Dexterous Prosthetic Hand Research Articles

Multichannel Sensorimotor Integration with a Dexterous Artificial Hand

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.

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

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

Multifunction Controlled 3D Printed Hand Prosthesis with the Myo Arm-Band by Hand Amputees

Upper limb amputations are highly impairing injuries that can substantially limit the quality of life of a person. The most advanced dexterous prosthetic hands have remarkable mechanical features. However, in most cases, the control systems are a simple extension of basic control protocols, making the use of the prosthesis not intuitive and sometimes complex. Furthermore, the cost of dexterous prosthetic hands is often prohibitive, especially for the pediatric population and developing countries. 3D-printed hand prostheses can represent an opportunity for the future. Open 3D models are increasingly being released, even for dexterous prostheses that are capable of moving each finger individually and actively rotating the thumb. However, the usage and testing of such devices by hand amputees (using electromyography and classification methods) is not well explored. This article aims to investigate the usage of a cost-effective system composed of a 3D-printed hand prosthesis and a low-cost myo electric armband. Two subjects with trans-radial amputation were asked to wear a custom-made socket supporting the Hand and the Thalmic Labs Myo armband. Afterward, the subjects were asked to control and use the prosthetic and to grasp several objects by attempting to perform a set of different hand gestures. Both the Hand and the Myo armband performed well during the test, which is encouraging considering that the Hand was developed as a research platform. The results are promising and show the feasibility of the multifunction control of dexterous 3D- printed hand prostheses based on low-cost setups. Factors such as the level of the amputation, neuromuscular fatigue, and mechanical limitations of the 3D-printed hand prosthesis can influence the performance of the setup. Practical aspects such as usability and robustness will need to be addressed for successful application in daily life.

Simulation Analysis of a Sandwich Cantilever Ultrasonic Motor for a Dexterous Prosthetic Hand.

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.

Simultaneous hand/wrist motion recognition and continuous grasp force estimation based on nonlinear spectral sEMG features for transradial amputees

Simultaneously and accurately estimating both grasp motion classes and force based on surface electromyogram (EMG) of residual arms is essential for precise grasping with a dexterous prosthetic hand. However, accurate grasping-mode prediction and grasping-force estimation often contradict each other. When performing hand grasping motion, different grasping forces will generate different EMG patterns, which would decay the grasp motion prediction accuracy. In this study, we proposed a framework of fusing motion classification and continuous grasp force estimation based on a new set of nonlinear Mel-Frequency Spectrum features of EMG signals. The performance of the proposed method was tested on eight able bodied (AB) subjects and four transradial amputees (TR) who are the end-users of dexterous prostheses. The experimental results showed that the newly proposed features obtained a significant reduction in the average classification error rate compared to other well-known feature sets, achieving improvements of about 5 % to 28 % in the average classification accuracy across all subjects and force levels. Simultaneously, the proposed features obtained the best continuous grasp force estimation accuracies with R2 (coefficient of determination) of 0.96 and 0.83, and root mean square of maximum voluntary contractions of grasp force (std%GFMVC) of 6.2 % and 10.5 % for the AB and TR subjects, respectively, compared to the previous EMG features. This study suggested that the proposed method would be helpful for the stable and fine manipulation of multifunctional prosthetic hand.

Myoelectric prosthesis hand grasp control following targeted muscle reinnervation in individuals with transradial amputation.

