120 likes | 214 Views
Context-aware Sensing of Physiological Signals. Winston H. Wu, Maxim A. Batalin, Lawrence K. Au, Alex A. T. Bui, and William J. Kaiser. Introduction. Purpose-Low power consuming physiological sensors implementation
E N D
Context-aware Sensing of Physiological Signals Winston H. Wu, Maxim A. Batalin, Lawrence K. Au, Alex A. T. Bui, and William J. Kaiser
Introduction Purpose-Low power consuming physiological sensors implementation Energy use decreased by enabling & disabling the sensors to real time measurement demand Use low cost sensors to schedule high cost sensors like ECG sensors
Hardware Used • Commercially Available PDA with Wifi capabilities • Bluetooth modules • 3 Sensors • ECG sensor • Pulse Oximeter • 3 Axis Accelerometer-2 sets
Software Inference Engine GUI Local Data Logger Device Server Device Driver
Motion detection Pulse oximeter used to detect start of the exercise 2 Accelerometers used to detect end of the exercise 1 on right ankle and 1 on left hip Inference engine on the wearable system computes when to activate ECG sensor Data collected is streamed to a central server via Wifi Network
Communication Via Bluetooth Each data point accompanied by tracking sequence number to check for errors PDA is the master node over bluetooth network
Context Aware Sensing • Feature Extraction • Pulse rate and SpO2 value-rate of decline of oxygen saturation • Accelerometer • Since cyclical movements are involved • Features from spectral domain are used • In general case features from time domain may be used • 512 data points window-100 points entered every second • 2 spectral feature values extracted from each axis -f peak and f energy
Naïve Bayes Classifier P(C/F) Where C is the patient states of interest F is the feature vector Pulse classification as Low , Medium, High When high Accelerometer activated Accelerometer classifies as Rest , Walk, Jog, Run If Jog or Run ECG sensor not activated Else it is activated