Abstract
In this thesis, an integrated wearable application platform with physiological sensors are presented. We use the wearable devices to collect the electrocardiography (ECG) respiration (RESP) signals. The first wearable device is a prototype with analog front-end, FPGA controller and Bluetooth module. The other wearable device is using accelerometers and a microphone to collect respiration signals and snore sound. The mobile phone is a platform for dealing with the digital signal processing. We design an Android app with convenient user interface for every application. In order to get more convenient to analyse these biomedical signals, we choose cloud computing system to be our computing platform. We implement the cloud computing by Apache Storm, which can transmit the data by streaming via 3G/Wi-Fi. Depending on the advantages of the cloud computing, we develop an application with the wearable device for the emotion recognition. The extracted-features of biomedical signals are implemented with methods by the cloud computing. The support vector machine (SVM) is a classifier for classifying the data set into the correct classes. The accuracy of the classification is 58.3 %. Besides the detecting emotion recognition system, this wearable device can be applied in various applications. The heart and brain crosstalk system is an application of using ECG and Electroencephalography (EEG) signals to observe the patients’ surgery recovery. The special part is that the mental status of the patients can use colors scale to represent. The home monitoring system for sleep-disordered breathing is an application to monitor the patients who is obstructive sleep apnea (OSA) and uses the accelerometers and microphones to detect the symptoms of disease. We modify the monitor platform from using MATLAB to Android mobile phone. The last application is using the same wearable device to collect RESP signals and then develop an entertaining app game.