Pulse Diagnosis Theory (PDT) has the advantages of non-invasive treatment and disease prevention. Combining these merits with Wireless Sensor Network(WSN), we propose a novel networked low-cost and wearable healthcare monitoring system, namely PDhms, for pulse data collection, pulse analysis and pulse diagnosis. Some practical challenges still exist in PDhms such as exerting appropriate pressure on a human radial artery, overcoming seriously limited resources, improving low Signal to Noise Ratio(SNR) and conducting resistance of interference. To address these challenges, we present a robust external pressure control algorithm for pulse data collection, and propose FEA, a novel light-weight and adaptive feature extraction algorithm for sensed pulse data. We conduct the large-scale pulse data collection experiments of 1356 pulse samples, the comparison experiment between the FEA and the typical derivative-based algorithm, as well as pulse diagnosis experiments based on SVM. Experimental results show that PDhms is a valuable solution for low-cost wearable healthcare monitoring system. It will benefit the public, especially low-income groups because small pulse-sensor node size, low system cost, as well as wearable pulse data collection and analysis.

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.

To meet the increasing needs of home-based healthcare, we propose a novel networked low-cost and wearable healthcare monitoring system, namely TCM-PCA, for pulse analysis in Traditional Chinese Medicine(TCM). The unique innovations of the system are as follows: (1) We propose a novel adaptive features extraction algorithm for pulse waveform to improve physiological feature extraction accuracy of sensed pulse waveform data. (2) We propose a light-weight information fusion framework for human symptom identification according to our previous work. (3) We present a robust external pressure control algorithm for pulse measure to obtain stable pulse waveform data on the pulse-sensor nodes. In this demo, we will demonstrate the working procedure of the system and prove the innovations summarized above.

There is a growing need for medical care resources when people pay more and more attention to their health conditions. The hospitals, however, can not satisfy all those ever-increasing requirements. There is hence a contradiction between the needs of the medical care and resource availability. A remote telemedical care system is a possible optimistic way towards solving this problem with the support of emerging new technologies. In this paper, we present the development of a wireless networked Chinese telemedicine system for remote pulse information monitoring, namely WNCTs, based on the principle of Chinese pulse diagnosis in Chinese medicine (CPD). The proposed wearing system may collect the pulse condition, analyze the information by data fusion and provide diagnostic results. The distinctive features of the proposed wireless system include: small in size, light in weight, multiple of type pulse detection and networked users' remote management. In this paper we provide the design and implementation of the wearing wireless networked Chinese telemedicine system. We also demonstrate the use of the system in pulse information retrieval. Our results indicate significant practical feasibility of the networked telemedicine system.