Device-free indoor localization systems play a pivotal role in enhancing the functionality and intelligence of modern environments. They can effectively monitor people’s movements in their everyday environment without the constraints of invasive or wearable devices, and are open to a wide range of application domains. Through a systematic experimental approach, in this work we investigate the performance of underfloor accelerometers in accurately detecting and tracking user movements. The collected data, augmented with ground truth information, are analyzed using fingerprint maps and k-Nearest Neighbor (k-NN) algorithms to estimate the user’s position within the environment. In the literature, this work represents a first attempt to apply the fingerprint technique in this context. The results show promising capabilities of underfloor accelerometers in facilitating location-based services, while the short time required for installation, data pre-processing and calibration indicate this approach as an easy-to-deploy location-based system. In this regard, intra-user tests show that the variability of the error exceeds 1 m regardless of k-values or time windows, inter-user tests show that the time window does not affect the variability of distance estimation with 2-NN, which outperforms other k-configurations, while 3-NN performs better as the time window increases. The cumulative distribution function over the entire test set shows that more than 75% of the predictions are less than 1 m.
Belli et al. (Fri,) studied this question.