Despite the growing availability of multifunctional prosthetic hands, users' control and overall functional abilities with these hands remain limited. The combination of pattern recognition control and targeted muscle reinnervation (TMR) surgery, an innovative technique where amputated nerves are transferred to reinnervate new muscle targets in the residual limb, has been used to improve prosthesis control of individuals with more proximal upper limb amputations (i.e., shoulder disarticulation and transhumeral amputation). The goal of this study was to determine if prosthesis hand grasp control improves following transradial TMR surgery. Eight participants were trained to use a multi-articulating hand prosthesis under myoelectric pattern recognition control. All participated in home usage trials pre- and post-TMR surgery. Upper limb outcome measures were collected following each home trial. Three outcome measures (Southampton Hand Assessment Procedure, Jebsen-Taylor Hand Function Test, and Box and Blocks Test) improved 9-12 months post-TMR surgery compared with pre-surgery measures. The Assessment of Capacity for Myoelectric Control and Activities Measure for Upper Limb Amputees outcome measures had no difference pre- and post-surgery. An offline electromyography analysis showed a decrease in grip classification error post-TMR surgery compared to pre-TMR surgery. Additionally, a majority of subjects noted qualitative improvements in their residual limb and phantom limb sensations post-TMR. The potential for TMR surgery to result in more repeatable muscle contractions, possibly due to the reduction in pain levels and/or changes to phantom limb sensations, may increase functional use of many of the clinically available dexterous prosthetic hands.

Design and Experimental Evaluation of a Sensorimotor-inspired Grasping Strategy for Dexterous Prosthetic Hands.

Stable grasping without slips or crushing is a major challenge for amputees who lose the natural sensorimotor system in dynamically changing daily life environments. Amputees rely largely on visual cues to control the prosthetic hand to complete daily living activities due to a lack of haptic feedback. The human tactile sense can simultaneously feel normal and shear forces. When grasping objects based on the anticipated load conditions, the human hand adjusts the grasping force in real time based on shear force feedback. Here, a sensorimotor-inspired grasping strategy for a dexterous prosthetic hand is proposed to improve grasping performance. The proposed grasping strategy allows the amputee to intuitively control the prosthetic hand. The dexterous prosthetic hand can adaptively adjust the grasp force based on tactile sensory feedback to simultaneously prevent the slipping of objects with unknown shapes, weight, roughness, and softness. Experiments show that the myoelectrical prosthetic hand has grasping force adaptive adjustment and slip prevention ability and provides improved grasping compared to prosthetics with traditional open-loop control.

Optimal design of dexterous prosthetic hand with five-joint thumb and fingertip tactile sensors based on novel precision grasp metric

Current linkage-driven prosthetic hands still show limitations in aspects such as the thumb design and fingertip sensor. Moreover, linkage-driven prosthetic hands still lack quantitative precision grasp quality. In this study, we developed a novel thumb structure with coupled abduction–adduction and pronation–supination movement in the trapeziometacarpal joint. We also developed a fully integrated fingertip tactile sensor with all components embedded in the distal phalanx designed to facilitate in-hand precision manipulation. Furthermore, we devised a new metric to evaluate the precision grasp quality based on the force conditions during grasp. On the basis of this metric, we optimized the geometry parameters of the thumb and index finger using the Monte Carlo method. The results show that, compared with the anthropomorphic trajectory measured from a human index, the proposed method improves the grasping ability by more than 10%. Finally, we developed a prototype prosthetic hand based on the proposed design methods and demonstrated by experiment that it was able to perform human-like thumb opposition and to pass both precision and power grasp tests.

Simultaneous estimation of multi-finger forces by surface electromyography and accelerometry signals

Myoelectric prostheses generally use pattern recognition strategies to decode users’ gesture intention; however, they lack intuitive force control. Regression strategy can extract force information of multiple degrees of freedom (DOFs) and has become a research focus in myoelectric control. In this paper, to realize a simultaneous estimation of continuous forces exerted at multi-fingertips, we used a generalized regression neural network (GRNN) to associate both surface electromyography (sEMG) and accelerometry (ACC) signals to finger forces of 6 DOFs (flexion of each finger plus thumb abduction). Totally nine force patterns were tested, including six single-DOF patterns (activation of each DOF) and three multi-DOF patterns of simultaneous activation of two DOFs. We extracted four popular sEMG feature sets from myoelectric signals and combined each of them with the mean feature of ACC signals to construct a multi-modal feature set. The estimation performance was evaluated by the coefficient of determination (R2), the normalized root mean squared error (NRMSE), and the mean absolute error (MAE). By combining the ACC modality, the estimation accuracy of all four sEMG feature sets was significantly improved. For intact subjects, the R2, NRMSE, and MAE values using the optimal feature set were 93.33 ± 3.45%, 2.85 ± 0.58%, and 0.34 ± 0.15, respectively. For amputees, the R2, NRMSE, and MAE values were 73.16 ± 5.79%, 5.43 ± 1.19%, and 0.73 ± 0.39, respectively. Besides, when the number of training samples decreased, the multi-modal feature set showed higher robustness. The results demonstrated the potential application of the proposed method for enabling natural and intuitive force control of dexterous prosthetic hands.

