Table2_reviewed_works.bib

@INPROCEEDINGS{serpa2020evaluating,
  author={Serpa, Ygor Rebouças and Nogueira, Matheus Batista and Neto, Pedro Paulo Macêdo and Rodrigues, Maria Andréia Formico},
  booktitle={2020 IEEE 8th International Conference on Serious Games and Applications for Health (SeGAH)}, 
  title={Evaluating Pose Estimation as a Solution to the Fall Detection Problem}, 
  year={2020},
  volume={},
  number={},
  pages={1-7},
  keywords={Feature extraction;Pose estimation;Neural networks;Cameras;Acceleration;Neurons;Sensors;fall detection;neural networks;pose estimation;evaluation},
  doi={10.1109/SeGAH49190.2020.9201701}}


@article{ramirez2021fall,
  title={Fall detection and activity recognition using human skeleton features},
  author={Ramirez, Heilym and Velastin, Sergio A and Meza, Ignacio and Fabregas, Ernesto and Makris, Dimitrios and Farias, Gonzalo},
  journal={Ieee Access},
  volume={9},
  pages={33532--33542},
  year={2021},
  publisher={IEEE}
}

@article{ramirez2022human,
  title={Human activity recognition by sequences of skeleton features},
  author={Ramirez, Heilym and Velastin, Sergio A and Aguayo, Paulo and Fabregas, Ernesto and Farias, Gonzalo},
  journal={Sensors},
  volume={22},
  number={11},
  pages={3991},
  year={2022},
  publisher={MDPI}
}

@article{alaouiFallDetectionElderly2019,
  title={Fall detection for elderly people using the variation of key points of human skeleton},
  author={Alaoui, Abdessamad Youssfi and El Fkihi, Sanaa and Thami, Rachid Oulad Haj},
  journal={IEEE Access},
  volume={7},
  pages={154786--154795},
  year={2019},
  publisher={IEEE}
}

@article{chen2020fall,
    title     = {Fall detection based on key points of human-skeleton using openpose},
    author    = {Chen, Weiming and Jiang, Zijie and Guo, Hailin and Ni, Xiaoyang},
    journal   = {Symmetry},
    volume    = {12},
    number    = {5},
    pages     = {744},
    year      = {2020},
    publisher = {Multidisciplinary Digital Publishing Institute}
}

@article{wang2020fall,
    title     = {Fall detection based on dual-channel feature integration},
    author    = {Wang, Bo-Hua and Yu, Jie and Wang, Kuo and Bao, Xuan-Yu and Mao, Ke-Ming},
    journal   = {IEEE Access},
    volume    = {8},
    pages     = {103443--103453},
    year      = {2020},
    publisher = {IEEE}
}

@inproceedings{dentamaro2021fall,
    title        = {Fall detection by human pose estimation and kinematic theory},
    author       = {Dentamaro, Vincenzo and Impedovo, Donato and Pirlo, Giuseppe},
    booktitle    = {2020 25th international conference on pattern recognition (ICPR)},
    pages        = {2328--2335},
    year         = {2021},
    organization = {IEEE}
}

@article{liu2021automatic,
    title     = {Automatic fall risk detection based on imbalanced data},
    author    = {Liu, Yen-Hung and Hung, Patrick CK and Iqbal, Farkhund and Fung, Benjamin CM},
    journal   = {IEEE Access},
    volume    = {9},
    pages     = {163594--163611},
    year      = {2021},
    publisher = {IEEE}
}

@article{zhang2022visual,
    title     = {Visual surveillance for human fall detection in healthcare IoT},
    author    = {Zhang, Yinlong and Zheng, Xiaoyan and Liang, Wei and Zhang, Sichao and Yuan, Xudong},
    journal   = {IEEE MultiMedia},
    volume    = {29},
    number    = {1},
    pages     = {36--46},
    year      = {2022},
    publisher = {IEEE}
}

