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

• Published in: IEEE sensors letters Vol. 5; no. 5; pp. 1 - 4
• Main Authors: Konings, Daniel, Grace, Russell, Alam, Fakhrul
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01.05.2021; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: activity detection; Activity recognition; channel state information (CSI); convolutional neural network (CNN); Data models; device-free localization (DFL); Fall detection; feature recognition; human sensing; Indoor environments; Machine learning; Neural networks; Sensor applications; Sensors; Testing; Training; Transfer learning; Wi-Fi sensing; Wireless fidelity
• ISSN: 2475-1472; 2475-1472
• DOI: 10.1109/LSENS.2021.3075671

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.