Artigos de revistas sobre o tema "Human Fall detection"
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Zhang, Duo, Xusheng Zhang, Shengjie Li, Yaxiong Xie, Yang Li, Xuanzhi Wang e Daqing Zhang. "LT-Fall". Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 7, n.º 1 (27 de março de 2022): 1–24. http://dx.doi.org/10.1145/3580835.
Texto completo da fonteSarthak Turki, Et al. "A Machine Learning Classification Paradigm for Automated Human Fall Detection". International Journal on Recent and Innovation Trends in Computing and Communication 11, n.º 10 (2 de novembro de 2023): 1169–76. http://dx.doi.org/10.17762/ijritcc.v11i10.8638.
Texto completo da fonteSahithi, Challa, Chennepalli HimaBindu, Harika C e Jyothi M C. "Fall Detection". International Research Journal of Computer Science 10, n.º 04 (31 de maio de 2023): 85–87. http://dx.doi.org/10.26562/irjcs.2023.v1004.09.
Texto completo da fonteZheng, Kun, Bin Li, Yu Li, Peng Chang, Guangmin Sun, Hui Li e Junjie Zhang. "Fall detection based on dynamic key points incorporating preposed attention". Mathematical Biosciences and Engineering 20, n.º 6 (2023): 11238–59. http://dx.doi.org/10.3934/mbe.2023498.
Texto completo da fonteShrivastava, Rashmi, e Manju Pandey. "Human Fall Detection Using Efficient Kernel and Eccentric Approach". International Journal of E-Health and Medical Communications 12, n.º 1 (janeiro de 2021): 62–80. http://dx.doi.org/10.4018/ijehmc.2021010105.
Texto completo da fonteZi, Xing, Kunal Chaturvedi, Ali Braytee, Jun Li e Mukesh Prasad. "Detecting Human Falls in Poor Lighting: Object Detection and Tracking Approach for Indoor Safety". Electronics 12, n.º 5 (6 de março de 2023): 1259. http://dx.doi.org/10.3390/electronics12051259.
Texto completo da fonteRibeiro, Osvaldo, Luis Gomes e Zita Vale. "IoT-Based Human Fall Detection System". Electronics 11, n.º 4 (15 de fevereiro de 2022): 592. http://dx.doi.org/10.3390/electronics11040592.
Texto completo da fonteKan, Xi, Shenghao Zhu, Yonghong Zhang e Chengshan Qian. "A Lightweight Human Fall Detection Network". Sensors 23, n.º 22 (9 de novembro de 2023): 9069. http://dx.doi.org/10.3390/s23229069.
Texto completo da fonteMartínez-Villaseñor, Lourdes, Hiram Ponce e Ricardo Abel Espinosa-Loera. "Multimodal Database for Human Activity Recognition and Fall Detection". Proceedings 2, n.º 19 (22 de outubro de 2018): 1237. http://dx.doi.org/10.3390/proceedings2191237.
Texto completo da fonteAbduljabbar Ali, Mohammed, Abir Jaafar Hussain e Ahmed T. Sadiq. "Human Fall Down Recognition Using Coordinates Key Points Skeleton". International Journal of Online and Biomedical Engineering (iJOE) 18, n.º 02 (16 de fevereiro de 2022): 88–104. http://dx.doi.org/10.3991/ijoe.v18i02.28017.
Texto completo da fonteKavya G, Sunil Kumar C T, Dhanush C e Kruthika J. "Human Fall Detection Using Video Surveillance". ACS Journal for Science and Engineering 1, n.º 1 (12 de março de 2021): 1–10. http://dx.doi.org/10.34293/acsjse.v1i1.1.
Texto completo da fonteP, Nishanth. "Machine Learning based Human Fall Detection System". International Journal for Research in Applied Science and Engineering Technology 9, n.º VI (25 de junho de 2021): 2677–82. http://dx.doi.org/10.22214/ijraset.2021.35394.
Texto completo da fonteYuan, Chunmiao, Pengju Zhang, Qingyong Yang e Jianming Wang. "Fall Detection and Direction Judgment Based on Posture Estimation". Discrete Dynamics in Nature and Society 2022 (15 de junho de 2022): 1–12. http://dx.doi.org/10.1155/2022/8372291.
Texto completo da fonteWang, Zhuo, Vignesh Ramamoorthy, Udi Gal e Allon Guez. "Possible Life Saver: A Review on Human Fall Detection Technology". Robotics 9, n.º 3 (19 de julho de 2020): 55. http://dx.doi.org/10.3390/robotics9030055.
