scholarly journals Comparison of Motion History Image and Approximated Ellipse Method in Human Fall Detection System

2019 ◽  
Vol 13 (2) ◽  
pp. 199
Author(s):  
Mohammad Brado Frasetyo ◽  
Elvira Sukma Wahyuni ◽  
Hendra Setiawan
Keyword(s):  
Detection System ◽  
Fall Detection ◽  
Initial Position ◽  
Video Data ◽  
Front Side ◽  
Motion History Image ◽  
Ellipse Method ◽  
Fall Direction ◽  
Cctv Camera ◽  
Better Than

This paper compares two different method in human fall detection system namely motion history image and approximated ellipse. Research has been done in small studio with 4 CCTV camera as video data recorder, whereas video data are processed using MATLAB software. The experiment was carried out using three object’s fall direction and two type of falling movement. The fall direction is consist of front, side, and back fall. Whereas the falling movement is consist of direct and indirect fall movement. Meanwhile, the object’s initial position is standing and size of captured object is constant. The result is motion history image has accuracy 74.26% for direct falling movement, and 75.69% for indirect falling movement. Whereas approximated ellipse has accuracy 56.85% for direct falling movement, and 61.81% for indirect falling movement. Therefore, motion history image is better than approximated ellipse in human fall detection system.

scholarly journals A novel computer vision-based data driven modelling approach for person specific fall detection

2021 ◽  
Vol 13 (5) ◽  
pp. 373-387
Author(s):  
Liyun Gong ◽  
Lu Zhang ◽  
Ming Zhu ◽  
Miao Yu ◽  
Ross Clifford ◽  
...  
Keyword(s):  
Home Environment ◽  
Independent Living ◽  
Detection System ◽  
Fall Detection ◽  
Video Data ◽  
Data Driven ◽  
Support Vector ◽  
False Alarms ◽  
Video Recordings ◽  
Low Dimensionality

In this paper, we propose a novel person specific fall detection system based on a monocular camera, which can be applied for assisting the independent living of an older adult living alone at home. A single camera covering the living area is used for video recordings of an elderly person’s normal daily activities. From the recorded video data, the human silhouette regions in every frame are then extracted based on the codebook background subtraction technique. Low-dimensionality representative features of extracted silhouetted are then extracted by convolutional neural network-based autoencoder (CNN-AE). Features obtained from the CNN-AE are applied to construct an one class support vector machine (OCSVM) model, which is a data driven model based on the video recordings and can be applied for fall detection. From the comprehensive experimental evaluations on different people in a real home environment, it is shown that the proposed fall detection system can successfully detect different types of falls (falls towards different orientations at different positions in a real home environment) with small false alarms.

ADVANCED CLASSIFICATION AND RULES-BASED EVALUATION OF MOTION, VISUAL AND BIOSIGNAL DATA FOR PATIENT FALL INCIDENT DETECTION

2010 ◽  
Vol 19 (02) ◽  
pp. 175-191 ◽  
Author(s):  
CHARALAMPOS DOUKAS ◽  
ILIAS MAGLOGIANNIS
Keyword(s):  
Detection System ◽  
Fall Detection ◽  
Video Data ◽  
Physiological Data ◽  
Incident Detection ◽  
Blood Oxygen Saturation ◽  
Fall Incident ◽  
Emergency Event ◽  
Acceleration Data ◽  
Electrocardiogram Ecg

The monitoring of human physiological data, in both normal and abnormal situations of activity, is interesting for the purpose of emergency event detection, especially in the case of elderly people living on their own. Several techniques have been proposed for identifying such distress situations using either motion, audio or video data from the monitored subject and the surrounding environment. This paper aims to present an integrated patient fall detection system that may be used for patient activity recognition and emergency treatment. Visual data captured from the user's environment, using overhead cameras among with motion and physiological data collected from the subject's body are utilized. Appropriate tracking techniques are applied to the aforementioned visual perceptual component enabling the trajectory tracking of the subjects, while acceleration data from the sensors can indicate a fall incident. Trajectory information and subject's visual location can verify fall and indicate an emergency event, whereas the interpretation of biosignals like electrocardiogram (ECG) and blood oxygen saturation (SPO2) can indicate the severity of the incident with the help of rules-based evaluation. The paper includes also the assessment of several classifiers and meta-classifiers in terms of accuracy in detecting falls and a user based evaluation.

