Understanding the predictability of smartwatch usage

Yunsheng Yao, Xing Liu, Feng Qian

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

We explore the predictability of smartwatch usage using a 9-month dataset collected from 27 users through a crowd-sourced user trial. Specifically, we investigate the predictability of (1) the device energy consumption, (2) the application launch time, and (3) the screen display. Overall, we find that all three aspects exhibit reasonably good predictability. Our findings provide key knowledge and insights for developing efficient and intelligent energy management services for future smartwatch systems.

Original languageEnglish (US)
Title of host publicationWearSys 2019 - Proceedings of the 5th ACM Workshop on Wearable Systems and Applications, co-located with MobiSys 2019
PublisherAssociation for Computing Machinery, Inc
Pages11-16
Number of pages6
ISBN (Electronic)9781450367752
DOIs
StatePublished - Jun 12 2019
Event5th ACM Workshop on Wearable Systems and Applications, WearSys 2019, co-located with MobiSys 2019 - Seoul, Korea, Republic of
Duration: Jun 21 2019 → …

Publication series

NameWearSys 2019 - Proceedings of the 5th ACM Workshop on Wearable Systems and Applications, co-located with MobiSys 2019

Conference

Conference5th ACM Workshop on Wearable Systems and Applications, WearSys 2019, co-located with MobiSys 2019
CountryKorea, Republic of
CitySeoul
Period6/21/19 → …

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Keywords

  • Energy consumption
  • Smartwatch
  • Usage predictability

Cite this

Yao, Y., Liu, X., & Qian, F. (2019). Understanding the predictability of smartwatch usage. In WearSys 2019 - Proceedings of the 5th ACM Workshop on Wearable Systems and Applications, co-located with MobiSys 2019 (pp. 11-16). (WearSys 2019 - Proceedings of the 5th ACM Workshop on Wearable Systems and Applications, co-located with MobiSys 2019). Association for Computing Machinery, Inc. https://doi.org/10.1145/3325424.3329661