An Ultra-Low Resource Wearable EDA Sensor Using Wavelet Compression
[pope:eda-bsn]Gunnar C. Pope, Varun Mishra, Stephanie Lewia, Byron Lowens, David Kotz, Sarah Lord, and Ryan Halter. An Ultra-Low Resource Wearable EDA Sensor Using Wavelet Compression. Proceedings of the IEEE Conference on Body Sensor Networks (BSN), pages 193–196. IEEE, March 2018. doi:10.1109/BSN.2018.8329691. ©Copyright IEEE.
This study presents an ultra-low resource platform for physiological sensing that uses on-chip wavelet compression to enable long-term recording of electrodermal activity (EDA) within a 64kB microcontroller. The design is implemented on a wearable platform and provides improvements in size and power compared to existing wearable technologies and was used in a lab setting to monitor EDA of 27 participants throughout a stress induction protocol. We demonstrate the device’s sensitivity to stress induction by providing descriptive statistics of 8 common EDA signal features for each stressor of the experiment. To the best of our knowledge, this is the first time a generic, 16-bit microcontroller (MCU) has been used to record real-time physiological signals on a wearable platform without the use of external memory chips or wireless transmission for extended periods of time. The compression techniques described can lead to reductions in size, power, and cost of wearable biosensors with little or no modifications to existing sensor hardware and could be valuable for applications interested in monitoring long-term physiological trends at lower data rates and memory requirements.
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Keywords: [mhealth] [sensors] [wearable]
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