An Extreme Edge Low Power Device with Wavelet-based Compression for Physiological signals

The combination between artificial intelligence, affective computing, and the internet of things, their use in real-time continuous monitoring applications, and their potential relevance in topics such as e-health or marketing has spotlighted the need of collecting extensive amounts of data constant...

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Bibliographic Details
Main Authors: Gutierrez-Martin, L., Naranjo, M.J. Sanchez, Miranda Calero, J. A., Lopez-Ongil, C.
Format: Conference Proceeding
Language:English
Subjects:
Affective Computing
Discrete wavelet transforms
DWT-based compression techniques
embedded implementation
Filter banks
in-memory
Integrated circuits
Memory management
on-the-edge
power consumption
Power demand
Real-time systems
Wireless communication
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Summary:The combination between artificial intelligence, affective computing, and the internet of things, their use in real-time continuous monitoring applications, and their potential relevance in topics such as e-health or marketing has spotlighted the need of collecting extensive amounts of data constantly. However, at the same time, these wireless body sensor networks should assure system usability and user comfort in terms of battery lifetime. This is where data compression techniques appear. This paper presents a detailed design and embedded implementation of a digital wavelet transform (DWT)-based filter bank within an extreme edge low-power device. Besides, a study on the improvement in power consumption, considering memory capabilities, computational power resources, and data transmission rate, has provided a reduction of up to 60% in the energy required for wireless transmission.
ISSN:2640-5563
DOI:10.1109/DCIS55711.2022.9970154