Investigation on Deployment Pattern of Wi-Fi Transceivers for CSI-based Indoor Localization and Activity Recognition

In recent years, many studies explore CSI-based Wi-Fi sensing produce high accurate results in device-free localization, human activity recognition. Channel state information (CSI) represents how wireless signals propagate from transmitter to receiver and provides rich information to identify human presence. By collecting CSI from the specific devices, the machine learning model can be trained to recognize the human activity. However, in multi-room residential settings where walls and furniture obstruct signals, effective coverage with a limited number of transceivers becomes a crucial challenge, underlining the importance of their optimal placement. In this paper, we deployed multiple transceivers in a smart home environment in our university and studied transceiver arrangement patterns for CSI-based indoor localization and activity recognition. We compared the accuracy of localization and activity recognition for a total of 18 patterns consisting of up to five transceivers. In the activity recognition, we used Support Vector Machine (SVM) to classify whether or not a person is moving. The results show that the pattern using only one pair of transceivers achieved an accuracy 85% and covered the entire house. Meanwhile, in the localization we used Light Gradient Boosting Machine (LGBM) to classify which room the person is. The results show that accuracy decreases as the number of devices is reduced. Therefore, we investigated deployment pattern to achieve accuracy with smaller number of transceivers.