Development of a Virtual Robot Rehabilitation Training System for Children with Cerebral Palsy: An Observational Study.

This paper presents the development of a robotic system for the rehabilitation and quality of life improvement of children with cerebral palsy (CP). The system consists of four modules and is based on a virtual humanoid robot that is meant to motivate and encourage children in their rehabilitation programs. The efficiency of the developed system was tested on two children with CP.

A Real-Time Analysis Method for Pulse Rate Variability Based on Improved Basic Scale Entropy.

Base scale entropy analysis (BSEA) is a nonlinear method to analyze heart rate variability (HRV) signal. However, the time consumption of BSEA is too long, and it is unknown whether the BSEA is suitable for analyzing pulse rate variability (PRV) signal. Therefore, we proposed a method named sliding window iterative base scale entropy analysis (SWIBSEA) by combining BSEA and sliding window iterative theory.

Improved cumulative probabilities and range accuracy of a pulsed Geiger-mode avalanche photodiode laser ranging system with turbulence effects.

There exists a performance limitation in a pulsed Geiger-mode avalanche photodiode laser ranging system because of the echo intensity random fluctuation caused by turbulence effects. To suppress the influence of turbulence effects, we present a cumulative pulse detection technique with the ability to achieve improved cumulative probabilities and range accuracy. Based on the modulated Poisson model, the cumulative probabilities, range accuracy, and their influencing factors are investigated for a cumulative Q-switched laser pulse train.

Output feedback control for a class of nonlinear systems with actuator degradation and sensor noise.

This paper investigates the output feedback control problem of a class of nonlinear systems with sensor noise and actuator degradation. Firstly, by using the descriptor observer approach, the origin system is transformed into a descriptor system. On the basis of the descriptor system, a novel Proportional Derivative (PD) observer is developed to asymptotically estimate sensor noise and system state simultaneously.