A Machine Learning Approach for Fall Detection and Daily Living Activity Recognition

Abstract

The number of older people in western countries is constantly increasing. Most of them prefer to live independently and are susceptible to fall incidents. Falls often lead to serious or even fatal injuries which are the leading cause of death for elderlies. To address this problem, it is essential to develop robust fall detection systems. In this context, we develop a machine learning framework for fall detection and daily living activity recognition. We use acceleration and angular velocity data from two public databases to recognize seven different activities, including falls and activities of daily living. From the acceleration and angular velocity data, we extract time- and frequency-domain features and provide them to a classification algorithm. In this paper, we test the performance of four algorithms for classifying human activities. These algorithms are the artificial neural network (ANN), $K$ -nearest neighbors (KNN), quadratic support vector machine (QSVM), and ensemble bagged tree (EBT). New features that improve the performance of the classifier are extracted from the power spectral density of the acceleration. In the first step, only the acceleration data are used for activity recognition. Our results reveal that the KNN, ANN, QSVM, and EBT algorithms could achieve overall accuracy of 81.2%, 87.8%, 93.2%, and 94.1%, respectively. The accuracy of fall detection reaches 97.2% and 99.1% without any false alarms for the QSVM and EBT algorithms, respectively. In a second step, we extract features from the autocorrelation function and the power spectral density of both the acceleration and the angular velocity data, which improves the classification accuracy. By using the proposed features, we could achieve overall accuracy of 85.8%, 91.8%, 96.1%, and 97.7% for the KNN, ANN, QSVM, and EBT algorithms, respectively. The accuracy of fall detection reaches 100% for both the QSVM and EBT algorithms without any false alarm, which is the best achievable performance.