Indoor Space Classification Using Cascaded LSTM

• Published in: 2020 15th IEEE Conference on Industrial Electronics and Applications (ICIEA) pp. 1110 - 1114
• Main Authors: Kumar Yadav, Rohan, Bhattarai, Bimal, Jiao, Lei, Goodwin, Morten, Granmo, Ole-Christoffer
• Format: Conference Proceeding
• Language: English
• Published: IEEE 09.11.2020
• Subjects: Data models; deep recurrent neural network; Feature extraction; geomagnetic field; LSTM; Machine learning; Object detection; radio signal strength; Recurrent neural networks; Testing; Wireless fidelity
• ISSN: 2158-2297
• DOI: 10.1109/ICIEA48937.2020.9248347

Indoor space classification is an important part of localization that helps in precise location extraction, which has been extensively utilized in industrial and domestic domain. There are various approaches that employ Bluetooth Low En-ergy (BLE), Wi-Fi, magnetic field, object detection, and Ultra Wide Band (UWB) for indoor space classification purposes. Many of the existing approaches need extensive pre-installed infrastructure, making the cost higher to obtain reasonable accuracy. Therefore, improvements are still required to increase the accuracy with minimum requirements of infrastructure. In this paper, we propose an approach to classify the indoor space using geomagnetic field (GMF) and radio signal strength (RSS) as the identity. The indoor space is an open big test bed divided into different indiscernible subspace. We collect GMF and RSS at each subspace and classify it using cascaded Long Short Term Memory (LSTM). The experimental results show that the accuracy is significantly improved when GMF and RSS are combined to make distinct features. In addition, we compare the performance of the proposed model with the state-of-the-art machine learning methods.