Location
Hilton Hawaiian Village, Honolulu, Hawaii
Event Website
https://hicss.hawaii.edu/
Start Date
3-1-2024 12:00 AM
End Date
6-1-2024 12:00 AM
Description
Human 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.
Recommended Citation
Hoof, Thomas and Buchwitz, Benjamin, "Energy Efficiency and Classification Locality: Pareto-optimal trade-offs in multi-class sensor-based Human Activity Recognition" (2024). Hawaii International Conference on System Sciences 2024 (HICSS-57). 5.
https://aisel.aisnet.org/hicss-57/st/sw_development/5
Energy Efficiency and Classification Locality: Pareto-optimal trade-offs in multi-class sensor-based Human Activity Recognition
Hilton Hawaiian Village, Honolulu, Hawaii
Human 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.
https://aisel.aisnet.org/hicss-57/st/sw_development/5