Personalized Fall Detection by Balancing Data with Selective Feedback Using Contrastive Learning
arXiv:2603.17148v1 Announce Type: new Abstract: Personalized fall detection models can significantly improve accuracy by adapting to individual motion patterns, yet their effectiveness is often limited by the scarcity of real-world fall data and the dominance of non-fall feedback samples. This imbalance biases the model toward routine activities and weakens its sensitivity to true fall events. To address this challenge, we propose a personalization framework that combines semi-supervised clustering with contrastive learning to identify and balance the most informative user feedback samples. The framework is evaluated under three retraining strategies, including Training from Scratch (TFS), Transfer Learning (TL), and Few-Shot Learning (FSL), to assess adaptability across learning paradigms. Real-time experiments with ten participants show that the TFS approach achieves the highest performance, with up to a 25% improvement over the baseline, while FSL achieves the second-highest perfor
arXiv:2603.17148v1 Announce Type: new Abstract: Personalized fall detection models can significantly improve accuracy by adapting to individual motion patterns, yet their effectiveness is often limited by the scarcity of real-world fall data and the dominance of non-fall feedback samples. This imbalance biases the model toward routine activities and weakens its sensitivity to true fall events. To address this challenge, we propose a personalization framework that combines semi-supervised clustering with contrastive learning to identify and balance the most informative user feedback samples. The framework is evaluated under three retraining strategies, including Training from Scratch (TFS), Transfer Learning (TL), and Few-Shot Learning (FSL), to assess adaptability across learning paradigms. Real-time experiments with ten participants show that the TFS approach achieves the highest performance, with up to a 25% improvement over the baseline, while FSL achieves the second-highest performance with a 7% improvement, demonstrating the effectiveness of selective personalization for real-world deployment.
Executive Summary
This study proposes a personalization framework that leverages contrastive learning to identify and balance informative user feedback samples for fall detection models. The framework is evaluated under three retraining strategies, demonstrating improved accuracy over baseline models. The findings suggest that the proposed approach can enhance performance in real-world scenarios, particularly when utilizing the 'Training from Scratch' method. The study's implications are significant, as they highlight the potential for improved fall detection accuracy through selective personalization. Furthermore, the application of contrastive learning in this context showcases its versatility in addressing data imbalance challenges.
Key Points
- ▸ The proposed framework combines semi-supervised clustering with contrastive learning to address data imbalance challenges in fall detection models.
- ▸ The study evaluates the framework under three retraining strategies, including Training from Scratch, Transfer Learning, and Few-Shot Learning.
- ▸ Real-time experiments demonstrate improved accuracy over baseline models, with the 'Training from Scratch' approach yielding the highest performance.
Merits
Strength in Addressing Data Imbalance Challenges
The proposed framework effectively addresses the scarcity of real-world fall data and dominance of non-fall feedback samples, leading to improved model accuracy.
Versatility of Contrastive Learning
The study showcases the applicability of contrastive learning in addressing data imbalance challenges, highlighting its potential for real-world deployment.
Demerits
Limited Generalizability to Other Applications
The study's findings and framework may not be directly generalizeable to other applications or scenarios, limiting their broader impact.
Need for Further Evaluation
The study's results may require further validation and evaluation to ensure their robustness and applicability in diverse settings.
Expert Commentary
The study's proposed framework and approach demonstrate a promising solution to the challenges of data imbalance in fall detection models. However, it is essential to consider the limitations of the study and the broader implications of its findings. The application of contrastive learning and semi-supervised clustering in this context highlights the potential for AI-driven healthcare solutions to improve patient outcomes. Nevertheless, further evaluation and validation of the study's results are necessary to ensure their robustness and applicability in diverse settings.
Recommendations
- ✓ Future studies should aim to evaluate the proposed framework and approach in a broader range of scenarios and applications to enhance generalizability and broader impact.
- ✓ Researchers should explore the potential for extending the proposed framework and approach to other healthcare applications and scenarios, capitalizing on the versatility of contrastive learning.
