Intelligent Detection Algorithm for Fall from Height Based on Support Vector Machines

Among the "five major injuries" in the construction industry-namely, falls from height, electric shock, object strike, mechanical injury, and collapse-fall accidents from height exhibit the highest incidence rate and pose significant dangers. To mitigate the frequency of fall accidents, a fall detection algorithm was developed based on three-axis acceleration sensors. This intelligent algorithm analyzes the acceleration data of the human body during different motion states. It extracts eigenvalues from the acceleration data, processes and analyzes them using the support vector machine method, and performs data classification to determine if a person has undergone a fall. Through experimental testing and validation, this method has demonstrated a high level of reliability in the detection of falling behavior. The accuracy of this algorithm surpasses that of traditional threshold detection methods and decision tree-based algorithms. This enhancement improves its potential for application in the detection of falls from heights in construction settings.