Publications

Stroke survivors often suffer from severe discomfort caused by persistent hand edema, including hand pain, restricted knuckle mobility, and negative emotions, which significantly hinders normal rehabilitation progress. Whether physical or psychological in impact, the prevention and treatment of hand edema warrant significant attention throughout all stages of stroke rehabilitation. However, traditional rehabilitation therapies generally neglect edema management, focusing primarily on motor function recovery. Consequently, this study introduces the TESSR (ThErmal Stimulation device for Stroke Rehabilitation), a novel apparatus designed to promote hand rehabilitation for stroke survivors. TESSR is equipped with a flexible stimulation array comprising 50 independently controlled stimulation units, each capable of rapid heating and cooling transitions as well as delivering sustained and stable thermal outputs. This alternating thermal stimulation (TS) is designed to alleviate edema and activate the sensory cortex, thereby facilitating neuroplasticity. A controlled clinical study was conducted with 12 stroke survivors to assess the effectiveness of the TESSR device. The participants were divided into two groups: one receiving conventional rehabilitation (CR group) and the other receiving supplementary TS conducted by the TESSR device (TS group). Following a two-week intervention, the TS group exhibited a notable reduction in hand edema, with a mean decrease of 38.6%, compared to the minimal improvement observed in the CR group. Furthermore, participants in the TS group demonstrated improved hand function, including enhanced sensory perception and diminished pain. The findings demonstrate the potential of TESSR as a comprehensive and effective tool for hand rehabilitation.

This study investigates the trajectory tracking strategy of a snake robot with sideslip disturbance and unknown model parameters. To guide the robot to track the ideal trajectory faster and more accurately, an adaptive anti-sideslip strategy for a snake robot with the Integral Line-of-Sight (ILOS) function is reported. This technique eliminates direction sideslip and error fluctuation by using auxiliary integral terms and shortens the convergence time of state variables. Following the position and angle control objectives, the proposed controller considers the negative effects caused by the uncertainty and time variability of environmental parameters and compensates for the joint input using the adaptive update laws. The environment adaptability and tracking efficiency are improved. The stability analysis indicates that the state errors converge to the origin. The simulation and experiment data verifies the effectiveness and strength of the work. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —This article was motivated by the problem of robust trajectory tracking for a snake robot in an environment with sideslip disturbance and unknown model parameters. In this environment, information of the motion space (for example, the coefficient of ground friction) cannot be obtained. In addition, there may be other system limitations (for example, motion sideslip limitations) and other operational limitations. These limitations are caused by the requirements of various common trajectory tracking objectives. These cases should also be considered in the control strategy. However, based on the existing methods of tracking control for snake robots, there is still a lack of a complete and reliable autonomous control scheme that can consider the above problems. On this basis, we present a reliable control strategy, which considers the above problems and the dynamic uncertainty of the model. In the future, we will extend the proposed method to the field of formation tracking control for multiple robots.

The cooperation of a pair of robot manipulators is required to manipulate a target object without any fixtures. The conventional control methods coordinate the end-effector pose of each manipulator with that of the other using their kinematics and joint coordinate measurements. Yet, the manipulators' inaccurate kinematics and joint coordinate measurements can cause significant pose synchronization errors in practice. This paper thus proposes an image-based visual servoing approach for enhancing the cooperation of a dual-arm manipulation system. On top of the classical control, the visual servoing controller lets each manipulator use its carried camera to measure the image features of the other's marker and adapt its end-effector pose with the counterpart on the move. Because visual measurements are robust to kinematic errors, the proposed control can reduce the end-effector pose synchronization errors and the fluctuations of the interaction forces of the pair of manipulators on the move. Theoretical analyses have rigorously proven the stability of the closed-loop system. Comparative experiments on real robots have substantiated the effectiveness of the proposed control.

Activity recognition plays a critical role in various applications, such as medical monitoring and rehabilitation. Deep learning has recently made great development in the wearable-based human activity recognition (HAR) area. However, real HAR applications should be adaptive and flexible to the available computational budget. So far, this problem has rarely been explored. In contrast to existing deep HAR studies focusing on static networks, this article aims to investigate adaptive networks, which can adjust their structure conditioned on available computing resources to trade off between accuracy and speed. We, for the first time, present an adaptive convolutional neural network by dynamically modifying network width. Specifically, first, instead of a normal convolution, the network is stacked by lower triangular convolutional layers in order to remove the impact of activation statistics caused by varying widths. Second, instead of fixed sampling, we perform random sampling over width, which can provide smooth control for the tradeoff between accuracy and speed. As a consequence, the networks with different widths are simultaneously trained as subnetworks by accumulating their losses during each iteration. On multiple HAR datasets, such as UCI-HAR, PAMAP2, and OPPORTUNITY, extensive experiments verify that the proposed approach can consistently provide further improved efficiency on top of state-of-the-art CNNs for HAR. Finally, evaluations are conducted on a Raspberry Pi platform to demonstrate its usefulness and practicality.