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Using Deep Neural Networks for Human Fall Detection Based on Pose Estimation.

Mohammadamin Salimi¹, José J M Machado², João Manuel R S Tavares²

¹Faculdade de Engenharia, Universidade do Porto, Rua Dr. Roberto Frias, s/n, 4200-465 Porto, Portugal.

Sensors (Basel, Switzerland)

|June 24, 2022

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This study introduces a new method for detecting falls in older adults using computer vision and pose estimation. The system achieves high accuracy, offering a reliable alternative to wearable devices for elder safety.

Area of Science:

Computer Vision
Artificial Intelligence
Gerontology

Background:

Increasing demand for elder care and safety monitoring.
Human falls are a significant concern for older adults.
Existing fall detection methods include wearable sensors and computer vision.

Purpose of the Study:

To propose a novel human fall detection solution.
To utilize Fast Pose Estimation for fall event identification.
To offer an effective and deployable alternative to body-worn solutions.

Main Methods:

Employing Fast Pose Estimation to extract data from image frames.
Utilizing Time-Distributed Convolutional Long Short-Term Memory (TD-CNN-LSTM) and 1D Convolutional Neural Network (1D-CNN) models for classification.

Keywords:

computer vision deep learning image analysis machine learning

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Developing an imaging-based solution for fall detection.

Main Results:

Achieved 98% accuracy with the 1D-CNN model.
Achieved 97% accuracy with the TD-CNN-LSTM model.
Demonstrated the effectiveness of Fast Pose Estimation in fall detection.

Conclusions:

The proposed Fast Pose Estimation-based solution provides accurate human fall detection.
The system is suitable for deployment on edge devices due to low computational and memory requirements.
This imaging-based approach offers a viable alternative to traditional wearable fall detection systems.