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

arXiv: https://arxiv.org/abs/2201.13202 · HTML: https://mlsanity.com/paper/2201.13202/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 31 Jan 2022.

## Built on by (most cited first, 5 of 5)

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- [LightDepth: A Resource Efficient Depth Estimation Approach for Dealing with Ground Truth Sparsity via Curriculum Learning](https://mlsanity.com/paper/2211.08608/) (2022)
- [Analyzing the Resource Utilization of Lambda Functions on Mobile Devices: Case Studies on Kotlin and Swift](https://mlsanity.com/paper/2502.07809/) (2025)
- [From Clicks to Conversations: Evaluating the Effectiveness of Conversational Agents in Statistical Analysis](https://mlsanity.com/paper/2502.08114/) (2025)
- [Speeding Up Question Answering Task of Language Models via Inverted Index](https://mlsanity.com/paper/2210.13578/) (2022)

## Abstract (arXiv)

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.