Browsing by Author "Li-Wei Ko"

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Development of a real-time wireless embedded brain signal acquisition/processing system and its application on driver's drowsiness estimation
(2006)
Hung-Yi Hsieh
;
Sheng-Fu Liang
;
Li-Wei Ko
;
May Lin
;
Chin-Teng Lin
In this paper, a portable real-time wireless embedded brain signal acquisition/processing system is developed. The proposed system integrates electroencephalogram signal amplifier technique, wireless transmission technique, and embedded real-time system. The development strategy of this system contains three parts: First, the Bluetooth protocol is used as a transmission interface and integrated with the bio-signal amplifier to transmit the measured physiological signals wirelessly. Second, the OMAP (Open Multimedia Architecture Platform) is used as a development platform and an embedded operating system for OMAP is also designed. Finally, DSP Gateway is developed as a mechanism to deal with the brain-signal analyzing tasks shared by ARM and DSP. A driver's cognitive-state estimation program has been developed and implemented on the proposed dual core processor-based real time wireless embedded system for demonstration.
LOWER LIMB REHABILITATION SYSTEM
(2018-08-16)
Chia-Hsin Chen
;
Wei-Zen Chen
;
Yi-Jen Chen
;
Li-Wei Ko
A lower limb rehabilitation system includes an analysis platform, a smart insole, a motion sensor and a brain wave sensor. The analysis platform has a deep learning model. The smart insole generates plural pressure signals through plural pressure sensors of a pressure sensing film. The smart insole includes a processing unit controlling a transmission unit to transmit the pressure signals to the analysis platform. The processing unit is connected to a power supply unit. The motion sensor generates a motion signal. The brain wave sensor is coupled with the analysis platform and detects a brain wave signal. The analysis platform inputs the pressure signals, the motion signal and the brain wave signal into the deep learning model for analyzing a gait. The deep learning model analyzes whether the gait is correct. The analysis platform generates a warning message if the gait is analyzed to be incorrect.