About: Understanding Smartwatch Battery Utilization in the Wild

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An Entity of Type : schema:ScholarlyArticle, within Data Space : wasabi.inria.fr associated with source document(s)

Attributes	Values
type	Academic Article research paper schema:ScholarlyArticle
isDefinedBy	Covid-on-the-Web dataset
title	Understanding Smartwatch Battery Utilization in the Wild
Creator	Dobbins, Chelsea Homayounfar, Morteza Malekijoo, Amirhossein Peltonen, Ella Pinsky, Eugene Rawassizadeh, Reza Teymourian, Kia Visuri, Aku
source	PMC
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.
has issue date	2020-07-06(xsd:dateTime)
bibo:doi	10.3390/s20133784
bibo:pmid	32640587
has license	cc-by
sha1sum (hex)	f9dd7589df429bbac9e31b48221d45cea28eac57
schema:url	https://doi.org/10.3390/s20133784
resource representing a document's title	Understanding Smartwatch Battery Utilization in the Wild
has PubMed Central identifier	PMC7374306
has PubMed identifier	32640587
schema:publication	Sensors (Basel)
resource representing a document's body	covid:f9dd7589df429bbac9e31b48221d45cea28eac57#body_text
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