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Gait-Based Identification Using Deep Recurrent Neural Networks and Acceleration Patterns.

Angel Peinado-Contreras¹, Mario Munoz-Organero²

¹School of Engineering, Universidad Carlos III de Madrid, 28911 Leganés, Madrid, Spain.

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|December 8, 2020

Summary

This summary is machine-generated.

This study introduces a novel biometric identification method using smartphone sensor data and recurrent neural networks (RNNs). The gait analysis achieved over 97% precision, offering a robust and efficient personal identification solution.

Keywords:

LSTM accelerometry accuracy gait identification recognition recurrent neural network smartphone walk

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Area of Science:

Biometrics
Machine Learning
Human-Computer Interaction

Background:

Biometric identification is crucial for security.
Gait analysis offers a unique, non-intrusive identification method.
Smartphone sensors provide accessible data for biometric applications.

Purpose of the Study:

To develop a biometric identification system using smartphone accelerometer and gyroscope data.
To optimize a recurrent neural network (RNN) for gait pattern recognition.
To evaluate the efficiency and robustness of the proposed deep learning model.

Main Methods:

Utilized smartphone accelerometer and gyroscope data from 15 users during gait activity.
Pre-processed data to extract vertical acceleration patterns.
Designed and implemented a deep recurrent neural network with LSTM cells for user recognition.

Main Results:

Achieved user recognition precision exceeding 97% in most executions.
Demonstrated the model's efficiency and robustness across different testing scenarios.
Successfully learned individual gait features using the optimized RNN architecture.

Conclusions:

The proposed deep neural network-based approach offers a highly accurate biometric identification system.
Smartphone-based gait analysis using RNNs is a viable and effective method for personal identification.
The system shows promise for real-world applications requiring secure and convenient user authentication.