A vision-based approach for fall detection using multiple cameras and convolutional neural networks: A case study using the UP-Fall detection dataset

Computers in Biology and Medicine ◽

10.1016/j.compbiomed.2019.103520 ◽

2019 ◽

Vol 115 ◽

pp. 103520 ◽

Cited By ~ 6

Author(s):

Ricardo Espinosa ◽

Hiram Ponce ◽

Sebastián Gutiérrez ◽

Lourdes Martínez-Villaseñor ◽

Jorge Brieva ◽

...

Keyword(s):

Neural Networks ◽

Convolutional Neural Networks ◽

Fall Detection ◽

Multiple Cameras

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Application of Convolutional Neural Networks for Fall Detection Using Multiple Cameras

Challenges and Trends in Multimodal Fall Detection for Healthcare - Studies in Systems, Decision and Control ◽

10.1007/978-3-030-38748-8_5 ◽

2020 ◽

pp. 97-120

Author(s):

Ricardo Espinosa ◽

Hiram Ponce ◽

Sebastián Gutiérrez ◽

Lourdes Martínez-Villaseñor ◽

Jorge Brieva ◽

...

Keyword(s):

Neural Networks ◽

Convolutional Neural Networks ◽

Fall Detection ◽

Multiple Cameras

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Contactless Fall Detection using Time-Frequency Analysis and Convolutional Neural Networks

IEEE Transactions on Industrial Informatics ◽

10.1109/tii.2021.3049342 ◽

2021 ◽

pp. 1-1

Author(s):

Hamidreza Sadreazami ◽

Miodrag Bolic ◽

Sreeraman Rajan

Keyword(s):

Neural Networks ◽

Convolutional Neural Networks ◽

Frequency Analysis ◽

Fall Detection ◽

Time Frequency Analysis ◽

Time Frequency

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Enhanced Active Learning of Convolutional Neural Networks: A Case Study for Defect Classification in the Semiconductor Industry

Proceedings of the 12th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management ◽

10.5220/0010142902630270 ◽

2020 ◽

Author(s):

Georgios Koutroulis ◽

Tiago Santos ◽

Michael Wiedemann ◽

Christian Faistauer ◽

Roman Kern ◽

...

Keyword(s):

Neural Networks ◽

Active Learning ◽

Convolutional Neural Networks ◽

Semiconductor Industry ◽

Defect Classification

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Fall detection for elderly person care using convolutional neural networks

2017 10th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI) ◽

10.1109/cisp-bmei.2017.8302004 ◽

2017 ◽

Cited By ~ 8

Author(s):

Xiaogang Li ◽

Tiantian Pang ◽

Weixiang Liu ◽

Tianfu Wang

Keyword(s):

Neural Networks ◽

Convolutional Neural Networks ◽

Elderly Person ◽

Fall Detection

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Deep convolutional neural networks as a decision support tool in medical problems – malignant melanoma case study

Advances in Intelligent Systems and Computing - Trends in Advanced Intelligent Control, Optimization and Automation ◽

10.1007/978-3-319-60699-6_81 ◽

2017 ◽

pp. 848-856 ◽

Cited By ~ 9

Author(s):

Arkadiusz Kwasigroch ◽

Agnieszka Mikołajczyk ◽

Michał Grochowski

Keyword(s):

Neural Networks ◽

Decision Support ◽

Malignant Melanoma ◽

Convolutional Neural Networks ◽

Decision Support Tool ◽

Deep Convolutional Neural Networks ◽

Medical Problems ◽

Support Tool ◽

Melanoma Case

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Human Fall Detection in Depth-Videos Using Temporal Templates and Convolutional Neural Networks

10.1007/978-3-030-87132-1_10 ◽

2021 ◽

pp. 217-236

Author(s):

Earnest Paul Ijjina

Keyword(s):

Neural Networks ◽

Convolutional Neural Networks ◽

Fall Detection

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Convolutional neural networks based focal loss for class imbalance problem: a case study of canine red blood cells morphology classification

Journal of Ambient Intelligence and Humanized Computing ◽

10.1007/s12652-020-01773-x ◽

2020 ◽

Cited By ~ 6

Author(s):

Kitsuchart Pasupa ◽

Supawit Vatathanavaro ◽

Suchat Tungjitnob

Keyword(s):

Neural Networks ◽

Red Blood Cells ◽

Convolutional Neural Networks ◽

Blood Cells ◽

Class Imbalance ◽

Class Imbalance Problem ◽

Imbalance Problem

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How Deep Should be the Depth of Convolutional Neural Networks: a Backyard Dog Case Study

Cognitive Computation ◽

10.1007/s12559-019-09667-7 ◽

2019 ◽

Vol 12 (2) ◽

pp. 388-397 ◽

Cited By ~ 3

Author(s):

Alexander N. Gorban ◽

Evgeny M. Mirkes ◽

Ivan Y. Tyukin

Keyword(s):

Neural Networks ◽

Convolutional Neural Networks

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Convolutional Neural Networks Tracker with Deterministic Sampling for Sudden Fall Detection

2019 IEEE 9th International Conference on System Engineering and Technology (ICSET) ◽

10.1109/icsengt.2019.8906505 ◽

2019 ◽

Cited By ~ 1

Author(s):

Nur Ayuni Mohamed ◽

Mohd Asyraf Zulkifley ◽

Nor Azwan Mohamed Kamari

Keyword(s):

Neural Networks ◽

Convolutional Neural Networks ◽

Fall Detection ◽

Deterministic Sampling

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Enhanced Active Learning of Convolutional Neural Networks: A Case Study for Defect Classification in the Semiconductor Industry

Proceedings of the 12th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management ◽

10.5220/0010142902690276 ◽

2020 ◽

Author(s):

Georgios Koutroulis ◽

Tiago Santos ◽

Michael Wiedemann ◽

Christian Faistauer ◽

Roman Kern ◽

...

Keyword(s):

Neural Networks ◽

Active Learning ◽

Convolutional Neural Networks ◽

Semiconductor Industry ◽

Defect Classification

Download Full-text