A4 Vertaisarvioitu artikkeli konferenssijulkaisussa
EA2: Energy Efficient Adaptive Active Learning for Smart Wearables
Tekijät: Alikhani, Hamidreza; Wang, Ziyu; Kanduri, Anil; Liljeberg, Pasi; Rahmani, Amir M.; Dutt, Nikil
Toimittaja: Meinerzhagen, Pascal; Dev, Kapil; Yoo, Jerald
Konferenssin vakiintunut nimi: International Symposium on Low Power Electronics and Design
Kustantaja: ASSOC COMPUTING MACHINERY
Kustannuspaikka: NEW YORK
Julkaisuvuosi: 2024
Kokoomateoksen nimi: ISLPED '24: Proceedings of the 29th ACM/IEEE International Symposium on Low Power Electronics and Design
Sivujen määrä: 6
ISBN: 979-8-4007-0688-2
DOI: https://doi.org/10.1145/3665314.3670840
Verkko-osoite: https://doi.org/10.1145/3665314.3670840
Rinnakkaistallenteen osoite: https://research.utu.fi/converis/portal/detail/Publication/459155506
Mobile Health (mHealth) applications rely on supervised Machine Learning (ML) algorithms, requiring end-user-labeled data for the training phase. The gold standard for obtaining such labeled data is by sending queries to users and gathering responses for the corresponding label, which was conventionally done through triggering questions sent at random. Active Learning (AL) methods use intelligent query-sending policies by incorporating users' contextual information to maximize the response rate and informativeness of the collected labeled data. However, wearable devices' substantial battery drainage associated with the sensing of physiological signals underscores the need for developing an efficient sensing policy in addition to a query-sending policy. In this work, we present a co-optimization framework for both sensing and querying strategies within wearable devices, leveraging contextual information and ML model's prediction confidence. We designed a Reinforcement Learning (RL) agent to quantify different contextual parameters combined with model confidence to determine sensing and querying decisions. Our evaluation of an exemplar stress monitoring application showed a 76% reduction in sensing and data transmission energy consumption, with only a 6% drop in user-labeled data.
Ladattava julkaisu This is an electronic reprint of the original article. |  |
Julkaisussa olevat rahoitustiedot:
This work was partially supported by NSF Smart and Connected Communities (S&CC) grant CNS-1831918, Nokia Foundation and Kaute Saatio, Finland.