Real-time visual and EMG signals recognition to control dexterous prosthetic hand based on deep learning and machine learning

The revolution in prosthetic hands allows the evolution of a new generation of prostheses that increase artificial intelligence to control an adept hand. A suitable gripping and grasping action for different shapes of the objects is currently a challenging task of prosthetic hand design. The most artificial hands are based on electromyography signals. A novel approach has been proposed in this work using deep learning classification method for assorting items into seven gripping patterns based on EMG and image recognition. Hence, this approach conducting two scenarios; The first scenario is recording the EMG signals for five healthy participants for the basic hand movement (cylindrical, tip, spherical, lateral, palmar, and hook). Then three time-domain (standard deviation, mean absolute value, and the principal component analysis) are used to extract the EMG signal features. After that, the SVM is used to find the proper classes and achieve an accuracy that reaches 89%. The second scenario is collecting the 723 RGB images for 24 items and sorting them into seven classes, i.e., cylindrical, tip, spherical, lateral, palmar, hook, and full hand. The GoogLeNet algorithm is used for training based on 144 layers; these layers include the convolutional layers, ReLU activation layers, max-pooling layers, drop-out layers, and a softmax layer. The GoogLeNet achieves high training accuracy reaches 99%. Finally, the system is tested, and the experiments showed that the proposed visual hand based on the myoelectric control method (Vision-EMG) could significantly give recognition accuracy reaches 95%.

Human-machine shared control: New avenue to dexterous prosthetic hand manipulation

At present, albeit the dexterous hand prostheses of multiple degrees of freedom (DOFs) have become prosperous on the market, the user’s demand on intuitively operating these devices have not been well addressed so that their acceptance rate is relatively low. The unintuitive control method and inadequate sensory feedback are frequently cited as the two barriers to the successful application of these dexterous products. Recently, driven by the wave of artificial intelligence (AI), a series of shared control methods have emerged, in which “bodily function” (myoelectric control) and “artificial intelligence” (local autonomy, computer vision, etc.) are tightly integrated, and provided a new conceptual solution for the intuitive operation of dexterous prostheses. In this paper, the background and development trends of this type of methods are described in detail, and the potential development directions and the key technologies that need breakthroughs are indicated. In practice, we instantiate this shared control strategy by proposing a new method combining simultaneous myoelectric control, multi-finger grasp autonomy, and augmented reality (AR) feedback together. This method “divides” the human sophisticated reach-and-grasp task into several subtasks, and then “conquers” them by using different strategies from either human or machine perspective. It is highly expected that the shared control methods with hybrid human-machine intelligence could address the control problem of dexterous prostheses.

Computer Vision-Based Grasp Pattern Recognition With Application to Myoelectric Control of Dexterous Hand Prosthesis.

Artificial intelligence provides new feasibilities to the control of dexterous prostheses. To achieve suitable grasps over various objects, a novel computer vision-based classification method assorting objects into different grasp patterns is proposed in this paper. This method can be applied in the autonomous control of the multi-fingered prosthetic hand, as it can help users rapidly complete "reach-and-pick up" tasks on various daily objects with low demand on the myoelectric control. Firstly, an RGB-D image database (121 objects) was established according to four important grasp patterns (cylindrical, spherical, tripod, and lateral). The image samples in the RGB-D dataset were acquired on a large variety of daily objects of different sizes, shapes, postures (16), as well as different illumination conditions (4) and camera positions (4). Then, different inputs and structures of the discrimination model (multilayer CNN) were tested in terms of the classification success rate through cross-validation. Our results showed that depth data play an important role in grasp pattern recognition. The bimodal data (Gray-D) integrating both grayscale and depth information about the objects can improve the classification accuracy acquired from the RGB images (> 10%) effectively. Within the database, the network could achieve the classification with high accuracy (98%); it also has a strong generalization capability on novel samples (93.9 ± 3.0%). We finally applied the method on a dexterous prosthetic hand and tested the whole system on performing the "reach-and-pick up" tasks. The experiments showed that the proposed computer vision-based myoelectric control method (Vision-EMG) could significantly improve the control effectiveness (6.4 s), with comparison to the traditional coding-based myoelectric control method (Coding-EMG, 13 s ).

