Baseline wander correction in pulse waveforms using wavelet-based cascaded adaptive filter
Computers in Biology and Medicine
Computational methods for Traditional Chinese Medicine: A survey
Computer Methods and Programs in Biomedicine
PERCOM '08 Proceedings of the 2008 Sixth Annual IEEE International Conference on Pervasive Computing and Communications
MEDiSN: medical emergency detection in sensor networks
Proceedings of the 6th ACM conference on Embedded network sensor systems
Wavelet Decomposition and Feature Extraction from Pulse Signals of the Radial Artery
ICACTE '08 Proceedings of the 2008 International Conference on Advanced Computer Theory and Engineering
Sensor selection for energy-efficient ambulatory medical monitoring
Proceedings of the 7th international conference on Mobile systems, applications, and services
Proceedings of the 8th ACM Conference on Embedded Networked Sensor Systems
A wireless PDA-based physiological monitoring system for patient transport
IEEE Transactions on Information Technology in Biomedicine
A mobile teletrauma system using 3G networks
IEEE Transactions on Information Technology in Biomedicine
Accurate cirrhosis identification with wrist-pulse data for mobile healthcare
Proceedings of the Second ACM Workshop on Mobile Systems, Applications, and Services for HealthCare
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Traditional Chinese Pulse Diagnosis is a convenient and noninvasive method for disease diagnosis and healthcare. We have designed and implemented a Chinese wrist-pulse retrieval system based on the principle of Traditional Chinese Pulse Diagnosis (TCPD), called EasiCPRS. It is designed to be small in size, low in cost, with flexibility in deployment, and simplicity in operation. The contributions of this work are: 1. The wrist-pulse at "cun, guan and chi"points over the radial artery are obtained by applying a moderate and adjustable taking pressure during wrist-pulse retrieval. 2. A wrist-pulse signal conditioning circuit and a robust external taking pressure control algorithm are designed to overcome low signal-to-noise ratio (SNR). 3. A lightweight algorithm for wrist-pulse feature extraction is achieved on a resource-constrained platform to economize energy and bandwidth. We developed EasiCPRS prototype, trained and verified the performance of the system by collecting and analyzing thousands of wrist-pulse samples from volunteers in a number of different health conditions such as hypertension, pregnancy and so on which were diagnosed by doctors in hospital. The experimental results showed potential usefulness of the system in disease diagnosis and healthcare.