ODSearch: Fast and Resource Efficient On-device Natural Language Search for Fitness Trackers' Data

Generative AI (Text, Image, Music, Video)Fitness Tracking & Physical Activity MonitoringContext-Aware ComputingSoftware Engineers & DevelopersAthletes & Fitness Enthusiasts

Mobile and wearable technologies have promised significant changes to the healthcare industry. Although cutting-edge communication and cloud-based technologies have allowed for these upgrades, their implementation and popularization in low-income countries have been challenging. We propose ODSearch, an On-device Search framework equipped with a natural language interface for mobile and wearable devices. To implement search, ODSearch employs compression and Bloom filter, it provides near real-time search query responses without network dependency. In particular, the Bloom filter reduces the temporal scope of the search and compression reduces the size of the data to be searched. Our experiments were conducted on a mobile phone and smartwatch. We compared ODSearch with current state-of-the-art search mechanisms, and it outperformed them on average by 53 times in execution time, 26 times in energy usage, and 2.3% in memory utilization. https://dl.acm.org/doi/10.1145/3569488

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https://hci.top/en/papers/ubicomp/128279/2023

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UbiComp
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Year
2023
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2 authors
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Subtopics
Generative AI (Text, Image, Music, Video), Fitness Tracking & Physical Activity Monitoring, Context-Aware Computing
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Software Engineers & Developers, Athletes & Fitness Enthusiasts
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Abstract only
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