@inproceedings{hasanRobustPoseBasedHuman2019,
    title        = {Robust pose-based human fall detection using recurrent neural network},
    author       = {Hasan, Md Mahedi and Islam, Md Shamimul and Abdullah, Sohaib},
    booktitle    = {2019 IEEE International Conference on Robotics, Automation, Artificial-intelligence and Internet-of-Things (RAAICON)},
    pages        = {48--51},
    year         = {2019},
    organization = {IEEE}
}
@inproceedings{jeongHumanskeletonBasedFallDetection2019,
    title      = {Human-Skeleton Based {{Fall-Detection Method}} Using {{LSTM}} for {{Manufacturing Industries}}},
    booktitle  = {2019 34th {{International Technical Conference}} on {{Circuits}}/{{Systems}}, {{Computers}} and {{Communications}} ({{ITC-CSCC}})},
    author     = {Jeong, Sungil and Kang, Sungjoo and Chun, Ingeol},
    year       = {2019},
    month      = jun,
    pages      = {1--4},
    publisher  = {{IEEE}},
    address    = {{JeJu, Korea (South)}},
    doi        = {10.1109/ITC-CSCC.2019.8793342},
    abstract   = {According to the statistics of the Korea Occupational Safety \& Health Agency, the incidences of falls in the manufacturing industry are increasing. In this paper, we introduce a fall-detection method based on skeleton data obtained from a 2D RGB CCTV Camera installed on the manufacturing floor. We proposed feature-extraction methods to improve of fall-detection accuracy and the construction of a fall-detection system using LSTM. Experiments were conducted through public datasets (URFD and SDUFall) to find feature-extraction methods that can achieve high classification accuracy. The experimental results showed that the proposed method is more effective in detecting falls than raw skeleton data which are not processed anything.},
    isbn       = {978-1-72813-271-6},
    langid     = {english},
    keywords   = {1. keypaper,data-prepare,urfall},
    annotation = {7 citations (Crossref) [2022-07-29]},
    file       = {C\:\\Users\\ha\\Zotero\\storage\\S26XKV6X\\Jeong et al. - 2019 - Human-skeleton based Fall-Detection Method using L.pdf}
}

@article{lin2020framework,
    title     = {A framework for fall detection based on openpose skeleton and lstm/gru models},
    author    = {Lin, Chuan-Bi and Dong, Ziqian and Kuan, Wei-Kai and Huang, Yung-Fa},
    journal   = {Applied Sciences},
    volume    = {11},
    number    = {1},
    pages     = {329},
    year      = {2020},
    publisher = {MDPI}
}

@article{chang2021pose,
    title     = {A pose estimation-based fall detection methodology using artificial intelligence edge computing},
    author    = {Chang, Wan-Jung and Hsu, Chia-Hao and Chen, Liang-Bi},
    journal   = {IEEE Access},
    volume    = {9},
    pages     = {129965--129976},
    year      = {2021},
    publisher = {IEEE}
}

@article{galvao2021framework,
    title     = {A framework for anomaly identification applied on fall detection},
    author    = {Galvao, Yves M and Portela, Let{\'\i}cia and Ferreira, Janderson and Barros, Pablo and Fagundes, Oberta Andrade De Ara{\'u}jo and Fernandes, Bruno JT},
    journal   = {IEEE Access},
    volume    = {9},
    pages     = {77264--77274},
    year      = {2021},
    publisher = {IEEE}
}

@article{huu2021proposing,
    title     = {Proposing posture recognition system combining MobilenetV2 and LSTM for medical surveillance},
    author    = {Huu, Phat Nguyen and Thi, Ngoc Nguyen and Ngoc, Thien Pham},
    journal   = {IEEE Access},
    volume    = {10},
    pages     = {1839--1849},
    year      = {2021},
    publisher = {IEEE}
}

@inproceedings{chen2022video,
    title        = {Video based fall detection using human poses},
    author       = {Chen, Ziwei and Wang, Yiye and Yang, Wankou},
    booktitle    = {Big Data: 9th CCF Conference, BigData 2021, Guangzhou, China, January 8--10, 2022, Revised Selected Papers},
    pages        = {283--296},
    year         = {2022},
    organization = {Springer}
}

