Bibliografías temáticas / Human Fall detection

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Literatura académica sobre el tema "Human Fall detection"

Autor: Grafiati
Publicado: 25 de mayo de 2024

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Artículos de revistas sobre el tema "Human Fall detection"

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Zhang, Duo, Xusheng Zhang, Shengjie Li, et al. "LT-Fall." Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 7, no. 1 (2022): 1–24. http://dx.doi.org/10.1145/3580835.

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Falls are the leading cause of fatal injuries to elders in modern society, which has motivated researchers to propose various fall detection technologies. We observe that most of the existing fall detection solutions are diverging from the purpose of fall detection: timely alarming the family members, medical staff or first responders to save the life of the human with severe injury caused by fall. Instead, they focus on detecting the behavior of human falls, which does not necessarily mean a human is in real danger. The real critical situation is when a human cannot get up without assistance
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Sarthak 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, no. 10 (2023): 1169–76. http://dx.doi.org/10.17762/ijritcc.v11i10.8638.

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For elderly people, falls are a severe worry since they can result in serious injuries, loss of independence, and deterioration of general health. In fact, among older persons, falls constitute the main reason for injury-related hospitalisations and fatalities. There is an obvious demand for fall detection systems that can help avoid or lessen the negative effects of falls given the enormous impact of falls on the senior population. Systems for detecting falls are created to notify carers or emergency services when a person has fallen, enabling quicker responses and better results. Elderly peo
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Sahithi, Challa, Chennepalli HimaBindu, Harika C, and Jyothi M C. "Fall Detection." International Research Journal of Computer Science 10, no. 04 (2023): 85–87. http://dx.doi.org/10.26562/irjcs.2023.v1004.09.

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Researchers from all across the world are interested in human fall detection and activity recognition. Fall detection is an exciting topic that may be tackled in several ways. Several alternatives have been suggested in recent years. These applications determine whether a person is walking, squatting, or falling, among other activities. Among these tasks detecting elderly falls is crucial. This is because it is a pretty typical and dangerous occurrence that affects people of all ages, with the elderly having a disproportionately negative impact. This is the motivation behind the development of
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Zheng, Kun, Bin Li, Yu Li, et al. "Fall detection based on dynamic key points incorporating preposed attention." Mathematical Biosciences and Engineering 20, no. 6 (2023): 11238–59. http://dx.doi.org/10.3934/mbe.2023498.

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<abstract> <p>Accidental falls pose a significant threat to the elderly population, and accurate fall detection from surveillance videos can significantly reduce the negative impact of falls. Although most fall detection algorithms based on video deep learning focus on training and detecting human posture or key points in pictures or videos, we have found that the human pose-based model and key points-based model can complement each other to improve fall detection accuracy. In this paper, we propose a preposed attention capture mechanism for images that will be fed into the trainin
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Shrivastava, Rashmi, and Manju Pandey. "Human Fall Detection Using Efficient Kernel and Eccentric Approach." International Journal of E-Health and Medical Communications 12, no. 1 (2021): 62–80. http://dx.doi.org/10.4018/ijehmc.2021010105.

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Unintentional human falls are a very crucial problem in elderly people. If the fall goes unnoticed or undetected, it can lead to severe injuries and can even lead to death. Detecting falls as early as possible is very important to avoid severe physical injurious and mental trauma. The objective of this paper is to design the fall detection model using data of daily living activities only. In the proposed fall detection model, an eccentric approach with SVM based one-class classification is used. For the pre-processing step, fast fourier transformation has been applied to the data and seven fea
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Zi, Xing, Kunal Chaturvedi, Ali Braytee, Jun Li, and Mukesh Prasad. "Detecting Human Falls in Poor Lighting: Object Detection and Tracking Approach for Indoor Safety." Electronics 12, no. 5 (2023): 1259. http://dx.doi.org/10.3390/electronics12051259.

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Falls are one the leading causes of accidental death for all people, but the elderly are at particularly high risk. Falls are severe issue in the care of those elderly people who live alone and have limited access to health aides and skilled nursing care. Conventional vision-based systems for fall detection are prone to failure in conditions with low illumination. Therefore, an automated system that detects falls in low-light conditions has become an urgent need for protecting vulnerable people. This paper proposes a novel vision-based fall detection system that uses object tracking and image
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Ribeiro, Osvaldo, Luis Gomes, and Zita Vale. "IoT-Based Human Fall Detection System." Electronics 11, no. 4 (2022): 592. http://dx.doi.org/10.3390/electronics11040592.