Texto completo da fonteNizam, Yoosuf, Mohd Mohd e M. Jamil. "Development of a User-Adaptable Human Fall Detection Based on Fall Risk Levels Using Depth Sensor". Sensors 18, n.º 7 (13 de julho de 2018): 2260. http://dx.doi.org/10.3390/s18072260.
Texto completo da fonteChen, Weiming, Zijie Jiang, Hailin Guo e Xiaoyang Ni. "Fall Detection Based on Key Points of Human-Skeleton Using OpenPose". Symmetry 12, n.º 5 (5 de maio de 2020): 744. http://dx.doi.org/10.3390/sym12050744.
Texto completo da fonteHendi, Ade, Hermanto Hermanto e Abdur Rozaaq. "Sistem Deteksi Jatuh dan Peringatan Dini Pada Manusia Berbasis Android". Jurnal Sistem Komputer dan Informatika (JSON) 3, n.º 3 (31 de março de 2022): 350. http://dx.doi.org/10.30865/json.v3i3.3927.
Texto completo da fonteValcourt, L., Y. D. L. Hoz e M. Labrador. "Smartphone-based Human Fall Detection System". IEEE Latin America Transactions 14, n.º 2 (fevereiro de 2016): 1011–17. http://dx.doi.org/10.1109/tla.2016.7437252.
Texto completo da fonteChander, Harish, Reuben F. Burch, Purva Talegaonkar, David Saucier, Tony Luczak, John E. Ball, Alana Turner et al. "Wearable Stretch Sensors for Human Movement Monitoring and Fall Detection in Ergonomics". International Journal of Environmental Research and Public Health 17, n.º 10 (19 de maio de 2020): 3554. http://dx.doi.org/10.3390/ijerph17103554.
Texto completo da fonteCai, Wen-Yu, Jia-Hao Guo, Mei-Yan Zhang, Zhi-Xiang Ruan, Xue-Chen Zheng e Shuai-Shuai Lv. "GBDT-Based Fall Detection with Comprehensive Data from Posture Sensor and Human Skeleton Extraction". Journal of Healthcare Engineering 2020 (25 de junho de 2020): 1–15. http://dx.doi.org/10.1155/2020/8887340.
Texto completo da fonteLim, Myeong Jun, Jin Ho Cho, Young Sun Cho e Tae Seong Kim. "Directional Human Fall Recognition Using a Pair of Accelerometer and Gyroscope Sensors". Applied Mechanics and Materials 135-136 (outubro de 2011): 449–54. http://dx.doi.org/10.4028/www.scientific.net/amm.135-136.449.
Texto completo da fonteYan, Jianjun, Xueqiang Wang, Jiangtao Shi e Shuai Hu. "Skeleton-Based Fall Detection with Multiple Inertial Sensors Using Spatial-Temporal Graph Convolutional Networks". Sensors 23, n.º 4 (14 de fevereiro de 2023): 2153. http://dx.doi.org/10.3390/s23042153.
Texto completo da fontePan, Daohua, Hongwei Liu, Dongming Qu e Zhan Zhang. "Human Falling Detection Algorithm Based on Multisensor Data Fusion with SVM". Mobile Information Systems 2020 (31 de outubro de 2020): 1–9. http://dx.doi.org/10.1155/2020/8826088.
Texto completo da fonteBharti Sahu, Shreya Pawar, Mansi Chaudhari, Vaishnavi Kalal. "Real-Time Posture Estimation-Based Human Fall Detection System". Tuijin Jishu/Journal of Propulsion Technology 44, n.º 3 (1 de dezembro de 2023): 4798–811. http://dx.doi.org/10.52783/tjjpt.v44.i3.2649.
Texto completo da fonteAlanazi, Thamer, e Ghulam Muhammad. "Human Fall Detection Using 3D Multi-Stream Convolutional Neural Networks with Fusion". Diagnostics 12, n.º 12 (6 de dezembro de 2022): 3060. http://dx.doi.org/10.3390/diagnostics12123060.
Texto completo da fonteLiu, Wei, Xu Liu, Yuan Hu, Jie Shi, Xinqiang Chen, Jiansen Zhao, Shengzheng Wang e Qingsong Hu. "Fall Detection for Shipboard Seafarers Based on Optimized BlazePose and LSTM". Sensors 22, n.º 14 (21 de julho de 2022): 5449. http://dx.doi.org/10.3390/s22145449.
