Browsing by Author "Chen, Li-Chung"

Now showing 1 - 2 of 2

A Real-Time Maximum-Likelihood Heart-Rate Estimator for Wearable Textile Sensors
(2008)
Cheng, Mu-Huo
;
Chen, Li-Chung
;
Hung, Ying-Che
;
Yang, Chang Ming
;
電控工程研究所
This paper presents a real-time maximum-likelihood heart-rate estimator for ECG data measured via wearable textile sensors. The ECG signals measured from wearable dry electrodes are notorious for its susceptibility to interference from the respiration or the motion of wearing person such that the signal quality may degrade dramatically. To overcome these obstacles, in the proposed heart-rate estimator we first employ the subspace approach to remove the wandering baseline, then use a simple nonlinear absolute operation to reduce the high-frequency noise contamination, and finally apply the maximum likelihood estimation technique for estimating the interval or R-R peaks. A parameter derived from the byproduct of maximum likelihood estimation is also proposed as an indicator for signal quality. To achieve the goal of real-time, we develop a simple adaptive algorithm from the numerical power method to realize the subspace filter and apply the fast-Fourier transform (FFT) technique for realization of the correlation technique such that the whole estimator can be implemented in an FPGA system. Experiments are performed to demonstrate the viability of the proposed system.
A Vital Wearing System with Wireless Capability
(2008)
Cheng, Mu-Huo
;
Chen, Li-Chung
;
Hung, Ying-Che
;
Chen, Chien-Nan
;
Yang, Chang Ming
A system with wearable sensors for detecting and sensing vital and physical motion signals such as the ECG, respiration, temperature, sweatiness, three-axis accelerations, and the motions of both knee ankles and elbows is presented. The system is also equipped with a bluetooth transceiver such that the obtained sensor signals can be transmitted to mobile phones or remote computers for either monitoring or post signal processing to extract knowledges or information. The presented system is embedded with signal processing algorithms such as the heart rate, the respiratory period, the body temperature, the degree of sweatiness, and the posture of wearing person are demonstrated. The heart rate and heart rate variation from the measured ECG data are estimated using an adaptive ESPRIT algorithm. This system is highly suitable for applications of remote healthcare and wellness.