The analysis of electromyographic (EMG) signals is crucial for advancing human-machine interaction. Despite recent progress, most methods still approach gesture intention prediction as a single classification task, which overlooks the complex temporal dynamics and channel-specific variations present in EMG signals. To address these shortcomings, we propose FESNet (Fine-grained Electromyography Segmentation Network), a novel segmentation-based network that temporally segments EMG signals, enabling a more fine-grained analysis of finger movements. Our approach utilizes a robust backbone network for feature extraction, followed by a functional head that adapts to different granularities or task objectives (classification or segmentation). We evaluate our method on the Ninapro DB8 dataset, where FESNet outperforms previous models, demonstrating its superior performance. The source code is publicly available at: https://github.com/Dianli97/FESNet

Efficient processing of multichannel biosignals has significant application values in the fields of healthcare and human-machine interaction. Although previous research has achieved high recognition performance with deep convolutional neural networks, several key challenges still remain: (1) Effective extraction of spatial and temporal features from the multichannel biosignals. (2) Appropriate trade-off between performance and complexity for improving applicability in real-life situations given that traditional machine learning and 2D-based CNN approaches often involve excessive preprocessing steps or model parameters; and (3) Generalization ability of neural networks to compensate for domain difference and to reduce overfitting during training process. To address challenges 1 and 2, we propose a 1D-based deep intra and inter channel (I2C) convolution neural network. The I2C convolutional block is introduced to replace the standard convolutional layer, further extending it to several state-of-the-art modules, with the intent of extracting more effective features from multichannel biosignals with fewer parameters. To address challenge 3, we integrate a branch model into the main model to perform dynamic label smoothing, enabling the model to learn domain difference and improve its generalization ability. Experiments were conducted on three public multichannel biosignals databases, namely ISRUC-S3, HEF and Ninapro-DB1. The results suggest that the proposed method exhibits significant competitive advantages in accuracy, complexity, and generalization ability.

Biological signals classification is essential for human machine interaction. Although previous research has achieved high classification performance, compensating for domain shift due to the intra and inter individual variations remains a challenge. In this paper, we propose a novel dynamic label smoothing strategy, named DLS, to address this issue. The proposed DLS constructs an auxiliary neural network to adjust the true label and to supervise the primary neural network. Experiments on the NinaPro DB1 dataset demonstrate that the proposed DLS outperforms current state-of-the-art methods. Furthermore, the proposed DLS has significant potential for practical applications as it can maintain or even improve the performance of the primary neural network on noisy data. The source code is publicly available at: https://github.com/peijii/DLS

In this study, we propose a design methodology for anthropomimetic robotic wrists and forearms. Conventional robotic wrists and forearms have few examples of human mimicry, and they have been simplified. The ligaments and tendons of the robotic forearm proposed in this study were replicated using chain-stitched wires and arranged in a manner similar to the human anatomy. Motion capture and goniometer measurements were used to measure the range of motion (ROM) of the wrist and forearm, driven by 16 servomotors. The results of the experiment were compared with the human ROM reported in previous studies, and it was found that the developed robotic forearm could achieve a ROM similar to that of humans. The use of this robotic forearm in functional verification experiments will enhance our understanding of the human structure. The integration of mechanical mechanisms with structures created through evolution is expected to improve the functionality of future robots.

The activities of muscles in the forearm have been widely investigated to develop human interfaces involving hand motions, especially in the fields of prosthetic hands and teleoperation. Although surface electromyography (sEMG) is considered as an effective biological signal from which hand motions can be recognized, the availability and quality of sEMG data can limit the usability and intuitiveness of human interfaces. This article introduces force myography (FMG) as a supplementary signal and proposes a layered sEMG–FMG hybrid sensor that can measure both sEMG and FMG at the same skin surface location. Meanwhile, a layer fusion convolution neural network (LFC) is designed to extract multiscale features from sEMG and FMG. To evaluate the effectiveness of the hybrid sEMG–FMG sensor and LFC, a 22-hand motion classification experiment was conducted on nine able-bodied subjects. The recognition results indicated a significantly improved classification accuracy (p < 0.001) of the hybrid sEMG–FMG modality with respect to single sEMG or FMG modality. The classification accuracies (CAs) of LFC were compared with conventional machine learning methods, including support vector machine, random forest classifier, xgboost, and k-nearest neighbor. Compared with the single-modality sEMG, the CAs of the dual-modality sEMG–FMG using conventional methods, and LFC were improved by 21.31% and 16.71%, respectively. These results suggest that the layered sEMG–FMG sensing approach can effectively enhance the performance of human interfaces, which offers great potential in the clinical applications of sophisticated prosthetic hands and teleoperation.

