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.
Highlights
Wearable devices are becoming increasingly popular, with smartwatches representing one of the most popular consumer devices on the market
RQ3: “What are the drainage symptoms of a smartwatch battery?” We propose a low-parameter fully convolutional neural network (FCNN) model that can be used to identify the correlation among different sensors and their impact on battery usage, using a binary classification of low and high slopes of battery usage
Since the goal of our model is to examine the impact that smartwatch sensors have on the battery discharge rate, an optimal threshold is required to distinguish between the two classes
Summary
Wearable devices are becoming increasingly popular, with smartwatches representing one of the most popular consumer devices on the market. Since 2016, smartwatches have held the third largest market share of wearable devices. This growth rate suggests that the smartwatch market is going to be bigger than other wearable devices and has the potential to grow to the second biggest market by 2022, with an estimated 109.2 million units being shipped worldwide by 2023 [1] (it is important to note that this statistic was produced before the Covid-19 outbreak; since the outbreak, the market interest in personal health monitoring devices, such as smartwatches, has significantly increased). Two well-known smartwatch constraints are the small screen size and limited battery capacity [3]. The small screen makes interaction with smartwatches less attractive compared to other ubiquitous devices, such as smartphones [4]
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
