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Context-Aware Physiological Sensing for Low-Power Implementation

This project focuses on implementing low-power consuming physiological sensors by enabling and disabling them based on real-time measurement demand. By utilizing low-cost sensors to schedule high-cost sensors like ECG sensors, energy use is decreased efficiently. The hardware setup includes a commercially available PDA with Wifi capabilities, Bluetooth modules, and sensors such as ECG sensor, Pulse Oximeter, and 3-axis Accelerometer. The software architecture comprises an Inference Engine, GUI, Local Data Logger, Device Server, and Device Driver. Motion detection and the Pulse Oximeter are used to detect the start and end of exercise, while accelerometers help in determining exercise intensity levels. The project involves context-aware sensing, feature extraction, and an algorithm using a Naïve Bayes Classifier for signal classification. Data collected is streamed to a central server via Wifi network, promoting real-time monitoring. The entities involved communicate via Bluetooth, with each data point accompanied by a tracking sequence number to ensure error-free transmission. The project results in efficient and accurate physiological signal monitoring.

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Context-Aware Physiological Sensing for Low-Power Implementation

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Presentation Transcript

  1. Context-aware Sensing of Physiological Signals Winston H. Wu, Maxim A. Batalin, Lawrence K. Au, Alex A. T. Bui, and William J. Kaiser

  2. 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

  3. Hardware Used • Commercially Available PDA with Wifi capabilities • Bluetooth modules • 3 Sensors • ECG sensor • Pulse Oximeter • 3 Axis Accelerometer-2 sets

  4. Software Inference Engine GUI Local Data Logger Device Server Device Driver

  5. Software Arcitecture

  6. 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

  7. Communication Via Bluetooth Each data point accompanied by tracking sequence number to check for errors PDA is the master node over bluetooth network

  8. 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

  9. Context Aware Sensing Algorithm of an ECG signal

  10. 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

  11. Results

  12. THANK YOU

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