CODA enables continuous online adaptation for HAR sensing by cache-based selective assimilation of informative instances and adaptive temporal retention to forget obsolete data under non-stationary drift.
InProceedings of the 13th ACM Conference on Embedded Networked Sensor Systems(Seoul, South Korea)(Sen- Sys ’15)
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GenHAR generalizes cross-domain human activity recognition by 9.97% accuracy and 6.4x lower FLOPs via tokenized sensor data, frequency channel correlations, selective masking, and efficient attention, with deployment detecting 2.15 billion activities.
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CODA: A Continuous Online Evolve Framework for Deploying HAR Sensing Systems
CODA enables continuous online adaptation for HAR sensing by cache-based selective assimilation of informative instances and adaptive temporal retention to forget obsolete data under non-stationary drift.
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GenHAR: Generalizing Cross-domain Human Activity Recognition for Last-mile Delivery
GenHAR generalizes cross-domain human activity recognition by 9.97% accuracy and 6.4x lower FLOPs via tokenized sensor data, frequency channel correlations, selective masking, and efficient attention, with deployment detecting 2.15 billion activities.
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