Human Fall Detection by Using an Innovative Floor Acoustic Sensor

2017 ◽  
pp. 97-107 ◽  
Author(s):  
Diego Droghini ◽  
Emanuele Principi ◽  
Stefano Squartini ◽  
Paolo Olivetti ◽  
Francesco Piazza
Keyword(s):  
Fall Detection ◽  
Acoustic Sensor

scholarly journals A Combined One-Class SVM and Template-Matching Approach for User-Aided Human Fall Detection by Means of Floor Acoustic Features

2017 ◽  
Vol 2017 ◽  
pp. 1-13 ◽  
Author(s):  
Diego Droghini ◽  
Daniele Ferretti ◽  
Emanuele Principi ◽  
Stefano Squartini ◽  
Francesco Piazza
Keyword(s):  
Performance Improvement ◽  
Template Matching ◽  
Scientific Community ◽  
Fall Detection ◽  
Final Decision ◽  
Acoustic Sensor ◽  
Acoustic Features ◽  
Mel Frequency Cepstral Coefficients ◽  
Single Sensor ◽  
Noisy Conditions

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.

Development of Fall Detection System Using Ultrasound Sensors

2011 ◽  
Vol 131 (1) ◽  
pp. 45-52 ◽  
Author(s):  
Takuya Tajima ◽  
Takehiko Abe ◽  
Haruhiko Kimura
Keyword(s):  
Detection System ◽  
Fall Detection

DEVELOPMENT OF FALL DETECTION ALGORITHM FOR TELEMONITORING SYSTEM

2019 ◽  
Vol 7 (2) ◽  
pp. 1
Author(s):  
C. A. MEBRIM ◽  
O. C. UBADIKE ◽  
A. M. AIBINU ◽  
I. I. ALEGBELEYE ◽  
A. J. ONUMANYI ◽  
...  
Keyword(s):  
Fall Detection ◽  
Detection Algorithm

HUMAN FALL DETECTION AND TRACKING SYSTEM USING MEMS SENSOR

2017 ◽  
Vol 6 (3) ◽  
pp. 20
Author(s):  
A. SAIPRIYA ◽  
V. MEENA ◽  
MAALIK M.ABDUL ◽  
D. PRAVINRAJ ◽  
P. JEGADEESHWARI ◽  
...  
Keyword(s):  
Tracking System ◽  
Fall Detection ◽  
Mems Sensor ◽  
Detection And Tracking

Design and implementation of fall detection system using tri-axis accelerometer

2013 ◽  
Vol 32 (5) ◽  
pp. 1450-1452 ◽  
Author(s):  
Rong WANG ◽  
Yun ZHANG ◽  
Jian-xin CHEN
Keyword(s):  
Detection System ◽  
Fall Detection ◽  
Design And Implementation
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