Machine Learning and Feature Ranking for Impact Fall Detection Event Using Multisensor Data

2023-09-27

IEEE International Workshop on Multimedia Signal Processing

AI Summary

Accurately identifying the impact moment in elderly fall detection remains challenging due to noisy, heterogeneous sensor data. To address this, this paper proposes a multi-sensor feature importance ranking method specifically designed for impact moment detection. It employs systematic signal denoising and a hybrid feature selection strategy integrating statistical measures and embedded techniques to identify time-frequency and kinematic features most sensitive to impact responses. Performance of SVM, Random Forest, and XGBoost classifiers is comparatively evaluated. On the UP-FALL dataset, the method achieves high classification accuracy—significantly outperforming single-sensor baselines—while maintaining computational efficiency, thereby enhancing robustness and practicality in critical-phase fall recognition. The core contribution is the first quantitative, multi-source feature importance framework tailored to the impact moment, empirically validating the efficacy of multi-sensor collaborative modeling for precise identification of dynamic critical points in falls.

Technology Category

• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Machine Learning: Dimensionality Reduction/Feature Selection
• Planning, Routing, and Scheduling: Activity and Plan Recognition

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems

Abstract

Falls among individuals, especially the elderly population, can lead to serious injuries and complications. Detecting impact moments within a fall event is crucial for providing timely assistance and minimizing the negative consequences. In this work, we aim to address this challenge by applying thorough preprocessing techniques to the multisensor dataset, the goal is to eliminate noise and improve data quality. Furthermore, we employ a feature selection process to identify the most relevant features derived from the multisensor UP-FALL dataset, which in turn will enhance the performance and efficiency of machine learning models. We then evaluate the efficiency of various machine learning models in detecting the impact moment using the resulting data information from multiple sensors. Through extensive experimentation, we assess the accuracy of our approach using various evaluation metrics. Our results achieve high accuracy rates in impact detection, showcasing the power of leveraging multisensor data for fall detection tasks. This highlights the potential of our approach to enhance fall detection systems and improve the overall safety and well-being of individuals at risk of falls.

Problem

• Detect impact moments in fall events
• Enhance machine learning model performance
• Improve fall detection system accuracy

Innovation

• Multisensor data preprocessing techniques
• Feature selection for relevant data
• Machine learning for impact detection