Classification of Electromyographic Hand Gesture Signals Using Modified Fuzzy C-Means Clustering and Two-Step Machine Learning Approach.

Understanding and classifying electromyogram (EMG) signals is of significance for dexterous prosthetic hand control, sign languages, grasp recognition, human-machine interaction, etc.. The existing research of EMG-based hand gesture classification faces the challenges of unsatisfied classification accuracy, insufficient generalization ability, lack of training data and weak robustness. To address these problems, this paper combines unsupervised and supervised learning methods to classify an EMG dataset consisting of 10 classes of hand gestures. To lessen the difficulty of classification, clustering methods including subtractive clustering and fuzzy c-means (FCM) clustering algorithms are employed first to obtain the initial partition of the inputs. In particular, modified FCM algorithm is proposed to accustom the conventional FCM to the multi-class classification problem. Based on the grouping information obtained from clustering, a type of two-step supervised learning approach is proposed. Specifically, a top-classifier and three sub-classifiers integrated with windowing method and majority voting are employed to accomplish the two-step classification. The results demonstrate that the proposed method achieves 100% test accuracy and the strongest robustness compared to the conventional machine learning approaches, which shows the potential for industrial and healthcare applications, such as movement intention detection, grasp recognition and dexterous prostheses control.

Classification of electromyographic hand gesture signals using machine learning techniques

The electromyogram (EMG) signals from an individual’s muscles can reflect the biomechanics of human movement. The accurate classification of individual and combined finger movements using surface EMG signals is able to support many applications such as dexterous prosthetic hand control. The existing research of EMG-based hand gesture classification faces the challenges of inaccurate classification, insufficient generalization ability and weak robustness. To address these problems, this paper proposes a deep learning model that combines convolutional auto-encoder and convolutional neural network (CAE+CNN) to classify an EMG dataset consisting of 10 classes of hand gestures. The proposed method shrinks the inputs into a smaller latent space representation using CAE and the resultant compressed features are served as inputs of CNN, which reduces the redundancy of EMG signals and improves the classification accuracy and training efficiency. Besides, to enhance the robustness and generalization ability for classification, a data processing approach is proposed which combines the windowing method and majority voting of the obtained results from the classifier. In addition, comprehensive comparative study is carried out with 8 widely applied and state-of-the-art classifiers in terms of classification accuracy, robustness subject to noise and statistical analysis (sensitivity, specificity, precision, F1 Score and Matthews correlation coefficient). The results demonstrates that the integration of windowing method, CAE+CNN and majority voting achieves the best performance (99.38% test accuracy for the data without adding noise, which is 3.78% higher than the best classifier used for comparison), strongest robustness (achieved 98.13% test accuracy when Gaussian noise of level 1e-5 is added to the raw dataset, which is 4.07% higher than the best classifier used for comparison) and statistical properties compared to other classifiers, which shows the potential for healthcare applications such as movement intention detection and dexterous prostheses control.

