back propagation neural network (BPNN), k‐nearest neighbor algorithm (k‐NN), and support vector machine (SVM) to compare their classification performance. We found that all three methods had a similar classification rate of about 95% for six types of hand motions. Moreover, a regressive analysis comparison of the time for each classification method to converge to 95% of the total classification rate showed that SVM converged significantly earlier than BPNN and k‐NN. The time it takes for SVM to converge the classification rate to 95% is less than 0.1 s, suggesting that real‐time motion classification is possible by using SVM. In a similar manner, we found that SVM requires the least training data of the three methods at only nine trials for a type of motion. Furthermore, SVM had the highest classification rate at about 90% in practical experimental conditions. In conclusion, SVM was found to be the most practical of the three methods. [ABSTRACT FROM AUTHOR]

Currently, the driving motor used in a dexterous prosthetic hand is limited by the driving principle, and it has the characteristics of a complex structure, slow response, low positioning accuracy, and excessive volume. There are special requirements in terms of quality and quality, and traditional motor drives have greatly affected the progress of prosthetic robots. A motor (ultrasonic motor) has been developed over more than 30 years. It has the advantages of a small size, small mass, simple structure, accurate positioning, high power density, and fast response time, which is enough to improve the driving mechanism performance of the prosthetic hand with a connecting rod. In this paper, the structural characteristics of the prosthetic hand will be analyzed, and the modal analysis, harmonic response analysis, and transient analysis simulation of the longitudinal vibration linear motor stator used in the prosthetic hand with a connecting rod will be carried out in order to provide preliminary preparation for the feasible design and manufacture of the size of the ultrasonic driver structure used for the prosthetic hand with a connecting rod. [ABSTRACT FROM AUTHOR]

The human hand is a complex and versatile organ that enables humans to interact with the environment, communicate, create, and use tools. The control of the hand by the brain is a crucial aspect of human cognition and behaviour, but also a challenging problem for both neuroscience and engineering. The aim of this study is to review the current state of the art in hand and grasp control from a neuroscientific perspective, focusing on the brain mechanisms that underlie sensory integration for hand control and the engineering implications for developing artificial hands that can mimic and interface with the human brain. The brain controls the hand by processing and integrating sensory information from vision, proprioception, and touch, using different neural pathways. The user's intention can be obtained to control the artificial hand by using different interfaces, such as electromyography, electroneurography, and electroencephalography. This and other sensory information can be exploited by different learning mechanisms that can help the user adapt to changes in sensory inputs or outputs, such as reinforcement learning, motor adaptation, and internal models. This work summarizes the main findings and challenges of each aspect of hand and grasp control research and highlights the gaps and limitations of the current approaches. In the last part, some open questions and future directions for hand and grasp control research are suggested by emphasizing the need for a neuroscientific approach that can bridge the gap between the brain and the hand. [ABSTRACT FROM AUTHOR]