Abstract
Mobile health promotes the development of the wearable devices. As a result, in this thesis we have proposed a wearable device which is composed of a micro-controller unit (MCU), a Bluetooth Low Energy (BLE) and two type sensors. These sensors in our wearable device can detect single-lead electrocardiography (ECG), impedance pneumography (IPG) and the 9-axis signals. In the firmware design, we have provided three kinds of operating modes in the device: all mode, ECG mode and R-R interval mode, and we have used the low power design in each operating mode. Generally, all mode transmits ECG, IPG and accelerometer data; ECG mode transmits ECG data only and R-R interval mode transmits R-R interval data only. To reduce the power consumption, we have switched the BLE between the active mode and sleep mode. We also have implemented the algorithm of R-R interval detection in the device to reduce the data amount and the accuracy has achieved 99%. Therefore, the BLE can transmit the data faster and stay in sleep mode longer. Without the low power design, the average current of BLE is 9.82 mA and the total average current is 20.17 mA. On the other hand, with the low power design, the power consumption of R-R interval mode is the minimum version, the average current of BLE is only 0.09 mA and the total average current is 4.47 mA. Besides, in all mode, the total average power is 29 mW. In ECG mode, the total average power is 24.68 mW. In R-R interval mode, the total average power is 14.75 mW. As the result, it reduces from 66.56 mW to 14.75 mW. That means it saves the maximum 77.83% power consumption, hence, the operating time of device extend to 66.98 hours continuously with a 300 mAh battery. Apart from the low power design, considering to people are paying more and more attention to their health, we have done an applications with our device. For example, nowadays the obstructive sleep apnea (OSA) causes the human breath pausing during sleep time. As we know the moment OSA breaks out is related to sleep posture, hence, we have used the 3-axis accelerometer to measure breath and have proposed an algorithm of sleep posture detection.