Packets-to-Prediction: An Unobtrusive Mechanism for Identifying Coarse-Grained Sleep Patterns with WiFi MAC Layer Traffic

• Published in: Sensors (Basel, Switzerland) Vol. 23; no. 14; p. 6631
• Main Authors: Jaisinghani, Dheryta, Phutela, Nishtha
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 01.07.2023; MDPI
• Subjects: Access control; Accuracy; Adult; Analysis; Automation; Cluster Analysis; College students; Colleges & universities; Focus Groups; Humans; Hypotheses; Light; MAC layer; Machine Learning; Neural Networks, Computer; Random Forest; Sensors; Sleep; Sleep deprivation; sleep detection; Smartphones; sniffer; Wi-Fi; WiFi; sleep detection; sniffer; WiFi; machine learning; MAC layer
• ISSN: 1424-8220; 1424-8220
• DOI: 10.3390/s23146631

A good night’s sleep is of the utmost importance for the seamless execution of our cognitive capabilities. Unfortunately, the research shows that one-third of the US adult population is severely sleep deprived. With college students as our focused group, we devised a contactless, unobtrusive mechanism to detect sleep patterns, which, contrary to existing sensor-based solutions, does not require the subject to put on any sensors on the body or buy expensive sleep sensing equipment. We named this mechanism Packets-to-Predictions(P2P) because we leverage the WiFi MAC layer traffic collected in the home and university environments to predict “sleep” and “awake” periods. We first manually established that extracting such patterns is feasible, and then, we trained various machine learning models to identify these patterns automatically. We trained six machine learning models—K nearest neighbors, logistic regression, random forest classifier, support vector classifier, gradient boosting classifier, and multilayer perceptron. K nearest neighbors gave the best performance with 87% train accuracy and 83% test accuracy.