Use of kinematic and mel-cepstrum-related features for fall detection based on data from infrared depth sensors

2018 ◽  
Vol 40 ◽  
pp. 102-110 ◽  
Author(s):  
Paweł Mazurek ◽  
Jakub Wagner ◽  
Roman Z. Morawski
Keyword(s):  
Fall Detection ◽  
Depth Sensors ◽  
Mel Cepstrum

Radar and RGB-Depth Sensors for Fall Detection: A Review

2017 ◽  
Vol 17 (12) ◽  
pp. 3585-3604 ◽  
Author(s):  
Enea Cippitelli ◽  
Francesco Fioranelli ◽  
Ennio Gambi ◽  
Susanna Spinsante
Keyword(s):  
Fall Detection ◽  
Depth Sensors

scholarly journals A SKELETON FEATURES-BASED FALL DETECTION USING MICROSOFT KINECT V2 WITH ONE CLASS-CLASSIFIER OUTLIER REMOVAL

2019 ◽  
Vol XLII-2/W12 ◽  
pp. 189-195
Author(s):  
O. S. Seredin ◽  
A. V. Kopylov ◽  
S.-C. Huang ◽  
D. S. Rodionov
Keyword(s):  
Fall Detection ◽  
Microsoft Kinect ◽  
Compact Representation ◽  
Support Vector ◽  
Kinect V2 ◽  
Depth Sensors ◽  
Individual Decisions ◽  
One Class Classifier ◽  
The One ◽  
The Individual

<p><strong>Abstract.</strong> The real-time and robust fall detection is one of the key components of elderly people care and monitoring systems. Depth sensors, as they became more available, occupy an increasing place in event recognition systems. Some of them can directly produce a skeletal description of the human figure for compact representation of a person’s posture. Skeleton description makes the output of source video or detailed information about the depth outside the system unnecessary and raises the privacy of the entire system. Based on a comparative study of different RGB-D cameras, the most promising model for further development was chosen - Microsoft Kinect v2. The TST Fall Detection Dataset v2 is used here as a base for experiments. The proposed algorithm is based on the skeleton features encoding on the sequence of neighboring frames and support vector machine classifier. A version of a cumulative sum method is applied for combining the individual decisions on the consecutive frames. It is offered to use the one-class classifier for detection of low-quality skeletons. The 0.958 accuracy of our fall detection procedure was obtained in the cross-validation procedure based on the removal of records of a particular person from the database (Leave-one-Person-out).</p>

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
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