![](/images/graphics-bg.png)
A Combined One-Class SVM and Template-Matching Approach for User-Aided Human Fall Detection by Means of Floor Acoustic Features
المؤلفون المشاركون
Droghini, Diego
Ferretti, Daniele
Principi, Emanuele
Piazza, Francesco
Squartini, Stefano
المصدر
Computational Intelligence and Neuroscience
العدد
المجلد 2017، العدد 2017 (31 ديسمبر/كانون الأول 2017)، ص ص. 1-13، 13ص.
الناشر
Hindawi Publishing Corporation
تاريخ النشر
2017-05-30
دولة النشر
مصر
عدد الصفحات
13
التخصصات الرئيسية
الملخص 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.
نمط استشهاد جمعية علماء النفس الأمريكية (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
نمط استشهاد الجمعية الأمريكية للغات الحديثة (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
نمط استشهاد الجمعية الطبية الأمريكية (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
نوع البيانات
مقالات
لغة النص
الإنجليزية
الملاحظات
Includes bibliographical references
رقم السجل
BIM-1139839
قاعدة معامل التأثير والاستشهادات المرجعية العربي "ارسيف Arcif"
أضخم قاعدة بيانات عربية للاستشهادات المرجعية للمجلات العلمية المحكمة الصادرة في العالم العربي
![](/images/ebook-kashef.png)
تقوم هذه الخدمة بالتحقق من التشابه أو الانتحال في الأبحاث والمقالات العلمية والأطروحات الجامعية والكتب والأبحاث باللغة العربية، وتحديد درجة التشابه أو أصالة الأعمال البحثية وحماية ملكيتها الفكرية. تعرف اكثر
![](/images/kashef-image.png)