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Human falls are an issue that especially affects elderly people, resulting in permanent disabilities or even in the person’s death. Preventing human falls is a social desire, but it is almost impossible to achieve because it is not possible to ensure full prevention. A possible solution is the detection of human falls in near real-time so that help can quickly be provided. This has the potential to greatly reduce the severity of the fall in long-term health consequences. This work proposes a solution based on the internet of things devices installed in people’s homes. The proposed non-wearable
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Kan, Xi, Shenghao Zhu, Yonghong Zhang, and Chengshan Qian. "A Lightweight Human Fall Detection Network." Sensors 23, no. 22 (2023): 9069. http://dx.doi.org/10.3390/s23229069.

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The rising issue of an aging population has intensified the focus on the health concerns of the elderly. Among these concerns, falls have emerged as a predominant health threat for this demographic. The YOLOv5 family represents the forefront of techniques for human fall detection. However, this algorithm, although advanced, grapples with issues such as computational demands, challenges in hardware integration, and vulnerability to occlusions in the designated target group. To address these limitations, we introduce a pioneering lightweight approach named CGNS-YOLO for human fall detection. Our
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Martínez-Villaseñor, Lourdes, Hiram Ponce, and Ricardo Abel Espinosa-Loera. "Multimodal Database for Human Activity Recognition and Fall Detection." Proceedings 2, no. 19 (2018): 1237. http://dx.doi.org/10.3390/proceedings2191237.

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Fall detection can improve the security and safety of older people and alert when fall occurs. Fall detection systems are mainly based on wearable sensors, ambient sensors, and vision. Each method has commonly known advantages and limitations. Multimodal and data fusion approaches present a combination of data sources in order to better describe falls. Publicly available multimodal datasets are needed to allow comparison between systems, algorithms and modal combinations. To address this issue, we present a publicly available dataset for fall detection considering Inertial Measurement Units (I
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Abduljabbar Ali, Mohammed, Abir Jaafar Hussain, and Ahmed T. Sadiq. "Human Fall Down Recognition Using Coordinates Key Points Skeleton." International Journal of Online and Biomedical Engineering (iJOE) 18, no. 02 (2022): 88–104. http://dx.doi.org/10.3991/ijoe.v18i02.28017.

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Falls pose a substantial threat to human safety and can quickly result in disastrous repercussions. This threat is particularly true for the elderly, where falls are the leading cause of hospitalization and injury-related death. A fall that is detected and responded to quickly has a lower danger and long-term impact. Many real-time fall detection solutions are available; however, these solutions have specific privacy, maintenance, and proper use issues. Vision-based fall event detection has the benefit of being completely private and straightforward to use and maintain. However, in real-world
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Tesis sobre el tema "Human Fall detection"

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DROGHINI, DIEGO. "Ambient Intelligence: Computational Audio Processing For Human Fall Detection." Doctoral thesis, Università Politecnica delle Marche, 2019. http://hdl.handle.net/11566/263538.

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L’ Ambient Intelligence rappresenta la sfida del futuro. Per ottenere un ecosis- tema funzionante e calibrato in base alle esigenze dell’utente, necessario inte- grare numerosi sistemi, ciascuno dedicato a un compito specifico. Uno di questi sottosistemi il rilevamento della caduta umana. In questa tesi, il rilevamento delle cadute umane affrontato da una prospettiva audio. In questo lavoro stato presentato un set di dati denominato A3FALL, composto da una serie di diversi eventi audio relativi alla caduta di oggetti comuni e cadute umane, simulate e reali. In particolare, per tale scopo stato
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Ojetola, O. "Detection of human falls using wearable sensors." Thesis, Coventry University, 2013. http://curve.coventry.ac.uk/open/items/93d006a7-540d-4ceb-8e19-df03e2f6c67f/1.

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Wearable sensor systems composed of small and light sensing nodes have the potential to revolutionise healthcare. While uptake has increased over time in a variety of application areas, it has been slowed by problems such as lack of infrastructure and the functional capabilities of the systems themselves. An important application of wearable sensors is the detection of falls, particularly for elderly or otherwise vulnerable people. However, existing solutions do not provide the detection accuracy required for the technology to gain the trust of medical professionals. This thesis aims to improv
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Mastorakis, Georgios. "Human fall detection methodologies : from machine learning using acted data to fall modelling using myoskeletal simulation." Thesis, Kingston University, 2018. http://eprints.kingston.ac.uk/42275/.

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Human Fall Detection is a research area with interest from many disciplines and aims to perform for many assisted-living monitoring applications to promptly identify life-threatening situations. A fall occurs when a person is unable to maintain balance due to a variety of issues; physical; mental or environmental. The accurate detection of the fall is crucial as a missed detection can be fatal. Variability of human physiological characteristics is currently unstudied as to the impact on a fall detector's performance as young adults and elderly are expected to fall differently. Another importan
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Na, Hana. "A study on detection of risk factors of a toddler's fall injuries using visual dynamic motion cues." Thesis, Brunel University, 2009. http://bura.brunel.ac.uk/handle/2438/3214.