Texto completo da fonteWAKITA, Kohei, Jian HUANG, Kosuke SEKIYAMA e Toshio FUKUDA. "Real-time Fall Detection and Prevention Control Using Intelligent Cane for Human Operator". Abstracts of the international conference on advanced mechatronics : toward evolutionary fusion of IT and mechatronics : ICAM 2010.5 (2010): 265–70. http://dx.doi.org/10.1299/jsmeicam.2010.5.265.
Texto completo da fonteFan, Xinnan, Qian Gong, Rong Fan, Jin Qian, Jie Zhu, Yuanxue Xin e Pengfei Shi. "Substation Personnel Fall Detection Based on Improved YOLOX". Electronics 12, n.º 20 (18 de outubro de 2023): 4328. http://dx.doi.org/10.3390/electronics12204328.
Texto completo da fonteMartínez-Villaseñor, Lourdes, Hiram Ponce, Jorge Brieva, Ernesto Moya-Albor, José Núñez-Martínez e Carlos Peñafort-Asturiano. "UP-Fall Detection Dataset: A Multimodal Approach". Sensors 19, n.º 9 (28 de abril de 2019): 1988. http://dx.doi.org/10.3390/s19091988.
Texto completo da fontePolepaka, Sanjeeva, Harshini Sangem, Amrutha Varshini Aleti, Akshitha Ajjuri, Myasar Mundher Adnan, Swathi B, Amandeep Nagpal e Ravi Kalra. "Promoting sustainable safety: Integrating fall detection for person and wheelchair safety". E3S Web of Conferences 507 (2024): 01025. http://dx.doi.org/10.1051/e3sconf/202450701025.
Texto completo da fonteSantos, Guto, Patricia Endo, Kayo Monteiro, Elisson Rocha, Ivanovitch Silva e Theo Lynn. "Accelerometer-Based Human Fall Detection Using Convolutional Neural Networks". Sensors 19, n.º 7 (6 de abril de 2019): 1644. http://dx.doi.org/10.3390/s19071644.
Texto completo da fonteTong, Chao, Yu Lian, Yang Zhang, Zhongyu Xie, Xiang Long e Jianwei Niu. "A Novel Real-Time Fall Detection System Based on Real-Time Video and Mobile Phones". Journal of Circuits, Systems and Computers 26, n.º 04 (6 de dezembro de 2016): 1750056. http://dx.doi.org/10.1142/s0218126617500566.
Texto completo da fonteBucinskas, Vytautas, Andrius Dzedzickis, Juste Rozene, Jurga Subaciute-Zemaitiene, Igoris Satkauskas, Valentinas Uvarovas, Rokas Bobina e Inga Morkvenaite-Vilkonciene. "Wearable Feet Pressure Sensor for Human Gait and Falling Diagnosis". Sensors 21, n.º 15 (3 de agosto de 2021): 5240. http://dx.doi.org/10.3390/s21155240.
Texto completo da fonteKolobe, Tsepo Constantinus, Chungling Tu e Pius Adewale Owolawi. "A Review on Fall Detection in Smart Home for Elderly and Disabled People". Journal of Advanced Computational Intelligence and Intelligent Informatics 26, n.º 5 (20 de setembro de 2022): 747–57. http://dx.doi.org/10.20965/jaciii.2022.p0747.
Texto completo da fonteZhang, Jin, Cheng Wu e Yiming Wang. "Human Fall Detection Based on Body Posture Spatio-Temporal Evolution". Sensors 20, n.º 3 (10 de fevereiro de 2020): 946. http://dx.doi.org/10.3390/s20030946.
Texto completo da fonteSiong Jun, Sai, Hafiz Rashidi Ramli, Azura Che Soh, Noor Ain Kamsani, Raja Kamil Raja Ahmad, Siti Anom Ahmad e Asnor Juraiza Ishak. "Development of fall detection and activity recognition using threshold based method and neural network". Indonesian Journal of Electrical Engineering and Computer Science 17, n.º 3 (1 de março de 2020): 1338. http://dx.doi.org/10.11591/ijeecs.v17.i3.pp1338-1347.
Texto completo da fonteDivya, S. "Developing a Fall Detection Technology for Mobility and System Level". Asian Journal of Computer Science and Technology 8, S2 (5 de março de 2019): 13–16. http://dx.doi.org/10.51983/ajcst-2019.8.s2.2034.
