Autonomous Fall Detection With Wearable Cameras by Using Relative Entropy Distance Measure

Özcan, Koray; Velipasalar, Senem; Varshney, Pramod K.

doi:10.1109/thms.2016.2620904

Cited by 44 publications

(26 citation statements)

References 27 publications

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“…A fall detection system embedded in sensing equipment is designed. Ozcan et al [13] placed a sensor camera on the human body to determine whether the fall occurred or not by comparing the changes of the angle of view. Ejupi et al [14] designed a wearable device based on the wavelet algorithm to detect and evaluate the motion process from sitting-to-station and the risk of old people falling.…”

Section: A Methods Based On Wearable Devicesmentioning

confidence: 99%

A Two-Stream Approach to Fall Detection With MobileVGG

et al. 2020

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The existing deep learning methods for human fall detection have difficulties to distinguish falls from similar daily activities such as lying down because of not using the 3D network. Meanwhile, they are not suitable for mobile devices because they are heavyweight methods and consume a large number of memories. In order to alleviate these problems, a two-stream approach to fall detection with the MobileVGG is proposed in this paper. One stream is based on the motion characteristics of the human body for detection of falls, while the other is an improved lightweight VGG network, named the MobileVGG, put forward in the paper. The MobileVGG is constructed as a lightweight network model through replacing the traditional convolution with a simplified and efficient combination of point convolution, depth convolution and point convolution. The residual connection between layers is designed to overcome the gradient disappeared and the obstruction of gradient reflux in the deep model. The experimental results show that the proposed two-stream lightweight fall classification model outperforms the existing methods in distinguishing falls from similar daily activities such as lying and reducing the occupied memory. Therefore, it is suitable for mobile devices. INDEX TERMS Deep learning, fall detection, motion characteristics, the two-stream model, the MobileVGG.

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Section: A Methods Based On Wearable Devicesmentioning

confidence: 99%

A Two-Stream Approach to Fall Detection With MobileVGG

et al. 2020

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“…For wearable-camera methods, Ozcan et al [33] used a reverse approach by taking a completely different view compared with the existing vision-based systems. The system employed a modified version of the histograms of oriented gradients (HOG) approach together with gradient local binary patterns (GLBP), and it detected falls by the change in the dissimilarity distances obtained by HOG and GLBP.…”

Section: Image-based Methodsmentioning

confidence: 99%

An Image-Based Fall Detection System for the Elderly

Chu

2018

Applied Sciences

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Due to advances in medical technology, the elderly population has continued to grow. Elderly healthcare issues have been widely discussed—especially fall accidents—because a fall can lead to a fracture and have serious consequences. Therefore, the effective detection of fall accidents is important for both elderly people and their caregivers. In this work, we designed an Image-based FAll Detection System (IFADS) for nursing homes, where public areas are usually equipped with surveillance cameras. Unlike existing fall detection algorithms, we mainly focused on falls that occur while sitting down and standing up from a chair, because the two activities together account for a higher proportion of falls than forward walking. IFADS first applies an object detection algorithm to identify people in a video frame. Then, a posture recognition method is used to keep tracking the status of the people by checking the relative positions of the chair and the people. An alarm is triggered when a fall is detected. In order to evaluate the effectiveness of IFADS, we not only simulated different fall scenarios, but also adopted YouTube and Giphy videos that captured real falls. Our experimental results showed that IFADS achieved an average accuracy of 95.96%. Therefore, IFADS can be used by nursing homes to improve the quality of residential care facilities.

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“…It is found the resulting RE detector is insensitive to statistical dependence among the sensors and achieves good performance despite of pulse type. Due to its superiority, the RE has also found wide application in other fields than the underwater pulse detection [17], [18].…”

Section: Introductionmentioning

confidence: 99%

“…The STFT of packet 7, where the subgraphs represent the pulses detected at(18,10) and (85,50) by the VSRE detector.…”

mentioning

confidence: 99%

Variable Scale Relative Entropy Detection for Non-Cooperative Underwater Acoustic Pulse Signals

2020

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This work investigates the detection of non-cooperative underwater acoustic pulse signals at low signal-to-noise ratio (SNR). A variable scale relative entropy (VSRE) pulse signal detection scheme is proposed. Different from conventional relative entropy (RE) method where an observation sequence is processed at a given scale for detection, the proposed scheme performs the processing at multiple different scales. As a result, the original RE difference vector becomes a RE difference matrix, each column corresponding to a particular scale. Before a decision is made, the VSRE difference matrix is post-processed for improved fidelity. The non-zero elements of the resulting VSRE difference matrix are divided into groups, each corresponding to a pulse signal, based on their occurrence times within the observation sequence. Within each group, the one with the maximum RE difference is chosen to determine the exact appearance time and duration of the pulse signal. The performance gain of the VSRE detector over existing RE detector has been verified by both simulation and at-sea experimental results. INDEX TERMS Non-cooperative detection, underwater acoustic pulse signals, variable scale relative entropy.

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Autonomous Fall Detection With Wearable Cameras by Using Relative Entropy Distance Measure

Cited by 44 publications

References 27 publications

A Two-Stream Approach to Fall Detection With MobileVGG

A Two-Stream Approach to Fall Detection With MobileVGG

An Image-Based Fall Detection System for the Elderly

Variable Scale Relative Entropy Detection for Non-Cooperative Underwater Acoustic Pulse Signals

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