Device-Free Indoor Human Activity Recognition Using Wi-Fi RSSI: Machine Learning Approaches

Chao Feng Hsieh, Yi Chu Chen, Cheng Ying Hsieh, Meng Lin Ku

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

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.

Original languageEnglish
Title of host publication2020 IEEE International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2020
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781728173993
DOIs
StatePublished - 28 Sep 2020
Event7th IEEE International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2020 - Taoyuan, Taiwan
Duration: 28 Sep 202030 Sep 2020

Publication series

Name2020 IEEE International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2020

Conference

Conference7th IEEE International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2020
Country/TerritoryTaiwan
CityTaoyuan
Period28/09/2030/09/20

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