Papers by Author: Xiao Ming Wu

Abstract: Originated from the need in the training of sports universities, a new personal motion-measurement system via piezoelectric sensor is presented to detect step number, stride frequency, and energy consumption in motion. The motion-measurement system in training should be not only accurate in detection, but also convenient for athletes. The novel system consists of detection unit, RF-wireless unit, and display unit. Using a high-sensitive piezoelectric sensor placed within the ball or lateral side of a personal shoe, the accuracy is improved greatly. With RF-wireless, the system achieves the goal in convenience. In the display unit, detected step number, stride frequency, and energy consumption are used to evaluate the quantity of motion, so the athletics can adjust their gait immediately. Compared with existing systems, the personal motion-measurement system via piezoelectric sensor improves greatly in accuracy and convenience.

Abstract: Movement whether it is actual or imaginary can produce different electroencephalogram (EEG) signals. How to extract features of signals and accurately classify them is a key to brain-computer interface(BCI) system. In the paper, BCI competition data downloaded from BCI website are used as study object, through time-domain analysis and frequency-domain analysis, according to the attribute of event-related synchronization (ERS) and event-related desynchronization (ERD) during imagery movement, energy difference of lead C3 and C4 are selected as features and wavelet package is used to extract them. Probabilistic neural networks (PNN) is used as classification method. Compared with other two calssification methods such as support vector method (SVM) and liner classifier, the classification accuracy rate of PNN reaches to 89.2% steadily and is higher than them. It is proved that the method provided in the paper are effective for identifying imaginary movements.