Related Experiment Video
Updated: May 11, 2026
08:05
Design and Analysis for Fall Detection System Simplification
Published on: April 6, 2020
10.6K
Deep Learning System for User Identification Using Sensors on Doorknobs
Jesús Vegas1, A Ravishankar Rao2, César Llamas1
1Escuela de Ingeniería Informática, Universidad de Valladolid, Paseo de Belén 15, 47011 Valladolid, Spain.
Sensors (Basel, Switzerland)
|August 10, 2024
Summary
This study introduces a new method for door access control using motion patterns from doorknob interactions. Deep learning accurately identifies users based on their unique motor activity, enhancing physical security.
Area of Science:
- Biometrics
- Human-Computer Interaction
- Security Systems
Background:
- Door access control is crucial for physical security.
- Current systems often rely on traditional authentication methods.
- Performance metrics like accuracy and speed are vital for access control systems.
Purpose of the Study:
- To investigate a novel approach for user identification in door access control.
- To utilize patterns of user interaction with a doorknob for authentication.
- To apply deep-learning algorithms to sensor data for behavioral biometrics.
Main Methods:
- Measuring user interactions with a doorknob using embedded accelerometer and gyroscope sensors.
- Applying deep-learning-based algorithms to analyze the collected sensor data.
- Evaluating identification accuracy across different user groups and sample durations.
Main Results:
- Achieved an overall user identification accuracy of 90.2% with 47 participants.
- User identification accuracy reached 97.0% for females and 89.8% for males.
- Demonstrated feasibility of identifying users with a short sample duration of 0.5 seconds (68.5% accuracy).
Conclusions:
- User identification through motor activity patterns is feasible for access control.
- This method offers a novel behavioral biometric for enhancing physical security.
- The approach provides an alternative to conventional authentication methods.
