Home Healthcare Technologies for Active Ageing

• Main Authors: Chao, Yen-Yang, 趙彥揚
• Other Authors: Hsi-Pin Ma
• Format: Others
• Language: en_US
• Published: 2013
• Online Access: http://ndltd.ncl.edu.tw/handle/37398982038487036177

碩士 === 國立清華大學 === 電機工程學系 === 102 === Healthcare system is an emerging topic in recent years. It can save time for patients, improve patients life, and reduce the abuse cost for medical treatment. The goal of this research is to develop a real-time portable Electrocardiography (ECG) monitoring device, and fall detection that is capable of discriminating between Activities of Daily Life (ADL) and falls. There are two main parts which discussed in this thesis. One is ECG real-time monitoring. Another fall detection is using fuzzy logic. The system contains wireless sensor node that capturing the bio-signal of the body and a mobile hub that wireless sensor node can send information to the mobile hub. The wireless sensor node consists an analog front-end amplifier, an MCU that control the inside analog digital converter (ADC), and a bluetooth module with 4.0 Bluetooth low energy (BLE) version. We even use Android platform as a hub to transfer data to remote server. In fall detection part, we use 3-axis accelerometer to develop an effective fall detection algorithm based on the characteristics of falls. To increase the error tolerance and increase accuracy rate , we propose a behavior estimation method which consists of the change of acceleration (COA) and fuzzy rule based system to estimate the subject’s behavior. Then, we use MATLAB Fuzzy Logic Toolbox to simulate and estimate the behavior. Results show that falls can be distinguished from ADL with a sensitivity over 95% and a specificity of 97.5%, for a total set of 160 movements.