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Understanding Smartwatch Battery Utilization in the Wild.

Morteza Homayounfar1, Amirhossein Malekijoo2, Aku Visuri3

  • 1Department of Biomedical Engineering, Amirkabir University of Technology, Tehran 159163, Iran.

Sensors (Basel, Switzerland)
|July 10, 2020
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Summary
This summary is machine-generated.

Smartwatch battery life is a major concern. This study uses a novel deep learning model with 85.3% accuracy to predict high battery drain, improving smartwatch usability.

Keywords:
batteryconvolutional neural networkdeep learningsmartwatchuser experience

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Area of Science:

  • Wearable Technology
  • Computer Science
  • Data Science

Background:

  • Smartwatch battery limitations hinder consumer adoption.
  • Limited research exists on real-world smartwatch battery usage across diverse devices.
  • Understanding battery consumption patterns is crucial for improving user experience.

Purpose of the Study:

  • To analyze real-world smartwatch battery utilization patterns.
  • To develop an accurate model for predicting high battery discharge events.
  • To introduce a method for assessing smartwatch battery quality degradation over time.

Main Methods:

  • Utilized a dataset from 832 real-world smartwatch users.
  • Employed clustering to identify battery utilization patterns.
  • Developed a transparent convolutional neural network (CNN) model for binary classification (low/high consumption).
  • Introduced a longitudinal indexing method to track battery quality changes.

Main Results:

  • The CNN model achieved 85.3% accuracy in predicting high battery discharge events.
  • The model outperforms existing machine learning algorithms in battery discharge prediction.
  • The model allows for the extraction of information from learned filters, offering unique insights.
  • The indexing method quantifies battery quality changes over time.

Conclusions:

  • The developed deep learning model accurately predicts high battery discharge events.
  • The findings provide actionable insights for improving smartwatch battery performance.
  • This research assists manufacturers, developers, and users in optimizing smartwatch battery life.