Enhancing Fall Detection Accuracy: The Ground-Face Coordinate System for 3D Accelerometer Data

The global elderly population is on the rise, leading to increased physical, sensory, and cognitive changes that heighten the risk of falls. Consequently, fall detection (FD) has emerged as a significant concern, attracting considerable attention in recent years. Utilizing 3D accelerometer sensors for FD offers advantages such as cost-effectiveness and ease of implementation; however, traditional raw 3D accelerometer signals are inherently dependent on the device's orientation and placement within the device coordinate system. Misalignment between the device's axes and the direction of movement can lead to misinterpretation of acceleration signals, potentially causing misclassification of activities and resulting in false positives or missed falls. This study introduces a novel coordinate system called "ground-face," which is designed to be independent of the device's orientation and placement. In this system, the vertical axis is aligned perpendicularly to the Earth, while the device's x-axis is aligned with the individual's direction of movement. To assess the potential of the vertical component of ground-face referenced accelerometer signals for FD, it was compared with the commonly used acceleration magnitude signal. Detailed analysis was conducted using frequently preferred features in FD studies, and fall detection was performed with various classifiers. Comprehensive experiments demonstrated that the vertical component of the ground-face signal effectively characterizes falls, yielding approximately a 2% improvement in detection accuracy. Moreover, the proposed coordinate system is not limited to FD but can also be applied to human activity recognition (HAR) systems. By mitigating orientation-related discrepancies, it reduces the likelihood of misclassification and enhances the overall HAR capabilities.