University of Oulu

Homayounfar, M.; Malekijoo, A.; Visuri, A.; Dobbins, C.; Peltonen, E.; Pinsky, E.; Teymourian, K.; Rawassizadeh, R. Understanding Smartwatch Battery Utilization in the Wild. Sensors 2020, 20, 3784, https://doi.org/10.3390/s20133784

Understanding smartwatch battery utilization in the wild

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Author: Homayounfar, Morteza1; Malekijoo, Amirhossein2; Visuri, Aku3;
Organizations: 1Department of Biomedical Engineering, Amirkabir University of Technology, Tehran 159163, Iran
2Electrical and Computer Engineering Department, Semnan University, Semnan 35131, Iran
3Center for Ubiquitous Computing, University of Oulu, 4500 Oulu, Finland
4School of Information Technology and Electrical Engineering, The University of Queensland, Brisbane QLD 4072, Australia
5Department of Computer Science, Metropolitan College, Boston University, Boston, MA 02215, USA
Format: article
Version: published version
Access: open
Online Access: PDF Full Text (PDF, 4.1 MB)
Persistent link: http://urn.fi/urn:nbn:fi-fe2020080648151
Language: English
Published: Multidisciplinary Digital Publishing Institute, 2020
Publish Date: 2020-08-06
Description:

Abstract

Smartwatch battery limitations are one of the biggest hurdles to their acceptability in the consumer market. To our knowledge, despite promising studies analyzing smartwatch battery data, there has been little research that has analyzed the battery usage of a diverse set of smartwatches in a real-world setting. To address this challenge, this paper utilizes a smartwatch dataset collected from 832 real-world users, including different smartwatch brands and geographic locations. First, we employ clustering to identify common patterns of smartwatch battery utilization; second, we introduce a transparent low-parameter convolutional neural network model, which allows us to identify the latent patterns of smartwatch battery utilization. Our model converts the battery consumption rate into a binary classification problem; i.e., low and high consumption. Our model has 85.3% accuracy in predicting high battery discharge events, outperforming other machine learning algorithms that have been used in state-of-the-art research. Besides this, it can be used to extract information from filters of our deep learning model, based on learned filters of the feature extractor, which is impossible for other models. Third, we introduce an indexing method that includes a longitudinal study to quantify smartwatch battery quality changes over time. Our novel findings can assist device manufacturers, vendors and application developers, as well as end-users, to improve smartwatch battery utilization.

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Series: Sensors
ISSN: 1424-8220
ISSN-E: 1424-8220
ISSN-L: 1424-8220
Volume: 20
Issue: 13
Article number: 3784
DOI: 10.3390/s20133784
OADOI: https://oadoi.org/10.3390/s20133784
Type of Publication: A1 Journal article – refereed
Field of Science: 113 Computer and information sciences
Subjects:
Copyright information: © 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
  https://creativecommons.org/licenses/by/4.0/