DGSense: A Domain Generalization Framework for Wireless Sensing
Rui Zhou, Yu Cheng, Songlin Li, Hongwang Zhang, Chenxu Liu

TL;DR
DGSense is a domain generalization framework for wireless sensing that enables models to perform well in unseen environments without requiring target domain data, using diverse training data and domain-independent feature extraction.
Contribution
The paper introduces DGSense, a novel framework that achieves domain generalization in wireless sensing tasks without target domain data, applicable across various sensing technologies.
Findings
High generalization to unseen domains in WiFi gesture recognition
Effective across diverse sensing tasks like mmWave activity recognition and fall detection
No retraining needed for new environments or users
Abstract
Wireless sensing is of great benefits to our daily lives. However, wireless signals are sensitive to the surroundings. Various factors, e.g. environments, locations, and individuals, may induce extra impact on wireless propagation. Such a change can be regarded as a domain, in which the data distribution shifts. A vast majority of the sensing schemes are learning-based. They are dependent on the training domains, resulting in performance degradation in unseen domains. Researchers have proposed various solutions to address this issue. But these solutions leverage either semi-supervised or unsupervised domain adaptation techniques. They still require some data in the target domains and do not perform well in unseen domains. In this paper, we propose a domain generalization framework DGSense, to eliminate the domain dependence problem in wireless sensing. The framework is a general…
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Taxonomy
TopicsEnergy Efficient Wireless Sensor Networks · Indoor and Outdoor Localization Technologies