This paper proposes a coupled tendon-driven waist joint for humanoid robots. The waist joint was designed as a 3 degrees of freedom (DOF) structure to simulate the motion of a human waist. The power transmission was designed by adopting a 3-motor 3-DOF (3M3D) coupled tendon-driven mechanism, so that the torque on the joints was multiplied. We derived the torque transmission formula and the rotation angle formula of the 3M3D tendon-driven structures and designed the waist joint by adopting an appropriate structure according to their features. To evaluate the accuracy and load capacity of the waist joint, we performed a rotational accuracy experiment and a maximum torque experiment. The experiment results showed that the maximum error of joint rotation was below 1°, and the maximum torque of the pitch, roll, and yaw rotations were 87[Nm], 53[Nm], and 22.2[Nm], respectively.

This article proposes coupled tendon-driven joint modules for anthropomorphic robots. Fully actuated 2-degree-of-freedom (DoF) and 3-DoF joint modules are classified and analyzed based on the motor-joint routing matrix that describes the tendon routing structure between the motors and the joints. Two-motor 2-DoF (2M2D) modules, which share the same form of motor-joint routing matrix, are classified into four types: externally actuated, internally coaxially actuated, internally separately actuated, and hybrid-actuated according to the location of the motors. Three-motor 3-DoF (3M3D) modules are classified into four forms based on the four possible forms of motor-joint routing matrix: fully routed motor-joint, 1-unrouted motor-joint, 2-unrouted motor-joint, and 3-unrouted motor-joint. The 2M2D and 3M3D coupled tendon-driven joint modules are analyzed and compared with respect to the relationship between the motor torque and the joint torque. A 7-DoF anthropomorphic robot arm was implemented with one 3M3D module for the shoulder joint and two 2M2D modules for the elbow and wrist joints, respectively, to demonstrate the utility of the proposed joint modules. The arm weighed 2.2 kg and was able to lift a 1.5-kg load with full outreach. The characteristics of the joint modules were evaluated in a current consumption experiment and a position accuracy experiment, and the performance of the robot arm was evaluated in a master-slave manipulation experiment involving dexterous movements.

This paper presents a lightweight (<;250 g) and low-cost (<;350 USD) biomimetic prosthetic hand with two actuators embedded in the palm. One of them is employed for flexion/extension of the five digits, and the other one is used for the adduction/abduction of thumb. Thus, the hand can achieve major grasping tasks that account for about 85% of activities in daily life. The unique transmission provides various advantages such as a compact structure, weight saving, and short driven distance. Furthermore, by using 3D printing technology, most parts of the prosthetic hand were made to be much lighter and have a humanlike appearance, compared with conventionally manufactured artificial hand. Finally, the performance and practical applicability of the proposed design was verified experimentally through both of a motion verification and an intuitive control test by a healthy subject and a transradial amputee.

A 2 motor 2 degrees-of-freedom (2M2D) coupled tendon driven joint module is proposed as a basic component for robot arms. Torque reallocation via tendon coupling can enhance the output torque of one single joint. According to the motor position, the joint module is classified into four types: the externally-actuated structure, the internally-coaxially-actuated structure, the internally-separately-actuated structure, and the hybrid-actuated structure. The four structures are analyzed and compared, and their implementation design examples are given. Experiments comparing the proposed joint module with directly-actuated traditional joint suggested that the 2M2D coupled tendon-driven joint module can obtain high control accuracy, and the torque reallocation via tendon coupling is effective to improve output torque. Additionally, an anthropomorphic robot arm with low weight and high payload was developed to show the utility of the proposed joint module.