Supporting Independent Living for Older Adults; Employing a Visual Based Fall Detection Through Analysing the Motion and Shape of the Human Body

Lotfi, Ahmad; Albawendi, Suad; Powell, Heather M.; Appiah, Kofi; Langensiepen, Caroline

doi:10.1109/access.2018.2881237

Cited by 58 publications

(40 citation statements)

References 41 publications

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“…In [22], motion vector of human object obtained by optical flow method was used to determine if a fall occurs. In [23], high degrees of motion information obtained by timed motion history image was adopted to discriminate fall event. Suriani et al proposed a fall classification algorithm based on motion history histogram and motion geometric distribution [24].…”

Section: Related Workmentioning

confidence: 99%

Vision-Based Fall Detection Using Dense Block With Multi-Channel Convolutional Fusion Strategy

Han

2021

IEEE Access

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Fall detection has become a hot issue in the field of video surveillance recently. Different from most traditional vision-based methods relying on hand-crafted features, fall detection methods based on deep learning technology can automatically mine features to detect fall events due to the powerful ability of deep learning in data analysis, and hence have received much more attention in recent years. However, information loss has become a problem that cannot be ignored, especially for the neural networks with deep layers, because loss of information will affect representativeness of features and further influence the performance of fall detection. To solve the abovementioned problem, we propose a fall detection method based on dense block with a multi-channel convolutional fusion (MCCF) strategy. In this method, MCCF-DenseBlock, a densely connected network structure, is proposed to fully extract information with its densely connected layers, and to avoid network overloading by breaking dense connections appropriately, and especially to reduce data redundancy and numerous parameters in the network via the MCCF strategy fusing its grouped features. Besides, an improved transition layer is presented to further lessen data accumulation by using a multi-level downsampling structure. Experimental results demonstrate that, the proposed method can provide accurate fall detection results (satisfactory F-score of 0.973 on the UR Fall Dataset) and outperforms several state-of-the-art methods. INDEX TERMS Fall detection, deep learning, multi-channel convolutional fusion (MCCF), MCCF-DenseBlock.

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Section: Related Workmentioning

confidence: 99%

Vision-Based Fall Detection Using Dense Block With Multi-Channel Convolutional Fusion Strategy

Han

2021

IEEE Access

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“…It got an accuracy of 90.6%. Lotfi et al [23] used the background subtraction to extract the moving human target and then extracted the external contour, ellipse, centroid, and other characteristics of the human body. Finally, these features were fed into a MLP for fall detection.…”

Section: A Traditional Features Basedmentioning

confidence: 99%

“…The external ellipse of humans while standing is displayed in Figure 7(a), and the external ellipses during falling are illustrated in Figure 7(b), (c), and (d). The image centre moment method [23] is used for fitting the ellipse. The fitting process and formula are as follows: (2) The angle between the long axis of the ellipse and the vertical direction θ, (3) The length of the elliptical semi-major axis , (4) The length of the semi-minor axis of the ellipse.…”

Section: Figure 7 Human Body External Ellipsementioning

confidence: 99%

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Fall Detection Based on Dual-Channel Feature Integration

Wang

Kuo

et al. 2020

IEEE Access

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Falls have caught great harm to the elderly living alone at home. This paper presents a novel visual-based fall detection approach by Dual-Channel Feature Integration. The proposed approach divides the fall event into two parts: falling-state and fallen-state, which describes the fall events from dynamic and static perspectives. Firstly, the object detection model (Yolo) and the human posture detection model (OpenPose) are used for preprocessing to obtain key points and the position information of a human body. Then, a dual-channel sliding window model is designed to extract the dynamic features of the human body (centroid speed, upper limb velocity) and static features (human external ellipse). After that, MLP (Multilayer Perceptron) and Random Forest are applied to classify the dynamic and static feature data separately. Finally, the classification results are combined for fall detection. Experimental results show that the proposed approach achieves an accuracy of 97.33% and 96.91% when tested with UR Fall Detection Dataset and Le2i Fall Detection Dataset. INDEX TERMS Computer vision, fall detection, dual channel, machine learning, classification.

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“…Traditional healthcare methods are categorized into three types: computer vision-based, wearable device-based, and ambient sensor-based methods [ 9 , 10 ].…”

Section: Related Workmentioning

confidence: 99%

Danger-Pose Detection System Using Commodity Wi-Fi for Bathroom Monitoring

Zhang

Ishida

Tagashira

et al. 2019

Sensors

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A bathroom has higher probability of accidents than other rooms due to a slippery floor and temperature change. Because of high privacy and humidity, we face difficulties in monitoring inside a bathroom using traditional healthcare methods based on cameras and wearable sensors. In this paper, we present a danger-pose detection system using commodity Wi-Fi devices, which can be applied to bathroom monitoring, preserving privacy. A machine learning-based detection method usually requires data collected in target situations, which is difficult in detection-of-danger situations. We therefore employ a machine learning-based anomaly-detection method that requires a small amount of data in anomaly conditions, minimizing the required training data collected in dangerous conditions. We first derive the amplitude and phase shift from Wi-Fi channel state information (CSI) to extract low-frequency components that are related to human activities. We then separately extract static and dynamic features from the CSI changes in time. Finally, the static and dynamic features are fed into a one-class support vector machine (SVM), which is used as an anomaly-detection method, to classify whether a user is not in bathtub, bathing safely, or in dangerous conditions. We conducted experimental evaluations and demonstrated that our danger-pose detection system achieved a high detection performance in a non-line-of-sight (NLOS) scenario.

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Supporting Independent Living for Older Adults; Employing a Visual Based Fall Detection Through Analysing the Motion and Shape of the Human Body

Cited by 58 publications

References 41 publications

Vision-Based Fall Detection Using Dense Block With Multi-Channel Convolutional Fusion Strategy

Vision-Based Fall Detection Using Dense Block With Multi-Channel Convolutional Fusion Strategy

Fall Detection Based on Dual-Channel Feature Integration

Danger-Pose Detection System Using Commodity Wi-Fi for Bathroom Monitoring

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