Fabric liquid moisture transport properties in multidimensions, called moisture management properties, significantly influence human perceptions of moisture sensations. A new method and instrument called the moisture management tester (MMT) is developed to evaluate textile moisture management properties. This new method can be used to quantitatively measure liquid moisture transfer in one step in a fabric in multidirections, where liquid moisture spreads on both surfaces of the fabric and transfers from one surface to the opposite. Ten indexes are introduced to characterize the liquid moisture management properties of fabrics. Eight sets of sportswear are tested with the MMT and the results show that liquid moisture management properties are significantly different for these fabrics. The objective measurements are compared with subjective perceptions of moisture sensations during exercise. A fabric’s one-way-transport capacity and its overall moisture management capacity are significantly correlated with perceptions of clammy and damp sensations with increased exercise time, indicating that subjective perceptions of moisture sensations in sweating such as clammy and damp can be predicted by the measurements of the MMT.

Autonomous exploration is an important application of multi-vehicle systems, where a team of networked robots are coordinated to explore an unknown environment collaboratively. This technique has earned significant research interest due to its usefulness in search and rescue, fault detection and monitoring, localization and mapping, etc. In this paper, a novel cooperative exploration strategy is proposed for multiple mobile robots, which reduces the overall task completion time and energy costs compared to conventional methods. To efficiently navigate the networked robots during the collaborative tasks, a hierarchical control architecture is designed which contains a high-level decision making layer and a low-level target tracking layer. The proposed cooperative exploration approach is developed using dynamic Voronoi partitions, which minimizes duplicated exploration areas by assigning different target locations to individual robots. To deal with sudden obstacles in the unknown environment, an integrated deep reinforcement learning based collision avoidance algorithm is then proposed, which enables the control policy to learn from human demonstration data and thus improve the learning speed and performance. Finally, simulation and experimental results are provided to demonstrate the effectiveness of the proposed scheme.

Automatic cruise control of a platoon of multiple connected vehicles in an automated highway system has drawn significant attention of the control practitioners over the past two decades due to its ability to reduce traffic congestion problems, improve traffic throughput and enhance safety of highway traffic. This paper proposes a two-layer distributed control scheme to maintain the string stability of a heterogeneous and connected vehicle platoon moving in one dimension with constant spacing policy assuming constant velocity of the lead vehicle. A feedback linearization tool is applied first to transform the nonlinear vehicle dynamics into a linear heterogeneous state-space model and then a distributed adaptive control protocol has been designed to keep equal inter-vehicular spacing between any consecutive vehicles while maintaining a desired longitudinal velocity of the entire platoon. The proposed scheme utilizes only the neighbouring state information (i.e. relative distance, velocity and acceleration) and the leader is not required to communicate with each and every one of the following vehicles directly since the interaction topology of the vehicle platoon is designed to have a spanning tree rooted at the leader. Simulation results demonstrated the effectiveness of the proposed platoon control scheme. Moreover, the practical feasibility of the scheme was validated by hardware experiments with real robots.

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This article presents a novel electromyography (EMG)-driven exoneuromusculoskeleton that integrates the neuromuscular electrical stimulation (NMES), soft pneumatic muscle, and exoskeleton techniques, for self-help upper limb training after stroke. The developed system can assist the elbow, wrist, and fingers to perform sequential arm reaching and withdrawing tasks under voluntary effort control through EMG, with a lightweight, compact, and low-power requirement design. The pressure/torque transmission properties of the designed musculoskeletons were quantified, and the assistive capability of the developed system was evaluated on patients with chronic stroke ( n = 10). The designed musculoskeletons exerted sufficient mechanical torque to support joint extension for stroke survivors. Compared with the limb performance when no assistance was provided, the limb performance (measured as the range of motion in joint extension) significantly improved when mechanical torque and NMES were provided ( p < 0.05). A pilot trial was conducted on patients with chronic stroke ( n = 15) to investigate the feasibility of using the developed system in self-help training and the rehabilitation effects of the system. All the participants completed the self-help device-assisted training with minimal professional assistance. After a 20-session training, significant improvements were noted in the voluntary motor function and release of muscle spasticity at the elbow, wrist, and fingers, as indicated by the clinical scores ( p < 0.05). The EMG parameters ( p < 0.05) indicated that the muscular coordination of the entire upper limb improved significantly after training. The results suggested that the developed system can effectively support self-help upper limb rehabilitation after stroke. Register Number NCT03752775.

BackgroundIt is a challenge to reduce the muscular discoordination in the paretic upper limb after stroke in the traditional rehabilitation programs.MethodIn this study, a neuromuscular electrical stimulation (NMES) and robot hybrid system was developed for multi-joint coordinated upper limb physical training. The system could assist the elbow, wrist and fingers to conduct arm reaching out, hand opening/grasping and arm withdrawing by tracking an indicative moving cursor on the screen of a computer, with the support from the joint motors and electrical stimulations on target muscles, under the voluntary intention control by electromyography (EMG). Subjects with chronic stroke (n = 11) were recruited for the investigation on the assistive capability of the NMES-robot and the evaluation of the rehabilitation effectiveness through a 20-session device assisted upper limb training.ResultsIn the evaluation, the movement accuracy measured by the root mean squared error (RMSE) during the tracking was significantly improved with the support from both the robot and NMES, in comparison with those without the assistance from the system (P < 0.05). The intra-joint and inter-joint muscular co-contractions measured by EMG were significantly released when the NMES was applied to the agonist muscles in the different phases of the limb motion (P < 0.05). After the physical training, significant improvements (P < 0.05) were captured by the clinical scores, i.e., Modified Ashworth Score (MAS, the elbow and the wrist), Fugl-Meyer Assessment (FMA), Action Research Arm Test (ARAT), and Wolf Motor Function Test (WMFT).ConclusionsThe EMG-driven NMES-robotic system could improve the muscular coordination at the elbow, wrist and fingers.Trial registrationClinicalTrials.gov. NCT02117089; date of registration: April 10, 2014