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

scholarly journals Deep learning for vision‐based fall detection system: Enhanced optical dynamic flow

2020 ◽  
Author(s):  
Sagar Chhetri ◽  
Abeer Alsadoon ◽  
Thair Al‐Dala'in ◽  
P. W. C. Prasad ◽  
Tarik A. Rashid ◽  
...  
Keyword(s):  
Deep Learning ◽  
Detection System ◽  
Fall Detection ◽  
Dynamic Flow

scholarly journals Mobile activity recognition and fall detection system for elderly people using Ameva algorithm

2017 ◽  
Vol 34 ◽  
pp. 3-13 ◽  
Author(s):  
Miguel Ángel Álvarez de la Concepción ◽  
Luis Miguel Soria Morillo ◽  
Juan Antonio Álvarez García ◽  
Luis González-Abril
Keyword(s):  
Elderly People ◽  
Activity Recognition ◽  
Detection System ◽  
Fall Detection

scholarly journals Contactless Video-Based Heart Rate Monitoring of a Resting and an Anesthetized Pig

Animals ◽  
2021 ◽  
Vol 11 (2) ◽  
pp. 442
Author(s):  
Meiqing Wang ◽  
Ali Youssef ◽  
Mona Larsen ◽  
Jean-Loup Rault ◽  
Daniel Berckmans ◽  
...  
Keyword(s):  
Heart Rate ◽  
Estimation Error ◽  
Well Being ◽  
Absolute Error ◽  
Mean Value ◽  
Video Data ◽  
Window Functions ◽  
The Face ◽  
Short Time ◽  
Better Than

Heart rate (HR) is a vital bio-signal that is relatively easy to monitor with contact sensors and is related to a living organism’s state of health, stress and well-being. The objective of this study was to develop an algorithm to extract HR (in beats per minute) of an anesthetized and a resting pig from raw video data as a first step towards continuous monitoring of health and welfare of pigs. Data were obtained from two experiments, wherein the pigs were video recorded whilst wearing an electrocardiography (ECG) monitoring system as gold standard (GS). In order to develop the algorithm, this study used a bandpass filter to remove noise. Then, a short-time Fourier transform (STFT) method was tested by evaluating different window sizes and window functions to accurately identify the HR. The resulting algorithm was first tested on videos of an anesthetized pig that maintained a relatively constant HR. The GS HR measurements for the anesthetized pig had a mean value of 71.76 bpm and standard deviation (SD) of 3.57 bpm. The developed algorithm had 2.33 bpm in mean absolute error (MAE), 3.09 bpm in root mean square error (RMSE) and 67% in HR estimation error below 3.5 bpm (PE3.5). The sensitivity of the algorithm was then tested on the video of a non-anaesthetized resting pig, as an animal in this state has more fluctuations in HR than an anaesthetized pig, while motion artefacts are still minimized due to resting. The GS HR measurements for the resting pig had a mean value of 161.43 bpm and SD of 10.11 bpm. The video-extracted HR showed a performance of 4.69 bpm in MAE, 6.43 bpm in RMSE and 57% in PE3.5. The results showed that HR monitoring using only the green channel of the video signal was better than using three color channels, which reduces computing complexity. By comparing different regions of interest (ROI), the region around the abdomen was found physiologically better than the face and front leg parts. In summary, the developed algorithm based on video data has potential to be used for contactless HR measurement and may be applied on resting pigs for real-time monitoring of their health and welfare status, which is of significant interest for veterinarians and farmers.

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