Big Data Small Footprint: The Design of A Low-Power Classifier for Detecting Transportation Modes
Summary: Hardware–software co-design of a low-power wearable transport-mode classifier, delivering big-data small-footprint with lean compute and memory. Sensor-hub config yields ~99% power savings with competitive accuracy; data released for research. (summarized by gpt-5-nano on Feb 09 2026)
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Authors
- 1. Meng-Chieh Yu (HTC Corporation)
- 2. Tong Yu (National Taiwan University)
- 3. Shao-Chen Wang (HTC Corporation)
- 4. Chih-Jen Lin (National Taiwan University)
- 5. Edward Y. Chang (HTC Corporation)
BibTeX Citation
@article{yu_vldb14,
title = {{Big Data Small Footprint: The Design of A Low-Power Classifier for Detecting Transportation Modes}},
author = {Yu, Meng-Chieh and Yu, Tong and Wang, Shao-Chen and Lin, Chih-Jen and Chang, Edward Y.},
journal = {PVLDB},
series = {{VLDB} '14},
volume = {7},
number = {13},
pages = {1429--1432},
doi = {10.14778/2733004.2733015},
url = {https://doi.org/10.14778/2733004.2733015},
year = {2014}
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