A fall alert system with prior-fall activity identification
Pisol Ruenin, Sarayut Techakaew, Patsakorn Towatrakool, Jakarin, Chawachat

TL;DR
This paper presents a fall alert system that identifies prior-fall activities using multi-spot on-body sensors and machine learning, achieving high accuracy in detecting falls and related activities, with potential applications in elderly care.
Contribution
The study introduces a novel fall alert system that detects prior-fall activities and optimizes sensor placement, improving fall detection accuracy over existing methods.
Findings
Fall detection accuracy of 88.91%
Prior-fall activity detection accuracy of 86.25%
Effective use of XGBoost and chest sensor placement
Abstract
Falling, especially in the elderly, is a critical issue to care for and surveil. There have been many studies focusing on fall detection. However, from our survey, there is still no research indicating the prior-fall activities, which we believe that they have a strong correlation with the intensity of the fall. The purpose of this research is to develop a fall alert system that also identifies prior-fall activities. First, we want to find a suitable location to attach a sensor to the body. We created multiple-spot on-body devices to collect various activity data. We used that dataset to train 5 different classification models. We selected the XGBoost classification model for detecting a prior-fall activity and the chest location for use in fall detection from a comparison of the detection accuracy. We then tested 3 existing fall detection threshold algorithms to detect fall and fall to…
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Taxonomy
TopicsContext-Aware Activity Recognition Systems
