Triaxial accelerometer-based fall detection method using a self-constructing cascade-AdaBoost-SVM classifier

In this paper, we propose a cascade-AdaBoost-support vector machine (SVM) classifier to complete the triaxial accelerometer-based fall detection method. The method uses the acceleration signals of daily activities of volunteers from a database and calculates feature values. By taking the feature values of a sliding window as an input vector, the cascade-AdaBoost-SVM algorithm can self-construct based on training vectors, and the AdaBoost algorithm of each layer can automatically select several optimal weak classifiers to form a strong classifier, which accelerates effectively the processing speed in the testing phase, requiring only selected features rather than all features. In addition, the algorithm can automatically determine whether to replace the AdaBoost classifier by support vector machine. We used the UCI database for the experiment, in which the triaxial accelerometers are, respectively, worn around the left and right ankles, and on the chest as well as the waist. The results are compared to those of the neural network, support vector machine, and the cascade-AdaBoost classifier. The experimental results show that the triaxial accelerometers around the chest and waist produce optimal results, and our proposed method has the highest accuracy rate and detection rate as well as the lowest false alarm rate.