Supervised and Unsupervised Transfer Learning for Activity Recognition from Simple In-home Sensors

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Sozo Inoue, Xincheng Pan,
International Conference on Mobile and Ubiquitous Systems: Computing, Networking and Services (MobiQuitous) (Acceptance Rate: 30%)
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In this paper, we propose an approach to improve the accuracy of life activity estimation using device-free sensors in the home.
We assumed two scenarios in which we only have training data from other households with labels and we also have training labels for our own household and, proposed the method to compose supervised transfer between labeled data and unsupervised for each scenario.
We developed the system which consists of an smartphone apps and web server, and gathered subjects from open called households during a period of approximately four months, and obtained approximately 11,745 activity inputs, 7.14GB of sensor data, and power consumption data of 237,280 hours from 35 households, and found to outperform naive methods in the both scenarios.

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