Vision-based fall detection system for improving safety of elderly people

Harrou, Fouzi; Zerrouki, Nabil; Sun, Ying; Houacine, Amrane

doi:10.1109/mim.2017.8121952

Cited by 97 publications

(36 citation statements)

References 15 publications

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“…The human contour is simplified as three basic parts of the head, body and legs, and at the same time, the orientation, sum of the heights and height ratio are calculated and used to analyze the shape of the human [17,21]. Then, a statistically determined method is used to collect a set of ratios of the human body part occupied area of each frame and the writer uses it as input data of the MEWMA chart [18][19][20]22,23]. In the above methods, their performance must depend on the extraction integrity of the contour of the human body.…”

Section: Related Workmentioning

confidence: 99%

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Human Fall Detection Based on Body Posture Spatio-Temporal Evolution

Zhang

Wang

2020

Sensors

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Abnormal falls in public places have significant safety hazards and can easily lead to serious consequences, such as trampling by people. Vision-driven fall event detection has the huge advantage of being non-invasive. However, in actual scenes, the fall behavior is rich in diversity, resulting in strong instability in detection. Based on the study of the stability of human body dynamics, the article proposes a new model of human posture representation of fall behavior, called the “five-point inverted pendulum model”, and uses an improved two-branch multi-stage convolutional neural network (M-CNN) to extract and construct the inverted pendulum structure of human posture in real-world complex scenes. Furthermore, we consider the continuity of the fall event in time series, use multimedia analytics to observe the time series changes of human inverted pendulum structure, and construct a spatio-temporal evolution map of human posture movement. Finally, based on the integrated results of computer vision and multimedia analytics, we reveal the visual characteristics of the spatio-temporal evolution of human posture under the potentially unstable state, and explore two key features of human fall behavior: motion rotational energy and generalized force of motion. The experimental results in actual scenes show that the method has strong robustness, wide universality, and high detection accuracy.

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Section: Related Workmentioning

confidence: 99%

“…Our experimental dataset called "Postures of Fall (PoF)" is different from existing fall datasets, such as the University of Rzeszow fall detection dataset (URFD) and the fall detection dataset (FDD) [23] and Multi-cam [19] in the indoor scenes. The comparison of these datasets and the proposed dataset (PoF) is shown in Table 3.…”

Section: Experimental Evaluationmentioning

confidence: 99%

Human Fall Detection Based on Body Posture Spatio-Temporal Evolution

Zhang

Wang

2020

Sensors

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“…The blend of numerical information is utilized so as to identify fall with high exactness and dependability. Fouzi Harrou et.al [2] proposed Acknowledgment of human developments is exceptionally helpful for a few applications, for example, brilliant rooms, intelligent augmented experience frameworks, human detection and condition displaying. The goal of this work centers around the identification and order of falls dependent on varieties fit as a fiddle, a key test in PC vision .the recognition is accomplished with multivariate exponentially weighted moving normal (MEWMA) checking plan, which is viable in identifying falls since it is touchy to little changes.…”

Section: Related Workmentioning

confidence: 99%

“…Most of the time injuries are due to the falling on the ground, unconscious and hurting themselves. It will cause serious injury or sometimes death [2]. Therefore, they should be urgently transported to the emergency clinic, where they will be watched and gave medicinal assistance if wellbeing condition is in danger.…”

Section: Introductionmentioning

confidence: 99%

Automatic Body Fall Detection System for Elderly People using Accelerometer and Vision Based Technique

Turkane*,

Vikhe,

Kadu

et al. 2019

IJRTE

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Body Falls in older adults are the significant cause of injury. Falls incorporate dropping from a standing position or from uncovered positions, for example, those on stepping stools or stepladders. The seriousness of damage is commonly identified with the height of fall often leading to disability or death. In this research generally we uses wearable sensor and vision based technique that is automatically detect body fall as early as possible. Accelerometer is used for measuring or maintaining orientation and angular velocity. In vision based procedure first we procure casings or video arrangements from the camera. The division module separates the body outline from the foundation.For Feature Extraction we used GLCM method. SVM method is used for classification. By using those methods we can surely detect the human body fall and can take the preventive measures.

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“…Some of other approaches resort to the video processing to identify the movements. For instance, in this context in [22] the weight and the height aspect ratio of an individual are evaluated and used, after separating the background from the object of interest. Livak et al [30] combined the extracted features from the floor accelerometer and the microphone measurements to be considered in the classifier.…”

Section: Introductionmentioning

confidence: 99%

Fall detection and human activity classification using wearable sensors and compressed sensing

Kerdjidj

Ramzan

Ghanem

et al. 2019

J Ambient Intell Human Comput

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The fall of elderly patients is still a critical medical issue since it can cause irreversible bone injuries due to the elderly bones weakness. To mitigate the likelihood of the occurrence of a fall, continuously tracking the patients with balance and health issues has been envisaged, despite being unpractical. To address this problem, we propose an efficient automatic fall detection system which is also fitted for the detection of different Activities of Daily Living (ADL). The system relies on a wearable Shimmer device, to transmit some inertial signals via a wireless connection to a computer. Aiming at reducing the size of the transmitted data and minimizing the energy consumption, a compressive sensing (CS) method is applied. In this perspective, we started by creating our dataset from 17 subjects performing a set of movements, then three distinct systems were investigated: one which detects the presence or the absence of the fall, a second which detects static or dynamic movements including the fall, and a third which recognizes the fall and six other ADL activities. In the acquisition and classification steps, first only the data collected by the accelerometer are exploited, then a mixture of the accelerometer and gyroscope measurements are taken into consideration. The two configurations are compared and the resulting system incorporating CS capabilities is shown to achieve up to 99.8% of accuracy.

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Vision-based fall detection system for improving safety of elderly people

Cited by 97 publications

References 15 publications

Human Fall Detection Based on Body Posture Spatio-Temporal Evolution

Human Fall Detection Based on Body Posture Spatio-Temporal Evolution

Automatic Body Fall Detection System for Elderly People using Accelerometer and Vision Based Technique

Fall detection and human activity classification using wearable sensors and compressed sensing

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