A Stacked Neural Network-Based Machine Learning Framework to Detect Activities and Falls Within Multiple Indoor Environments Using Wi-Fi CSI

Abstract

Device-free methods for activity or fall detection using Wi-Fi channel state information (CSI) have become popular in the literature as they are not intrusive to privacy like competing camera-based solutions. However, such methods require significant setup processes. The objective of this letter is to improve upon the current CSI-based systems by proposing a two-stage modular architecture. A stacked neural network is developed that selects which environment or room a person is located within, before engaging a room-level model for activity recognition. This allows machine learning models to be iteratively deployed to multiple environments without retraining previously deployed room-level models.