The Navigation of Mobile Robot in the Indoor Dynamic Unknown Environment Based on Decision Tree Algorithm.

This study proposes an optimized algorithm for the navigation of the mobile robot in the indoor and dynamic unknown environment based on the decision tree algorithm. Firstly, the error of the yaw value outputted from IMU sensor fusion module is analyzed in the indoor environment; then, the adaptive FAST SLAM is proposed to optimize the yaw value from the odometer; in the next, a decision tree algorithm is applied which predicts the correct moving direction of the mobile robot through the outputted yaw value from the IMU sensor fusion module and adaptive FAST SLAM of the odometer data in the indoor and dynamic environment; the following is the navigation algorithm proposed for the mobile robot in the dynamic and unknown environment; finally, a real mobile robot is designed to verify the proposed algorithm.The final result shows the proposed algorithms are valid and effective.

Advanced quantitative estimation methods for spasticity: a literature review.

Post-stroke spasticity seriously affects patients' quality of life. Spasticity is considered to involve both neural and non-neural factors. Current clinical scales, such as the Modified Ashworth Scale and the Modified Tardieu Scale, lack reliability and reproducibility. These scales are also unable to identify the neural and non-neural contributions to spasticity. Surface electromyography and biomechanical and myotonometry measurement methods for post-stroke spasticity are discussed in this report. Surface electromyography can provide neural information, while myotonometry can estimate muscular properties. Both the neural and non-neural contributions can be estimated by biomechanical measurement. These laboratory methods can quantitatively assess spasticity. They can provide more valuable information for further study on treatment and rehabilitation than clinical scales.