@inproceedings{96b66a2d41ac4f5a90f445c48dbe6620,
title = "Device-Free Indoor Human Activity Recognition Using Wi-Fi RSSI: Machine Learning Approaches",
abstract = "A device-free methodology is proposed to recognize the human activity in indoor environments using WiFi received signal strength indication (RSSI), and several machine learning (ML) approaches are investigated to realize the activity detection. With multiple access points (APs), the RSSIs are synchronously collected at multiple mobile phones (MPs) with different locations over a time duration and served as input data for training the detectors, enabling us to recognize the human activities of either moving or stationary. Extensive real experiments are conducted in the frequency bands of 2.4 GHz and 5 GHz, and the results show that the proposed methods can achieve over 95% recognition accuracy in the 5 GHz frequency band.",
author = "Hsieh, {Chao Feng} and Chen, {Yi Chu} and Hsieh, {Cheng Ying} and Ku, {Meng Lin}",
note = "Publisher Copyright: {\textcopyright} 2020 IEEE.; 7th IEEE International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2020 ; Conference date: 28-09-2020 Through 30-09-2020",
year = "2020",
month = sep,
day = "28",
doi = "10.1109/ICCE-Taiwan49838.2020.9258097",
language = "???core.languages.en_GB???",
series = "2020 IEEE International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2020",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "2020 IEEE International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2020",
}