Discriminating Between Indoor and Outdoor Environments During Daily Living Activities Using Local Magnetic Field Characteristics and Machine Learning Techniques

Wearable technology has rapidly advanced, opening new possibilities for context-aware applications in fields such as healthcare and gait analysis, where distinguishing between indoor and outdoor environments is essential. This is often accomplished through technologies like GPS, Wi-Fi, cellular, and Bluetooth which, however, come with privacy concerns, high power consumption, and dependency on external infrastructure. To address these challenges, recent studies have preliminary exploited the ambient magnetic field, though comprehensive validation with real-life data is lacking. This article seeks to validate machine learning techniques, i.e., random forest (RF), extreme gradient boosting, and stacked long short-term memory (LSTM) networks, for indoor-outdoor discrimination using exclusively magnetometer data from the daily activities of 20 participants in four cities across three countries. The study investigated the most effective magnetometer placement (feet, lower back, and nondominant wrist) and pre-processing techniques (e.g., features and window size). Reference data are obtained through a GPS-based algorithm coupled with a geographical database running on a smartphone. The extreme gradient boosting algorithm yielded the best results, with an accuracy of 0.91, an F1-score of 0.90, and an area under the ROC curve of 0.94. These findings confirm the feasibility of accurately estimating indoor/outdoor context information from a magnetometer at the same update frequency as a common GPS, but with important energy savings. The proposed model can be integrated into state-of-the-art gait analysis systems, being able to discriminate the location to avoid misinterpreting gait deviations in real-world settings, thus supporting continuous and ubiquitous gait monitoring. Datasets and algorithm implementations have been made publicly available.