title: Accelerometer and Camera-Based Strategy for Improved Human Fall Detection.
abstract: In this paper, we address the problem of detecting human falls using anomaly detection. Detection and classification of falls are based on accelerometric data and variations in human silhouette shape. First, we use the exponentially weighted moving average (EWMA) monitoring scheme to detect a potential fall in the accelerometric data. We used an EWMA to identify features that correspond with a particular type of fall allowing us to classify falls. Only features corresponding with detected falls were used in the classification phase. A benefit of using a subset of the original data to design classification models minimizes training time and simplifies models. Based on features corresponding to detected falls, we used the support vector machine (SVM) algorithm to distinguish between true falls and fall-like events. We apply this strategy to the publicly available fall detection databases from the university of Rzeszow's. Results indicated that our strategy accurately detected and classified fall events, suggesting its potential application to early alert mechanisms in the event of fall situations and its capability for classification of detected falls. Comparison of the classification results using the EWMA-based SVM classifier method with those achieved using three commonly used machine learning classifiers, neural network, K-nearest neighbor and naïve Bayes, proved our model superior.
authors: LCPTS, Faculty of Electronics and Computer Science, University of Sciences and Technology Houari Boumédienne (USTHB), Algiers, Algeria.
authors: Nabil
authors: N
authors: Zerrouki
authors: Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division, King Abdullah University of Science and Technology (KAUST), Thuwal, 23955-6900, Saudi Arabia. fouzi.harrou@kaust.edu.sa.
authors: Fouzi
authors: F
authors: Harrou
authors: Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division, King Abdullah University of Science and Technology (KAUST), Thuwal, 23955-6900, Saudi Arabia.
authors: Ying
authors: Y
authors: Sun
authors: LCPTS, Faculty of Electronics and Computer Science, University of Sciences and Technology Houari Boumédienne (USTHB), Algiers, Algeria.
authors: Amrane
authors: A
authors: Houacine
mesh_terms: D061725:Accelerometry / Q000379:methods*; D000058:Accidental Falls / Q000706:statistics & numerical data*; D000465:Algorithms; D001499:Bayes Theorem; D006801:Humans; D000069550:Machine Learning; D018670:Monitoring, Ambulatory / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods*; D060388:Support Vector Machine
keywords: Anomaly detection; Fall detection and classification; Support vector machine; Tri axial accelerometer; Visiosurvaillance