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.

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.

In this paper, we focus on how to dynamically allocate a divisible resource fairly among n players who arrive and depart over time. The players may have general heterogeneous valuations over the resource. It is known that the exact envy-free and proportional allocations may not exist in the dynamic setting [Walsh, 2011]. Thus, we will study to what extent we can guarantee the fairness in the dynamic setting. We first design two algorithms which are O(log n)-proportional and O(n)-envy-free for the setting with general valuations, and by constructing the adversary instances such that all dynamic algorithms must be at least Omega(1)-proportional and Omega(n/log n)-envy-free, we show that the bounds are tight up to a logarithmic factor. Moreover, we introduce the setting where the players' valuations are uniform on the resource but with different demands, which generalize the setting of [Friedman et al., 2015]. We prove an O(log n) upper bound and a tight lower bound for this case.

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.