Accelerometer and Camera-Based Strategy for Improved Human Fall Detection

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 potent...

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Vydáno v:	Journal of medical systems Ročník 40; číslo 12; s. 284
Hlavní autoři:	Zerrouki, Nabil, Harrou, Fouzi, Sun, Ying, Houacine, Amrane
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	New York Springer US 01.12.2016 Springer Nature B.V
Témata:	Accelerometers Accelerometry - methods Accidental Falls - statistics & numerical data Algorithms Bayes Theorem Falls Health Informatics Health Sciences Humans Intelligent systems Machine Learning Medicine Medicine & Public Health Monitoring, Ambulatory - methods Pattern Recognition, Automated - methods Statistics for Life Sciences Support Vector Machine Surveillance Systems-Level Quality Improvement Tri axial accelerometer Visiosurvaillance Fall detection and classification Support vector machine Anomaly detection
ISSN:	0148-5598, 1573-689X, 1573-689X
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Shrnutí:	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.
Bibliografie:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23
ISSN:	0148-5598 1573-689X 1573-689X
DOI:	10.1007/s10916-016-0639-6