Surface electromyography signal plays an important role in hand function recovery training. In this paper, an IoT-enabled stroke rehabilitation system was introduced which was based on a smart wearable armband (SWA), machine learning (ML) algorithms, and a 3-D printed dexterous robot hand. User comfort is one of the key issues which should be addressed for wearable devices. The SWA was developed by integrating a low-power and tiny-sized IoT sensing device with textile electrodes, which can measure, pre-process, and wirelessly transmit bio-potential signals. By evenly distributing surface electrodes over user's forearm, drawbacks of classification accuracy poor performance can be mitigated. A new method was put forward to find the optimal feature set. ML algorithms were leveraged to analyze and discriminate features of different hand movements, and their performances were appraised by classification complexity estimating algorithms and principal components analysis. According to the verification results, all nine gestures can be successfully identified with an average accuracy up to 96.20%. In addition, a 3-D printed five-finger robot hand was implemented for hand rehabilitation training purpose. Correspondingly, user's hand movement intentions were extracted and converted into a series of commands which were used to drive motors assembled inside the dexterous robot hand. As a result, the dexterous robot hand can mimic the user's gesture in a real-time manner, which shows the proposed system can be used as a training tool to facilitate rehabilitation process for the patients after stroke.

Facial expressions are among behavioral signs of pain that can be employed as an entry point to develop an automatic human pain assessment tool. Such a tool can be an alternative to the self-report method and particularly serve patients who are unable to self-report like patients in the Intensive Care Unit and minors. In this paper, a wearable device with a bio-sensing facial mask is proposed to monitor pain intensity of a patient by utilizing facial surface electromyogram (sEMG). The wearable device works as a wireless sensor node and is integrated into an Internet of Things system for remote pain monitoring. In the sensor node, up to eight channels of sEMG can be each sampled at 1000 Hz, to cover its full frequency range, and transmitted to the cloud server via the gateway in real-time. In addition, both low energy consumption and wearing comfort are considered throughout the wearable device design for long-term monitoring. To remotely illustrate real-time pain data to caregivers, a mobile web application is developed for real-time streaming of high-volume sEMG data, digital signal processing, interpreting, and visualization. The cloud platform in the system act as a bridge between the sensor node and web browser, managing wireless communication between the server and the web application. In summary, this study proposes a scalable IoT system for real-time biopotential monitoring and a wearable solution for automatic pain assessment via facial expressions.

A hyper-redundant manipulator(HRM) can flexibly accomplish tasks in narrow spaces. However, its excessive degrees of freedom pose challenges for path planning. In this letter, an ellipsoid-shape rapidly-exporing random tree (E-RRT*) method is proposed for path planning of HRMs in workspace, particularly those with angle limits. This method replaces line segments with ellipsoids to connect adjacent nodes. Firstly, an analysis of angle constraints of the HRM is conducted, providing restrictions on node selection during path planning. Secondly, a slow-speed informed guiding approach is introduced to optimize the sampling process. Finally, the obtained path is enhanced by adding control points and applying cubic polynomial interpolation to achieve path smoothing. Simulations demonstrate that the proposed E-RRT* method effectively solves the path planning problem for HRMs. Especially in narrow environments, appropriate informed guiding speeds enable E-RRT* to outperform other methods.

Hyper-redundant manipulator has a slim geometric shape and super high degrees of freedom (DOF), which promises high motion efficiency and flexible obstacle avoidance ability in a confined space. Nevertheless, it also challenges the more complicated motion control strategy. In this article, the concepts of “link eigenvectors” and “manipulator's eigenpoints,” which apply to a widely used geometric kinematics method, are proposed. Hence, the kinematic and dynamic models can be constructed intuitively. And since the indirect joint space and local frames are replaced by the link eigenvectors and unified global frame, the kinematic and dynamic problems, especially the link's position, velocity, and acceleration, can be solved more directly. Further, based on the new models, mechanical analysis and Newton–Euler dynamic equations are effectively simplified, so as to realize the fast and precise dynamic resolution. Experiment results show that the kinematic and dynamic theoretical models are correct. And the driving forces of motors are successfully optimized, while the kinematic precision is improved simultaneously.

This paper presents the regulation methods of flow forces acting on the main poppet in a large flow load control valve. The negative flow forces working on the originally designed main poppet, which is as high as 10% of the control force, act as a big disturbance to the main poppet position control, resulting in poor lowering speed control performance of the load control valve or even instability of the main poppet. Firstly, by introducing a damping tail structure to the main poppet, the direction of the flow forces can be regulated to positive and best stability of the main poppet can be achieved with the calculated best parameter combination. Secondly, by introducing a damping tail with holes to the main poppet, the positive flow forces of all main poppet positions can be reduced to minimum, which results in minimum disturbance for the control of main poppet. Computational fluid dynamics calculations were conducted to analyze how the damping tail parameters affect the flow forces and obtain the best parameter combinations. A test rig was set up to validate the effect of above methods. Good agreement of the computational fluid dynamics calculations and experiment results indicates that the proposed methods can provide guidance for the flow force regulation of other valve poppets.