An SVM fall recognition algorithm based on a gravity acceleration sensor

Abstract

To address the increasing health care needs for an ageing population, in this paper, a method of detecting human movements using smartphones is proposed to decrease the risk of accidents in the elderly. The method proposed in this paper uses a mobile phone that has an embedded acceleration sensor to record human motion information that are divided into daily activities (walking, running, going up stairs, going down stairs, and standing still) and falling down. In the process of data acquisition, motion noise contains some interference, and thus the median filter is employed to de-noise and smooth the motion data. Moreover, we extract representative multi-group features and analyse the features by principal component analysis and singular value decomposition to reduce dimensions. Through experimental comparisons with various classifiers, the support vector machine classifier is selected to classify the extracted features. The accuracy of fall detection reached 96.072%, which proved the accuracy of our proposed method.

Keywords

• Gravity acceleration sensor
• principal component analysis
• support vector machine
• neural network
• fall detection