Electromyogram (EMG) based fingers movement recognition using sparse filtering of wavelet packet coefficients

Surface electromyogram (EMG) signals collected from amputee’s residual limb have been utilized to control the prosthetic limb movements for many years. The extensive research has been carried out to classify arm and hand movements by many researchers. However, for control of the more dexterous prosthetic hand, controlling of single and multiple prosthetic fingers needs to be focused. The classification of single and multiple finger movements is challenging as the large number of EMG electrodes/channels are required to classify more number of movement classes. Also the misclassification rate increases significantly with the increased number of finger movements. To enable such control, the most informative and discriminative feature set which can accurately differentiate between different finger movements must be extracted. This work proposes an accurate and novel scheme for feature set extraction and projection based on Sparse Filtering of wavelet packet coefficients. Unlike the existing feature extraction-projection techniques, the proposed method can classify a large number of single and multiple finger movements accurately with reduced hardware complexity. The proposed method is compared to other combinations of feature extraction-reduction methods and validated on EMG dataset collected from eight subjects performing 15 different finger movements. The experimental results show the importance of the proposed scheme in comparison with existing feature extraction-projection schemes with an average accuracy of 99.52% ± 0.6%. The results also indicate that the subset of five EMG channels delivers similar accuracy (>99%) to those obtained from all eight channels. The resultant accuracy values are improved over the existing one reported in the literature, whereas only one-third numbers of channels per identified motions are employed. The experimental results and analysis of variance tests (p < 0.001) prove the feasibility of the proposed work.

Surface Electromyographic Biofeedback as a Rehabilitation Tool for Patients with Global Brachial Plexus Injury Receiving Bionic Reconstruction.

In patients with global brachial plexus injury and lack of biological treatment alternatives, bionic reconstruction, including the elective amputation of the functionless hand and its replacement with a prosthesis, has recently been described. Optimal prosthetic function depends on a structured rehabilitation protocol, as residual muscle activity in a patient's arm is later translated into prosthetic function. Surface electromyographic (sEMG) biofeedback has been used during rehabilitation after stroke, but has so far not been used in patients with complex peripheral nerve injuries. Here, we present our rehabilitation protocol implemented in patients with global brachial plexus injuries suitable for bionic reconstruction, starting from identification of sEMG signals to final prosthetic training. This structured rehabilitation program facilitates motor relearning, which may be a cognitively debilitating process after complex nerve root avulsion injuries, aberrant re-innervation and extra-anatomical reconstruction (as is the case with nerve transfer surgery). The rehabilitation protocol using sEMG biofeedback aids in the establishment of new motor patterns as patients are being made aware of the advancing re-innervation process of target muscles. Additionally, faint signals may also be trained and improved using sEMG biofeedback, rendering a clinically "useless"muscle (exhibiting muscle strength M1 on the British Medical Research Council [BMRC] scale) eligible for dexterous prosthetic hand control. Furthermore, functional outcome scores after successful bionic reconstruction are presented in this article.

Modular Prosthetic Limb Control by an Individual with Congenital Upper-Limb Amputation: A Case Report

ABSTRACT Introduction This article presents a unique case study of an individual with congenital limb loss and long-time (&gt;56 years) body-powered prosthesis use, who was able to control a sophisticated robotic upper-limb prosthesis using surface electromyography signals and pattern recognition (PR) algorithms. This case demonstrates that individuals with congenital limb amputation are able to learn unique strategies to intuitively control a dexterous prosthetic limb. Materials and Methods After completing four training sessions using a virtual integration environment, a single subject participated in 12 in-laboratory clinical training sessions using the modular prosthetic limb (MPL)—a novel multiple–degree-of-freedom dexterous upper-limb prosthesis prototype. Baseline assessments were made with her body-powered prosthesis, as well as a two-site direct-control myoelectric Bebionic she had recently received. Functional assessments with the MPL were conducted during sessions 6 and 12. Outcome measures included timed box and blocks (BB) test, Assessment of Capacity for Myoelectric Control (ACMC), Jebsen-Taylor Hand Function Test (JTHFT), Trinity Amputation and Prosthesis Experience Scale, Upper Extremity Functional Scale (UEFS), and NASA Task Load Index. Results The subject was able to control two independent wrist degrees of freedom and up to three independent hand grasps of the MPL, using an array of surface electrodes. Improvements in the BB and ACMC were observed, although the total time to complete the JTHFT stayed relatively the same from weeks 6 to 12, using the MPL. While her enpoint perceived funcitonal ability with the MPL was 58% compared with 83% with her personal myoelectric prosthesis (12 hours of use vs 4–5 weeks of use as denoted on the UEFS); the subject reported short length of training, a long-term body-powered prosthetic user with congenital limb loss was able to demonstrate objective improvements in control of a dexterous prosthetic hand over a 12-week period of in-laboratory training, achieving intuitive independent control of a variety of simultaneous individual wrist motions and grasp patterns using PR. Conclusions This case demonstrates that even individuals with congenital amputation may be considered as candidates for upper-limb PR-controlled myoelectric prosthetic devices using surface electrodes.

