Fall Detection Based on Key Points of Human-Skeleton Using OpenPose

Chen, Weiming; Jiang, Zijie; Guo, Hailin; Ni, Xiaowu

doi:10.3390/sym12050744

Cited by 134 publications

(76 citation statements)

References 40 publications

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“…Besides, Chen et al [ 29 ] detect human fall events from the human skeleton information by OpenPose and obtains an accuracy of 97%. To identify fall action three efficient parameters are considered here like centerline angle between human body and ground, speed of descent at the center point of the hip joint, the ratio of width and height of external rectangle surrounding the human body.…”

Section: Related Workmentioning

confidence: 99%

Classification of Indoor Human Fall Events Using Deep Learning

Sultana

Deb

Dhar

et al. 2021

Entropy

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Human fall identification can play a significant role in generating sensor based alarm systems, assisting physical therapists not only to reduce after fall effects but also to save human lives. Usually, elderly people suffer from various kinds of diseases and fall action is a very frequently occurring circumstance at this time for them. In this regard, this paper represents an architecture to classify fall events from others indoor natural activities of human beings. Video frame generator is applied to extract frame from video clips. Initially, a two dimensional convolutional neural network (2DCNN) model is proposed to extract features from video frames. Afterward, gated recurrent unit (GRU) network finds the temporal dependency of human movement. Binary cross-entropy loss function is calculated to update the attributes of the network like weights, learning rate to minimize the losses. Finally, sigmoid classifier is used for binary classification to detect human fall events. Experimental result shows that the proposed model obtains an accuracy of 99%, which outperforms other state-of-the-art models.

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Section: Related Workmentioning

confidence: 99%

Classification of Indoor Human Fall Events Using Deep Learning

Sultana

Deb

Dhar

et al. 2021

Entropy

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“…Formulas (9) and (10) indicate that the vehicle must serve all hospitals and they can only be served once;…”

Section: Mathematical Model Of the Second Problemmentioning

confidence: 99%

“…Regular training programs for all sectors of health care workers are needed, with a special focus on waste handlers. With the development of the information age, artificial algorithms have been widely used to all kinds of fields, including the public opinion polarization process [6][7][8][9], fall detection [10], analysis of user satisfaction [11], welding flame detection [12], and road networks [13,14].…”

Section: Introductionmentioning

confidence: 99%

Path Optimization of Medical Waste Transport Routes in the Emergent Public Health Event of COVID-19: A Hybrid Optimization Algorithm Based on the Immune–Ant Colony Algorithm

Liu

Chen

et al. 2020

IJERPH

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In response to the emergent public health event of COVID-19, the efficiency of transport of medical waste from hospitals to disposal stations is a worthwhile issue to study. In this paper, based on the actual situation of COVID-19 and environmental impact assessment guidelines, an immune algorithm is used to establish a location model of urban medical waste storage sites. In view of the selection of temporary storage stations and realistic transportation demand, an efficiency-of-transport model of medical waste between hospitals and temporary storage stations is established by using an ant colony–tabu hybrid algorithm. In order to specify such status, Wuhan city in Hubei Province, China—considered the first city to suffer from COVID-19—was chosen as an example of verification; the two-level model and the immune algorithm–ant colony optimization–tabu search (IA–ACO–TS) algorithm were used for simulation and testing, which achieved good verification. To a certain extent, the model and the algorithm are proposed to solve the problem of medical waste disposal, based on transit temporary storage stations, which we are convinced will have far-reaching significance for China and other countries to dispatch medical waste in response to such public health emergencies.

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“…After that, particle swarm optimization (PSO) [ 3 ], artificial fish swarm algorithm (AFSA) [ 4 ], artificial bee colony algorithm (ABC) [ 5 ], and other algorithms based on biological foraging were proposed by scholars, and the research on swarm intelligence was gradually deepened. With the development of the information age, those algorithms have been widely used in various kinds of fields, including the public opinion polarization process [ 6 , 7 , 8 , 9 ], fall detection [ 10 ], analysis of user satisfaction [ 11 ], welding flame detection [ 12 ], and road networks [ 13 , 14 ].…”

Section: Introductionmentioning

confidence: 99%

How to Improve Urban Intelligent Traffic? A Case Study Using Traffic Signal Timing Optimization Model Based on Swarm Intelligence Algorithm

Gao

Cai

et al. 2021

Sensors

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Traffic congestion is a major problem in today’s society, and the intersection, as an important hub of urban traffic, is one of the most common places to produce traffic congestion. To alleviate the phenomenon of congestion at urban traffic intersections and relieve the traffic pressure at intersections, this paper takes the traffic flow at intersections as the research object and adopts the swarm intelligent algorithm to establish an optimization model of intersection traffic signal timing, which takes the average delay time of vehicles, the average number of stops of vehicles and the traffic capacity as the evaluation indexes. This model adjusts the intersection traffic signal timing intelligence according to the real-time traffic flow and carries out simulation experiments with MATLAB. Compared with the traditional timing schemes, the average delay time of vehicles is reduced by 10.25%, the average number of stops of vehicles is reduced by 24.55%, and the total traffic capacity of the intersection is increased by 3.56%, which verifies that the scheme proposed in this paper is effective in relieving traffic congestion.

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Fall Detection Based on Key Points of Human-Skeleton Using OpenPose

Cited by 134 publications

References 40 publications

Classification of Indoor Human Fall Events Using Deep Learning

Classification of Indoor Human Fall Events Using Deep Learning

Path Optimization of Medical Waste Transport Routes in the Emergent Public Health Event of COVID-19: A Hybrid Optimization Algorithm Based on the Immune–Ant Colony Algorithm

How to Improve Urban Intelligent Traffic? A Case Study Using Traffic Signal Timing Optimization Model Based on Swarm Intelligence Algorithm

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