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oai:docta.ucm.es:20.500.14352/948622025-03-13T18:31:01Zcom_20.500.14352_14col_20.500.14352_20

Adaptive compressed sensing at the fingertip of Internet-of-Things sensors: An ultra-low power activity recognition Fallahzadeh, Ramin Pagán Ortiz, Josué Ghasemzadeh, Hassan With the proliferation of wearable devices in the Internet-of-Things applications, designing highly power-efficient solutions for continuous operation of these technologies in life-critical settings emerges. We propose a novel ultra-low power framework for adaptive compressed sensing in activity recognition. The proposed design uses a coarse-grained activity recognition module to adaptively tune the compressed sensing module for minimized sensing/transmission costs. We pose an optimization problem to minimize activity-specific sensing rates and introduce a polynomial time approximation algorithm using a novel heuristic dynamic optimization tree. Our evaluations on real-world data shows that the proposed autonomous framework is capable of generating feedback with -80% confidence and improves power reduction performance of the state-of-the-art approach by a factor of two. 2024-01-23T16:19:12Z 2024-01-23T16:19:12Z 2017 conference paper R. Fallahzadeh, J. P. Ortiz and H. Ghasemzadeh, "Adaptive compressed sensing at the fingertip of Internet-of-Things sensors: An ultra-low power activity recognition," Design, Automation & Test in Europe Conference & Exhibition (DATE), 2017, Lausanne, Switzerland, 2017, pp. 996-1001, doi: 10.23919/DATE.2017.7927136. 10.23919/date.2017.7927136 https://hdl.handle.net/20.500.14352/94862 1558-1101 https://www.doi.org/10.23919/date.2017.7927136 https://ieeexplore.ieee.org/document/7927136 eng http://creativecommons.org/licenses/by-nc-nd/4.0/ restricted access Attribution-NonCommercial-NoDerivatives 4.0 International