@article{inturi2022novel,
    title     = {A Novel Vision-Based Fall Detection Scheme Using Keypoints of Human Skeleton with Long Short-Term Memory Network},
    author    = {Inturi, Anitha Rani and Manikandan, VM and Garrapally, Vignesh},
    journal   = {Arabian Journal for Science and Engineering},
    pages     = {1--13},
    year      = {2022},
    publisher = {Springer}
}

@article{juraev2022exploring,
    title     = {Exploring Human Pose Estimation and the Usage of Synthetic Data for Elderly Fall Detection in Real-World Surveillance},
    author    = {Juraev, Sardor and Ghimire, Akash and Alikhanov, Jumabek and Kakani, Vijay and Kim, Hakil},
    journal   = {IEEE Access},
    volume    = {10},
    pages     = {94249--94261},
    year      = {2022},
    publisher = {IEEE}
}

@article{lau2022fall,
    title     = {Fall Detection and Motion Analysis Using Visual Approaches},
    author    = {Lau, Xin Lin and Tee, Connie and Goh, Michael Kah Ong and Lau, Siong Hoe},
    journal   = {International Journal of Technology},
    volume    = {13},
    number    = {6},
    pages     = {1173},
    year      = {2022},
    publisher = {IJTech}
}

@article{li2022kamtfenet,
    title     = {KAMTFENet: a fall detection algorithm based on keypoint attention module and temporal feature extraction},
    author    = {Li, Jiangjiao and Gao, Mengqi and Li, Bin and Zhou, Dazheng and Zhi, Yumin and Zhang, Youmei},
    journal   = {International Journal of Machine Learning and Cybernetics},
    pages     = {1--14},
    year      = {2022},
    publisher = {Springer}
}

@inproceedings{suarez2022afar,
    title     = {AFAR: a real-time vision-based activity monitoring and fall detection framework using 1D convolutional neural networks},
    author    = {Suarez, Jessie James Profeta and Orillaza, Nathaniel and Naval, Prospero},
    booktitle = {2022 14th International Conference on Machine Learning and Computing (ICMLC)},
    pages     = {555--559},
    year      = {2022}
}

@article{yadav2022arfdnet,
    title     = {ARFDNet: An efficient activity recognition \& fall detection system using latent feature pooling},
    author    = {Yadav, Santosh Kumar and Luthra, Achleshwar and Tiwari, Kamlesh and Pandey, Hari Mohan and Akbar, Shaik Ali},
    journal   = {Knowledge-Based Systems},
    volume    = {239},
    pages     = {107948},
    year      = {2022},
    publisher = {Elsevier}
}

@inproceedings{yuan2022real,
    title        = {Real-Time Human Falling Recognition via Spatial and Temporal Self-Attention Augmented Graph Convolutional Network},
    author       = {Yuan, Jiayao and Liu, Chengju and Liu, Chuangwei and Wang, Liuyi and Chen, Qijun},
    booktitle    = {2022 IEEE International Conference on Real-time Computing and Robotics (RCAR)},
    pages        = {438--443},
    year         = {2022},
    organization = {IEEE}
}

@article{zahan2022sdfa,
    title     = {SDFA: Structure Aware Discriminative Feature Aggregation for Efficient Human Fall Detection in Video},
    author    = {Zahan, Sania and Hassan, Ghulam Mubashar and Mian, Ajmal},
    journal   = {IEEE Transactions on Industrial Informatics},
    year      = {2022},
    publisher = {IEEE}
}

@article{amsaprabhaa2023multimodal,
    title     = {Multimodal spatiotemporal skeletal kinematic gait feature fusion for vision-based fall detection},
    author    = {Amsaprabhaa, M and others},
    journal   = {Expert Systems with Applications},
    volume    = {212},
    pages     = {118681},
    year      = {2023},
    publisher = {Elsevier}
}

@article{ramirez2023bert,
    title     = {BERT for Activity Recognition Using Sequences of Skeleton Features and Data Augmentation with GAN},
    author    = {Ramirez, Heilym and Velastin, Sergio A and Cuellar, Sara and Fabregas, Ernesto and Farias, Gonzalo},
    journal   = {Sensors},
    volume    = {23},
    number    = {3},
    pages     = {1400},
    year      = {2023},
    publisher = {MDPI}
}