An ultra-low power dual-mode ECG monitor for healthcare and wellness

Daniele Bortolotti, Mauro Mangia, Andrea Bartolini, Riccardo Rovatti, Gianluca Setti, Luca Benini

Research output: Chapter in Book/Report/Conference proceedingConference contribution

31 Scopus citations


Technology scaling enables today the design of ultra-low cost wireless body sensor networks for wearable biomedical monitors. These devices, according to the application domain, show greatly varying tradeoffs in terms of energy consumption, resources utilization and reconstructed biosignal quality. To achieve minimal energy operation and extend battery life, several aspects must be considered, ranging from signal processing to the technological layers of the architecture. The recently proposed Rakeness-based Compressed Sensing (CS) expands the standard CS paradigm deploying the localization of input signal energy to further increase data compression without sensible RSNR degradation. This improvement can be used either to optimize the usage of a non volatile memory (NVM) to store in the device a record of the biosignal or to minimize the energy consumption for the transmission of the entire signal as well as some of its features. We specialize the sensing stage to achieve signal qualities suitable for both Healthcare (HC) and Wellness (WN), according to an external input (e.g. the patient). In this paper we envision a dual-operation wearable ECG monitor, considering a multi-core DSP for input biosignal compression and different technologies for either transmission or local storage. The experimental results show the effectiveness of the Rakeness approach (up to ≈ 70% more energy efficient than the baseline) and evaluate the energy gains considering different use case scenarios.
Original languageEnglish (US)
Title of host publicationProceedings -Design, Automation and Test in Europe, DATE
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages6
ISBN (Print)9783981537048
StatePublished - Apr 22 2015
Externally publishedYes


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