ODSearch: Fast and Resource Efficient On-device Natural Language Search for Fitness Trackers' Data
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
Research Questions / Practical Problems
Question signals indexed for this paper.
Research Questions
3- How can fast, energy-efficient natural language data retrieval be implemented on resource-constrained wearables such as smartwatches?Category: Personal Multimodal Memory RetrievalSimilar questionsarrow_forward
- Can combining Bloom filters and Huffman coding improve the efficiency and performance of local health data retrieval?Category: Personal Multimodal Memory RetrievalSimilar questionsarrow_forward
- What specific improvements does the ODSearch framework offer in execution time, energy consumption, and memory utilization compared with existing solutions?Category: Personal Multimodal Memory RetrievalSimilar questionsarrow_forward
Practical Problems
1- Users have difficulty quickly retrieving health data on smartwatches when the network is unstable or unavailable.Category: Personal Multimodal Memory RetrievalSimilar questionsarrow_forward
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