Fall detection using supervised machine learning algorithms: A comparative study

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

doi:10.1109/icmic.2016.7804195

Cited by 38 publications

(27 citation statements)

References 13 publications

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“…Hence, we will discuss the results obtained for each dataset: URFD (i) Harrou et al [24] and [23] used both URFD and FDD but do not specify which dataset was used to obtain their results or how they combined the performance on both datasets. For these reasons, these works are not included in the comparison tables.…”

Section: Results and Comparison With The State Of The Artmentioning

confidence: 99%

“…This is the case, for example, of Charfi et al [17], who extracted 14 features, applied some transformations to them (the first and second derivatives, the Fourier transform, and the Wavelet transform), and used a SVM to do the classification step. [23] computed occupancy areas around the body's gravity center, extracted their angles, and fed them into various classifiers, being the SVM the one which obtained the best results. In 2017, the same author extended his previous work by adding Curvelet coefficients as extra features and applying a Hidden Markov Model (HMM) to model the different body poses [14].…”

Section: Related Workmentioning

confidence: 99%

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Vision-Based Fall Detection with Convolutional Neural Networks

Núñez-Marcos

Azkune

Arganda‐Carreras

2017

Wireless Communications and Mobile Computing

215

125

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One of the biggest challenges in modern societies is the improvement of healthy aging and the support to older persons in their daily activities. In particular, given its social and economic impact, the automatic detection of falls has attracted considerable attention in the computer vision and pattern recognition communities. Although the approaches based on wearable sensors have provided high detection rates, some of the potential users are reluctant to wear them and thus their use is not yet normalized. As a consequence, alternative approaches such as vision-based methods have emerged. We firmly believe that the irruption of the Smart Environments and the Internet of Things paradigms, together with the increasing number of cameras in our daily environment, forms an optimal context for vision-based systems. Consequently, here we propose a vision-based solution using Convolutional Neural Networks to decide if a sequence of frames contains a person falling. To model the video motion and make the system scenario independent, we use optical flow images as input to the networks followed by a novel three-step training phase. Furthermore, our method is evaluated in three public datasets achieving the state-of-the-art results in all three of them.

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Section: Results and Comparison With The State Of The Artmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Vision-Based Fall Detection with Convolutional Neural Networks

Núñez-Marcos

Azkune

Arganda‐Carreras

2017

Wireless Communications and Mobile Computing

215

125

View full text Add to dashboard Cite

show abstract

“…From the perspective of feature extraction, existing solutions can be divided into two categories: static features and dynamic features. Static feature analysis is essentially a morphological model of fall behavior [14][15][16][17][18][19][20]. Typical examples are the shape matching cost defined by the full Procrustes distance [14] and the application of an approximate ellipse [19] to describe the shape of human body.…”

Section: Related Workmentioning

confidence: 99%

“…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%

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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“…These features are invariant to translation and scaling and take into account the rotation information needed for human activity classification [32]. The extracted features are descriptive enough to represent human postures and not too computationally complex permitting a fast treatment.…”

Section: Feature Extractionmentioning

confidence: 99%

Vision-Based Human Action Classification Using Adaptive Boosting Algorithm

et al. 2018

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Abstract-Precise recognition of human action is a key enabler for the development of many applications including autonomous robots for medical diagnosis and surveillance of elderly people in home environment. This paper addresses the human action recognition based on variation in body shape. Specifically, we divide the human body into five partitions that correspond to five partial occupancy areas. For each frame, we calculated area ratios and used them as input data for recognition stage. Here, we consider six classes of activities namely: walking, standing, bending, lying, squatting, and sitting. In this paper, we proposed an efficient human action recognition scheme, which takes advantages of superior discrimination capacity of AdaBoost algorithm. We validated the effectiveness of this approach by using experimental data from two publicly available databases fall detection databases from the University of Rzeszow's and the Universidad de Málaga fall detection datasets. We provided comparisons of the proposed approach with state-of-the-art classifiers based on the neural network, K-nearest neighbor, support vector machine and naïve Bayes and showed that we achieve better results in discriminating human gestures.

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Fall detection using supervised machine learning algorithms: A comparative study

Cited by 38 publications

References 13 publications

Vision-Based Fall Detection with Convolutional Neural Networks

Vision-Based Fall Detection with Convolutional Neural Networks

Human Fall Detection Based on Body Posture Spatio-Temporal Evolution

Vision-Based Human Action Classification Using Adaptive Boosting Algorithm

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