Dexterous Prosthetic Hand with Force Perception and Electrical Stimulation

Dexterous Prosthetic Hand with Force Perception and Electrical Stimulation

Improved Training Paradigms and Motor-decode Algorithms: Results from Intact Individuals and a Recent Transradial Amputee with Prior Complex Regional Pain Syndrome.

Working towards improved neuromyoelectric control of dexterous prosthetic hands, we explored how differences in training paradigms affect the subsequent online performance of two different motor-decode algorithms. Participants included two intact subjects and one participant who had undergone a recent transradial amputation after complex regional pain syndrome (CRPS) and multi-year disuse of the affected hand. During algorithm training sessions, participants actively mimicked hand movements appearing on a computer monitor. We varied both the duration of the hold-time (0.1 s or 5 s) at the end-point of each of six different digit and wrist movements, and the order in which the training movements were presented (random or sequential). We quantified the impact of these variations on two different motordecode algorithms, both having proportional, six-degree-offreedom (DOF) control: a modified Kalman filter (MKF) previously reported by this group, and a new approach - a convolutional neural network (CNN). Results showed that increasing the hold-time in the training set improved run-time performance. By contrast, presenting training movements in either random or sequential order had a variable and relatively modest effect on performance. The relative performance of the two decode algorithms varied according to the performance metric. This work represents the first-ever amputee use of a CNN for real-time, proportional six-DOF control of a prosthetic hand. Also novel was the testing of implanted high-channelcount devices for neuromyoelectric control shortly after amputation, following CRPS and long-term hand disuse. This work identifies key factors in the training of decode algorithms that improve their subsequent run-time performance.

A novel hybrid closed-loop control approach for dexterous prosthetic hand based on myoelectric control and electrical stimulation

PurposeThe purpose of this study is to present a novel hybrid closed-loop control method together with its performance validation for the dexterous prosthetic hand.Design/methodology/approachThe hybrid closed-loop control is composed of a high-level closed-loop control with the user in the closed loop and a low-level closed-loop control for the direct robot motion control. The authors construct the high-level control loop by using electromyography (EMG)-based human motion intent decoding and electrical stimulation (ES)-based sensory feedback. The human motion intent is decoded by a finite state machine, which can achieve both the patterned motion control and the proportional force control. The sensory feedback is in the form of transcutaneous electrical nerve stimulation (TENS) with spatial-frequency modulation. To suppress the TENS interfering noise, the authors propose biphasic TENS to concentrate the stimulation current and the variable step-size least mean square adaptive filter to cancel the noise. Eight subjects participated in the validation experiments, including pattern selection and egg grasping tasks, to investigate the feasibility of the hybrid closed-loop control in clinical use.FindingsThe proposed noise cancellation method largely reduces the ES noise artifacts in the EMG electrodes by 18.5 dB on average. Compared with the open-loop control, the proposed hybrid closed-loop control method significantly improves both the pattern selection efficiency and the egg grasping success rate, both in blind operating scenarios (improved by 1.86 s, p < 0.001, and 63.7 per cent, p < 0.001) or in common operating scenarios (improved by 0.49 s, p = 0.008, and 41.3 per cent, p < 0.001).Practical implicationsThe proposed hybrid closed-loop control method can be implemented on a prosthetic hand to improve the operation efficiency and accuracy for fragile objects such as eggs.Originality/valueThe primary contribution is the proposal of the hybrid closed-loop control, the spatial-frequency modulation method for the sensory feedback and the noise cancellation method for the integrating of the myoelectric control and the ES-based sensory feedback.