Indoor Positioning System: A New Approach Based on LSTM and Two Stage Activity Classification

Electronics

Ghulam Hussain¹  MuhammadShahid Jabbar¹  Sangmin Bae¹  Jun-Dong Cho¹ 

[1] College of Information and Communication Engineering, Sungkyunkwan University, Suwon 16419, Korea;

关键词: activity recognition;    indoor localization;    accelerometer;    gyroscope;    signal segmentation;    long short-term memory;   

DOI  :  10.3390/electronics8040375

来源: DOAJ

【 摘 要 】

The number of studies on the development of indoor positioning systems has increased recently due to the growing demands of the various location-based services. Inertial sensors available in commercial smartphones play an important role in indoor localization and navigation owing to their highly accurate localization performance. In this study, the inertial sensors of a smartphone, which generate distinct patterns for physical activities and action units (AUs), are employed to localize a target in an indoor environment. These AUs, (such as a left turn, right turn, normal step, short step, or long step), help to accurately estimate the indoor location of a target. By taking advantage of sophisticated deep learning algorithms, we propose a novel approach for indoor navigation based on long short-term memory (LSTM). The LSTM accurately recognizes physical activities and related AUs by automatically extracting the efficient features from the distinct patterns of the input data. Experiment results show that LSTM provides a significant improvement in the indoor positioning performance through the recognition task. The proposed system achieves a better localization performance than the trivial fingerprinting method, with an average error of 0.782 m in an indoor area of 128.6 m². Additionally, the proposed system exhibited robust performance by excluding the abnormal activity from the pedestrian activities.

【 授权许可】

Unknown