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Clinical Assessment of Spatiotemporal Gait Parameters in Patients and Older Adults

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Published on: November 7, 2014

Temporal Convolutional Neural Networks for Radar Micro-Doppler Based Gait Recognition.

Pia Addabbo¹, Mario Luca Bernardi², Filippo Biondi³

¹Science and Technology for Transportations Faculty, Università degli Studi "Giustino Fortunato", Viale Raffale Delcogliano, 12, 82100 Benevento, Italy.

Sensors (Basel, Switzerland)

|January 12, 2021

Summary

This study introduces a radar-based system using deep learning for discrete human identification, outperforming traditional cameras. The approach offers a low-power, scalable solution for public security applications.

Keywords:

deep learning gait recognition human ID low-power radar micro-Doppler

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Area of Science:

Biometrics and Surveillance Technology
Signal Processing and Machine Learning

Background:

Traditional camera surveillance for human identification faces limitations in discretion, computational demands, and environmental factors.
There is a need for robust, low-power, and discrete human identification systems, particularly for public security.

Purpose of the Study:

To propose and evaluate a novel approach for individual identification using radar micro-Doppler signatures.
To develop a system with low size, weight, and power (SWaP) for scalable deployment.

Main Methods:

Utilized temporal convolutional deep neural networks (CNNs) as a classifier for radar micro-Doppler signatures.
Assessed the proposed approach on real-world data from 106 individuals.

Main Results:

Achieved high classification accuracy, with the best performance reaching 0.89 accuracy and an F1-score of 0.885.
Demonstrated improved performance compared to existing standard identification methods.

Conclusions:

The proposed radar-based deep learning approach offers a viable and effective alternative for discrete human identification.
The system's low SWaP profile enables widespread, unobtrusive deployment in sensitive security scenarios.