schrodingho/FM_ZSL_IoT
Source Code for ECAI 2024 Paper "Leveraging Foundation Models for Zero-Shot IoT Sensing"
This project helps researchers and engineers analyze data from Internet of Things (IoT) sensors, like activity trackers or smart home devices, even for activities or events they haven't specifically trained their systems on. You feed it raw sensor data (e.g., from mmWave, IMU, or Wi-Fi) along with descriptions of what those signals represent, and it classifies new, previously 'unseen' activities. This is useful for anyone working with IoT sensor data who needs to adapt quickly to new scenarios without extensive re-training.
Use this if you need to classify or detect events from IoT sensor data for categories or situations that were not included in your original training data.
Not ideal if your IoT sensing tasks are entirely within well-defined, pre-trained categories and do not require recognizing novel events.
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Language
Python
License
MIT
Category
Last pushed
Feb 27, 2026
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