An intelligent video-monitoring system to detect falls: a proof of concept

2018 ◽  
Vol 12 (4) ◽  
pp. 155-168
Author(s):  
Nolwenn Lapierre ◽  
Jean Meunier ◽  
Alain St-Arnaud ◽  
Jacqueline Rousseau
Keyword(s):  
Older Adults ◽  
Monitoring System ◽  
Detection System ◽  
Phase 1 ◽  
Fall Detection ◽  
Video Monitoring ◽  
False Alarms ◽  
Proof Of Concept ◽  
Content Type ◽  
At Home

Purpose To face the challenges raised by the high incidence of falls among older adults, the intelligent video-monitoring system (IVS), a fall detection system that respects privacy, was developed. Most fall detection systems are tested only in laboratories. The purpose of this paper is to test the IVS in a simulation context (apartment-laboratory), then at home. Design/methodology/approach This study is a proof of concept including two phases: a simulation study to test the IVS in an apartment-laboratory (29 scenarios of activities including falls); and a 28-day pre-test at home with two young occupants. The IVS’s sensitivity (Se), specificity (Sp), accuracy (A) and error rate (E) in the apartment-laboratory were calculated, and functioning at home was documented in a logbook. Findings For phase 1, results are: Se =91.67 per cent, Sp =99.02 per cent, A=98.25 per cent, E=1.75. For phase 2, the IVS triggered four false alarms and some technical dysfunctions appeared (e.g. computer screen never turning off) that are easily overcome. Practical implications Results show the IVS’s efficacy at automatically detecting falls at home. Potential issues related to future installation in older adults’ homes were identified. This proof of concept led to recommendations about the installation and calibration of a camera-based fall detection system. Originality/value This paper highlights the potentialities of a camera-based fall detection system in real-world contexts and supports the use of the IVS to help older adults age in place.

Implementing an intelligent video monitoring system to detect falls of older adults at home: a multiple case study

2020 ◽  
Vol 14 (4) ◽  
pp. 253-271
Author(s):  
Nolwenn Lapierre ◽  
Alain St-Arnaud ◽  
Jean Meunier ◽  
Jacqueline Rousseau
Keyword(s):  
Older Adults ◽  
Monitoring System ◽  
Multiple Case Study ◽  
Ease Of Use ◽  
Video Monitoring ◽  
False Alarms ◽  
Content Type ◽  
Multiple Case ◽  
At Home

Purpose Older adults are at a high risk of falling. The consequences of falls are worse when the person is unable to get up afterward. Thus, an intelligent video monitoring system (IVS) was developed to detect falls and send alerts to a respondent. This study aims to explore the implementation of the IVS at home. Design/methodology/approach A multiple case study was conducted with four dyads: older adults and informal caregivers. The IVS was implemented for two months at home. Perceptions of the IVS and technical variables were documented. Interviews were thematically analyzed, and technical data were descriptively analyzed. Findings The rate of false alarms was 0.35 per day. Participants had positive opinions of the IVS and mentioned its ease of use. They also made suggestions for improvement. Originality/value This study showed the feasibility of a two-month implementation of this IVS. Its development should be continued and tested with a larger experimental group.

Perception et réceptivité des proches-aidants à l’égard de la vidéosurveillance intelligente pour la détection des chutes des aînés à domicile

2015 ◽  
Vol 34 (4) ◽  
pp. 445-456 ◽  
Author(s):  
Nolwenn Lapierre ◽  
Chloë Proulx Goulet ◽  
Alain St-Arnaud ◽  
Francine Ducharme ◽  
Jean Meunier ◽  
...  
Keyword(s):  
Monitoring System ◽  
Mixed Method ◽  
Detection System ◽  
Fall Detection ◽  
Video Monitoring ◽  
Care Recipient ◽  
Positive Perception ◽  
Cross Sectional ◽  
Individual Interviews ◽  
System A

ABSTRACTTo address the issue of falls, which are increasing as the population ages, an intelligent video-monitoring system is being developed. The aim of the study is to explore caregivers’ perceptions of and receptiveness to a prototype of this fall detection system. A cross-sectional mixed-method study was carried out with individual interviews of 18 caregivers. Statistical frequencies and content analysis were conducted (SPSS and N’Vivo). The results show that most participants (n = 15/18) liked the intelligent video-monitoring system and were willing to use it. They would worry less if they could be alerted if a care recipient fell, but they were concerned about privacy and cost. Participants had a positive perception of the system and expressed their wishes regarding the kind of alert and the person to contact in case of a fall.

Towards a Real-Time Fall Detection System using Kinect Sensor

2016 ◽  
Vol 6 (1) ◽  
pp. 41-58 ◽  
Author(s):  
Nadia Baha ◽  
Eden Beloudah ◽  
Mehdi Ousmer
Keyword(s):  
Real Time ◽  
Detection System ◽  
Fall Detection ◽  
Microsoft Kinect ◽  
False Alarms ◽  
Depth Information ◽  
Detection Rates ◽  
Major Health Problem ◽  
Major Health ◽  
Time Fall

Falls are the major health problem among older people who live alone in their home. In the past few years, several studies have been proposed to solve the dilemma especially those which exploit video surveillance. In this paper, in order to allow older adult to safely continue living in home environments, the authors propose a method which combines two different configurations of the Microsoft Kinect: The first one is based on the person's depth information and his velocity (Ceiling mounted Kinect). The second one is based on the variation of bounding box parameters and its velocity (Frontal Kinect). Experimental results on real datasets are conducted and a comparative evaluation of the obtained results relative to the state-of-art methods is presented. The results show that the authors' method is able to accurately detect several types of falls in real-time as well as achieving a significant reduction in false alarms and improves detection rates.

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.

scholarly journals Functional dementia assessment using a video monitoring system: Proof of concept

2013 ◽  
Vol 10 (4) ◽  
Author(s):  
E. Mulin ◽  
V. Joumier ◽  
I. Leroi ◽  
J.H. Lee ◽  
J. Piano ◽  
...  
Keyword(s):  
Monitoring System ◽  
Video Monitoring ◽  
Proof Of Concept

A Realization of Face Detection System Based on ARM Linux

2013 ◽  
Vol 333-335 ◽  
pp. 864-867 ◽  
Author(s):  
Cong Ting Zhao ◽  
Hong Yun Wang ◽  
Jia Wei Li ◽  
Zi Lu Ying
Keyword(s):  
Embedded System ◽  
Monitoring System ◽  
Face Detection ◽  
Detection System ◽  
Detection Algorithm ◽  
Video Monitoring ◽  
Adaboost Algorithm ◽  
Arm Processor ◽  
The Face ◽  
Detection Effect

In order to adapt to the requirements of intelligent video monitoring system, this paper presents an ARM-Linux based video monitoring system for face detection. In this system, an ARM processor with a Linux operating system was used, and the USB camera was used to capture data, and then the face detection was conducted in the ARM device. The OpenCV library was transplanted to Linux embedded system. The algorithm of face detection was realized by calling the OpenCV library. Specially, adaboost algorithm was chose as the face detection algorithm. Experimental results show that the face detection effect of the system is satisfactory and can meet the real time requirement of video surveillance.

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