Texto completo da fonteFan, Kaibo, Ping Wang e Shuo Zhuang. "Human fall detection using slow feature analysis". Multimedia Tools and Applications 78, n.º 7 (24 de janeiro de 2018): 9101–28. http://dx.doi.org/10.1007/s11042-018-5638-9.
Texto completo da fonteAlanazi, Thamer, Khalid Babutain e Ghulam Muhammad. "A Robust and Automated Vision-Based Human Fall Detection System Using 3D Multi-Stream CNNs with an Image Fusion Technique". Applied Sciences 13, n.º 12 (7 de junho de 2023): 6916. http://dx.doi.org/10.3390/app13126916.
Texto completo da fonteXu, He, Leixian Shen, Qingyun Zhang e Guoxu Cao. "Fall Behavior Recognition Based on Deep Learning and Image Processing". International Journal of Mobile Computing and Multimedia Communications 9, n.º 4 (outubro de 2018): 1–15. http://dx.doi.org/10.4018/ijmcmc.2018100101.
Texto completo da fonteVishnu, Chalavadi, Rajeshreddy Datla, Debaditya Roy, Sobhan Babu e C. Krishna Mohan. "Human Fall Detection in Surveillance Videos Using Fall Motion Vector Modeling". IEEE Sensors Journal 21, n.º 15 (1 de agosto de 2021): 17162–70. http://dx.doi.org/10.1109/jsen.2021.3082180.
Texto completo da fonteHao, Zhanjun, Yu Duan, Xiaochao Dang e Hongwen Xu. "KS-FALL: Indoor Human Fall Detection Method Under 5GHz Wireless Signals". IOP Conference Series: Materials Science and Engineering 569 (9 de agosto de 2019): 032068. http://dx.doi.org/10.1088/1757-899x/569/3/032068.
Texto completo da fonteSong, Zhengyun. "Fall Detection Method Based on Convolutional Neural Network". Academic Journal of Science and Technology 7, n.º 2 (27 de setembro de 2023): 207–9. http://dx.doi.org/10.54097/ajst.v7i2.12274.
Texto completo da fonteSingh, Komal, Akshay Rajput e Sachin Sharma. "Human Fall Detection Using Machine Learning Methods: A Survey". International Journal of Mathematical, Engineering and Management Sciences 5, n.º 1 (1 de novembro de 2019): 161–80. http://dx.doi.org/10.33889/ijmems.2020.5.1.014.
Texto completo da fonteWu, Falin, Hengyang Zhao, Yan Zhao e Haibo Zhong. "Development of a Wearable-Sensor-Based Fall Detection System". International Journal of Telemedicine and Applications 2015 (2015): 1–11. http://dx.doi.org/10.1155/2015/576364.
Texto completo da fonteQiu, Yuting, James Meng e Baihua Li*. "Automated Falls Detection Using Visual Anomaly Detection and Pose-based Approaches: Experimental Review and Evaluation". Journal of Biomedical Research & Environmental Sciences 5, n.º 1 (janeiro de 2024): 055–63. http://dx.doi.org/10.37871/jbres1872.
Texto completo da fonteLuo, Jiaqing, Ruiyu Bai, Suining He e Kang G. Shin. "Pervasive Pose Estimation for Fall Detection". ACM Transactions on Computing for Healthcare 3, n.º 3 (31 de julho de 2022): 1–23. http://dx.doi.org/10.1145/3478027.
Texto completo da fonteSrikanth, K. "Alert System for Fall Detection". International Journal for Research in Applied Science and Engineering Technology 9, n.º 8 (31 de agosto de 2021): 1739–47. http://dx.doi.org/10.22214/ijraset.2021.37658.
Texto completo da fonteIazzi, Abderrazak, Mohammed Rziza e Rachid Oulad Haj Thami. "Fall Detection System-Based Posture-Recognition for Indoor Environments". Journal of Imaging 7, n.º 3 (26 de fevereiro de 2021): 42. http://dx.doi.org/10.3390/jimaging7030042.
Texto completo da fonteHasegawa, Tadahiro, e Hiroki Yokota. "Verification of Fall Detection Sensor". Journal of Robotics and Mechatronics 24, n.º 6 (20 de dezembro de 2012): 1089–91. http://dx.doi.org/10.20965/jrm.2012.p1089.
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