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Capturing Dynamic Finger Gesturing with High-resolution Surface Electromyography and Computer Vision

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Fingerprint Feature Extraction for Indoor Localization.

Jehn-Ruey Jiang¹, Hanas Subakti¹, Hui-Sung Liang¹

¹Department of Computer Science and Information Engineering, National Central University, Taoyuan City 320317, Taiwan.

Sensors (Basel, Switzerland)

|August 28, 2021

Summary

This summary is machine-generated.

This study introduces a novel fingerprint feature extraction (FPFE) method for precise indoor localization using Bluetooth low energy (BLE) beacons. FPFE achieves a low average error of 0.68 m, outperforming existing BLE localization techniques.

Keywords:

Bluetooth autoencoder beacon fingerprint indoor localization principal component analysis

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Area of Science:

Computer Science
Electrical Engineering
Robotics

Background:

Indoor localization remains a challenge, with existing methods often exhibiting significant errors.
Bluetooth low energy (BLE) beacons offer a viable solution for indoor positioning systems.
Accurate fingerprint-based localization requires efficient feature extraction and similarity measurement.

Purpose of the Study:

To propose and evaluate a novel fingerprint-based indoor localization method, FPFE (fingerprint feature extraction).
To enhance the accuracy of indoor localization using BLE beacon technology.
To compare FPFE's performance against other related BLE indoor localization methods.

Main Methods:

Deployment of BLE beacon nodes (BNs) to emit signals.
Measurement of Received Signal Strength Indication (RSSI) at reference points (RPs) to create fingerprints.
Application of autoencoder (AE) or principal component analysis (PCA) for feature extraction.
Similarity measurement using Minkowski distance and selection of k-nearest RPs for location estimation.

Main Results:

The proposed FPFE method demonstrated a high localization accuracy.
FPFE achieved an average localization error of 0.68 meters.
Experimental results indicate FPFE outperforms other BLE fingerprint-based indoor localization methods.

Conclusions:

FPFE is an effective and accurate method for indoor localization using BLE technology.
The feature extraction techniques (AE and PCA) contribute to improved localization performance.
FPFE offers a promising solution for precise indoor positioning applications.