A Combined One-Class SVM and Template-Matching Approach for User-Aided Human Fall Detection by Means of Floor Acoustic Features
Joint Authors
Droghini, Diego
Ferretti, Daniele
Principi, Emanuele
Piazza, Francesco
Squartini, Stefano
Source
Computational Intelligence and Neuroscience
Issue
Vol. 2017, Issue 2017 (31 Dec. 2017), pp.1-13, 13 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2017-05-30
Country of Publication
Egypt
No. of Pages
13
Main Subjects
Abstract EN
The primary cause of injury-related death for the elders is represented by falls.
The scientific community devoted them particular attention, since injuries can be limited by an early detection of the event.
The solution proposed in this paper is based on a combined One-Class SVM (OCSVM) and template-matching classifier that discriminate human falls from nonfalls in a semisupervised framework.
Acoustic signals are captured by means of a Floor Acoustic Sensor; then Mel-Frequency Cepstral Coefficients and Gaussian Mean Supervectors (GMSs) are extracted for the fall/nonfall discrimination.
Here we propose a single-sensor two-stage user-aided approach: in the first stage, the OCSVM detects abnormal acoustic events.
In the second, the template-matching classifier produces the final decision exploiting a set of template GMSs related to the events marked as false positives by the user.
The performance of the algorithm has been evaluated on a corpus containing human falls and nonfall sounds.
Compared to the OCSVM only approach, the proposed algorithm improves the performance by 10.14% in clean conditions and 4.84% in noisy conditions.
Compared to Popescu and Mahnot (2009) the performance improvement is 19.96% in clean conditions and 8.08% in noisy conditions.
American Psychological Association (APA)
Droghini, Diego& Ferretti, Daniele& Principi, Emanuele& Squartini, Stefano& Piazza, Francesco. 2017. A Combined One-Class SVM and Template-Matching Approach for User-Aided Human Fall Detection by Means of Floor Acoustic Features. Computational Intelligence and Neuroscience،Vol. 2017, no. 2017, pp.1-13.
https://search.emarefa.net/detail/BIM-1139839
Modern Language Association (MLA)
Droghini, Diego…[et al.]. A Combined One-Class SVM and Template-Matching Approach for User-Aided Human Fall Detection by Means of Floor Acoustic Features. Computational Intelligence and Neuroscience No. 2017 (2017), pp.1-13.
https://search.emarefa.net/detail/BIM-1139839
American Medical Association (AMA)
Droghini, Diego& Ferretti, Daniele& Principi, Emanuele& Squartini, Stefano& Piazza, Francesco. A Combined One-Class SVM and Template-Matching Approach for User-Aided Human Fall Detection by Means of Floor Acoustic Features. Computational Intelligence and Neuroscience. 2017. Vol. 2017, no. 2017, pp.1-13.
https://search.emarefa.net/detail/BIM-1139839
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1139839