Energy Efficiency and Classification Locality: Pareto-optimal trade-offs in multi-class sensor-based Human Activity Recognition

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dc.contributor.authorHoof, Thomas
dc.contributor.authorBuchwitz, Benjamin
dc.date.accessioned2023-12-26T18:55:26Z
dc.date.available2023-12-26T18:55:26Z
dc.date.issued2024-01-03
dc.identifier.doihttps://doi.org/10.24251/HICSS.2024.927
dc.identifier.isbn978-0-9981331-7-1
dc.identifier.otherb59ff827-eb30-4a92-b215-d9455c300aaa
dc.identifier.urihttps://hdl.handle.net/10125/107316
dc.language.isoeng
dc.relation.ispartofProceedings of the 57th Hawaii International Conference on System Sciences
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subjectSoftware Technology and Software Development
dc.subjectmachine learning
dc.subjectneural networks
dc.subjectpareto front
dc.subjectultra low power devices
dc.titleEnergy Efficiency and Classification Locality: Pareto-optimal trade-offs in multi-class sensor-based Human Activity Recognition
dc.typeConference Paper
dc.type.dcmiText
dcterms.abstractHuman Activity Recognition (HAR) is one of the central analytical workloads on wearable devices and effective solutions often require methods from machine learning or artificial intelligence to perform activity classification. Contrary to the hardware in wearable devices, these algorithms have mainly been developed without strict constraints on processing power, memory footprint, or storage space. Wearable applications, therefore, need to simultaneously balance at least the energy consumption of the device and the predictive performance of the algorithms. A plethora of software development kits and frameworks aim to bring those algorithms to smaller ultra-low power Systems-on-Chip (SoCs) and promise efficient execution of analytical workloads. In this study, we provide a holistic view of hardware, algorithms, and software that is useful to build smart wearable devices and provide guidance to researchers and practitioners for the selection of algorithms, configurations, and toolsets that, in combination, provide a Pareto-optimal trade-off between energy consumption and classification performance.
dcterms.extent10 pages
prism.startingpage7730

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