Temporal Shift Module (TSM) Based Automatic Fall Detection with Bounding Box Grounding.

Area of Science:

• Computer Vision
• Geriatric Care Technology
• Artificial Intelligence in Healthcare

Background:

• Falls are a major cause of injury and mortality in the elderly, necessitating remote monitoring solutions.
• Existing vision-based fall detection models often perform poorly in real-world, complex environments due to training on acted datasets.
• Accurate, real-time fall detection is crucial for remote caregivers to provide timely medical intervention.

Purpose of the Study:

• To develop an improved vision-based fall detection mechanism for accurate detection of in-the-wild complex events.
• To enhance the reliability of fall detection systems for remote geriatric care operations.
• To address the limitations of current models trained on acted datasets.

Main Methods:

• A novel vision-based fall detection system leveraging the Temporal Shift Module (TSM).
• Integration of a bounding box grounding (BBG) approach for accurate Region Of Interest (ROI) sequence generation.
• Development of a model with computational complexity comparable to 2D CNNs, outperforming 3D CNN approaches.

Main Results:

• The proposed system demonstrates improved accuracy in detecting in-the-wild complex fall events.
• The model maintains computational efficiency, similar to 2D CNNs, while offering superior performance.
• Promising results were observed on both acted and real-world (in-the-wild) fall detection datasets.

Conclusions:

• The developed vision-based fall detection system offers a more accurate and efficient solution for real-world applications.
• This technology can significantly enhance the safety and care of elderly individuals living in isolation.
• The approach shows potential for widespread adoption in remote geriatric monitoring systems.