![]() We assess the performance of early and late fusion methods borrowed from the machine learning field, instead of the usual Kalman filtering approaches used in such problems. ![]() More specifically, we present the results of the individual analysis of each modality and the fusion of two modalities at the early level as well as at the results’ level, using two evaluation protocols. In the present work, we extract features from the RSSI readings as well and apply a variety of fusion methods and classifiers to investigate the performance of RSSI and accelerometers in room-level localization both in combination and separately. The experimental results revealed better performance of the RSSI extracted features, while the accelerometer’s individual performance was poor and subsequently affected the fusion results. The classification algorithms and the fusion methods used to predict the room were evaluated using different protocols applied to a public dataset. We further test the performance of the feature extraction from RSSI values. We present here the results of this preliminary approach of the early and late fusion of RSSI and accelerometer features in room-level localization. Motivated by a current project, where there is the need to locate a missing child in crowded spaces, we intend to test the added value of using an accelerometer along with RSSI for room-level localization and assess the performance of ensemble learning methods. Localization tasks with the goal to locate the room are actually classification problems. A common service offered by IoT systems is the estimation of a person’s position in indoor spaces, which is quite often achieved with the exploitation of the Received Signal Strength Indication (RSSI). Internet-of-Things (IoT) systems are used to provide remote solutions in different domains, like healthcare and security. The continuing advancements in technology have resulted in an explosion in the use of interconnected devices and sensors.
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |