Fall Detection Framework for Smart Home

Paneerselvam, Athimahendran; Yaakob, Razali; Perumal, Thinagaran; Marlisah, Erzam

doi:10.1109/gcce.2018.8574617

Cited by 5 publications

(2 citation statements)

References 10 publications

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“…Any wearable platform like spectacles on the eyes, helmet on the head, and gear around the waist can build up an anti-fall system. This system should be transparent to fuse with a smart environment like smart health care and smart home system [18], [19]. This intelligent system can be an integral part of information and communications technology (ICT)-based healthcare solutions for elderly people in the smart home concept similar to [20].…”

Section: Introductionmentioning

confidence: 99%

EdgeFall: a promising cloud-edge-end architecture for elderly fall care

Shahiduzzaman

Yusuf

2023

IJECE

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<p>Elder citizens face sudden fall, which can lead to injuries of both destructive and non-virulent. These sudden falls are later more precarious than diseases like heart attack, blood sugar, blood pressure because these can be untreated for a lengthy time which can lead to death. Elder citizen who experiences a precipitous fall, carry out their communal life narrowed. Therefore, a shrewd and adequate anti-fallen system is required for aiding elderly health care, specifically to those who live individually. So, it can identify and anticipate a precipitous fall through appropriate human activity recognition. In this study, we have suggested an end-edge-cloud based wearable EdgeFall architecture for elderly care. We have performed simulation setups to clarify the query of why we need such a strategy, and its validity. We have achieved maximum 91.87% accuracy with 1.6% false alarm rate (FAR). These empirical results indicate the superiority of using tightly couple multiple information for recognizing human activity. We can accomplish a low FAR with an enhanced accuracy. We can observe that our proposed end-edge-cloud based architecture can reduce the execution time to millisecond range (ms) of 14.16 to 15.74. This work serves as the starting mark for future related research activities.</p>

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Section: Introductionmentioning

confidence: 99%

EdgeFall: a promising cloud-edge-end architecture for elderly fall care

Shahiduzzaman

Yusuf

2023

IJECE

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“…At present, there are also studies that implement the fall detection algorithm in the edge computing platform and integrate it into the smart home environment [19][20][21]. The method proposed in [22] deploys the network model to the edge computing platform, reports fall events through WiFi, and performs fall detection while protecting user privacy.…”

Section: Introductionmentioning

confidence: 99%

Fall detection based on OpenPose and MobileNetV2 network

Gao

Zhou

et al. 2022

IET Image Processing

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The proposed fall detection approach is aimed at building a support system for the elders. In this work, a method based on human pose estimation and lightweight neural network is used to detect falls. First, the OpenPose is used to extract human keypoints and label them in the images. After that, the modified MobileNetV2 network is used to detect falls by integrating both human keypoint information and pose information in the original images. The above operation can use the original image information to correct the deviation in the keypoint labeling process. Through experiments, the accuracy of the proposed method is 98.6% and 99.75% on the UR and Le2i datasets, which is higher than the listed comparison methods.

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A Real Time Fall Detection System Using Tri-Axial Accelerometer and Clinometer Based on Smart Phones

Twu

2019

IFMBE Proceedings

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Fall Detection Framework for Smart Home

Cited by 5 publications

References 10 publications

EdgeFall: a promising cloud-edge-end architecture for elderly fall care

EdgeFall: a promising cloud-edge-end architecture for elderly fall care

Fall detection based on OpenPose and MobileNetV2 network

A Real Time Fall Detection System Using Tri-Axial Accelerometer and Clinometer Based on Smart Phones

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