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The research in this thesis is intended to aid caregivers’ supervision of toddlers to prevent accidental injuries, especially injuries due to falls in the home environment. There have been very few attempts to develop an automatic system to tackle young children’s accidents despite the fact that they are particularly vulnerable to home accidents and a caregiver cannot give continuous supervision. Vision-based analysis methods have been developed to recognise toddlers’ fall risk factors related to changes in their behaviour or environment. First of all, suggestions to prevent fall events of you
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Runeskog, Henrik. "Continuous Balance Evaluation by Image Analysis of Live Video : Fall Prevention Through Pose Estimation." Thesis, KTH, Skolan för kemi, bioteknologi och hälsa (CBH), 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-297541.

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The deep learning technique Human Pose Estimation (or Human Keypoint Detection) is a promising field in tracking a person and identifying its posture. As posture and balance are two closely related concepts, the use of human pose estimation could be applied to fall prevention. By deriving the location of a persons Center of Mass and thereafter its Center of Pressure, one can evaluate the balance of a person without the use of force plates or sensors and solely using cameras. In this study, a human pose estimation model together with a predefined human weight distribution model were used to ext
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Lin, Chia-Hua. "A Real-Time Human Posture Classifier and Fall-Detector." Case Western Reserve University School of Graduate Studies / OhioLINK, 2014. http://rave.ohiolink.edu/etdc/view?acc_num=case1401707860.

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Aslamy, Benjamin. "Utveckling av ett multisensorsystem för falldetekteringsanordningar." Thesis, KTH, Data- och elektroteknik, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-188401.

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Fallolyckor bland äldre är ett stort folkhälsoproblem. Således har det utvecklats en rad olika system för fjärrövervakning av äldre för att möjliggöra en tidig detekte- ring av fallolyckor. Majoriteten av de forskningar som hittills har gjorts inom fallo- lyckor har fokuserat på att utveckla nya mer framgångsrika algoritmer just för att identifiera fall från icke-fall. Trots det visar statistiken att dödsfall och skador orsa- kade av fallolyckor ökar för varje år i samband med den ökande andel äldre perso- ner i befolkningen. Detta examensarbete handlar om att förbättra
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Shaafi, Aymen. "Secured and trusted remote wireless health monitoring systems for assisted living of elderly people." Electronic Thesis or Diss., Université Paris Cité, 2021. http://www.theses.fr/2021UNIP5208.

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Le vieillissement de la population est l'un des problèmes clés pour la grande majorité de nombreux pays. Le nombre de personnes âgées souffrant de multiples maladies et nécessitant une surveillance continue de leurs signes vitaux augmente chaque jour, entraînant des coûts de santé supplémentaires. Les systèmes de santé modernes en médecine gériatrique nécessitent souvent la présence de personnes âgées à l'hôpital, ce qui est en conflit avec leur exigence d'indépendance et d'intimité. Les développements récents sur la télésurveillance e-santé offrent une large gamme de solutions. Cependant, la
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Liao, Kai-Chieh, and 廖楷捷. "Human Tracking System and Fall Detection." Thesis, 2008. http://ndltd.ncl.edu.tw/handle/58887516245737991221.

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碩士<br>國立中正大學<br>電機工程所<br>96<br>In recent years, because the decline in birth rate and the increase of national average life span, special attention is gradually shifting to the security of the elderly people and children, fall is one of the often seen accidental among them. The purpose of this thesis is to detect the situation and to examine the possibility whether or not the personages fall while moving by a monitoring system. Based on a result of detection, the doctor and medical treatment-nursing group could put forward a medical treatment plan and carry on improvement of the pedestrian''s
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Liu, Yin-Chu, and 劉殷助. "Hidden Conditonal Random Fields for Human Fall Detection." Thesis, 2014. http://ndltd.ncl.edu.tw/handle/55383181007118280640.

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碩士<br>國立臺灣科技大學<br>電子工程系<br>102<br>In recent years, the global population has begun to age rapidly. Automatic fall detection for senior citizens has become an important issue for smart home. In this research, we propose a novel video-based human fall detection system that can detect a human fall in real-time with a high detection rate. This fall detection system is based on Hidden Conditional Random Fields model, and an intelligent combination of height estimation and appearance cues. Our system can efficiently distinguish “fall-down incidents” from “fall-like incidents” such as sit-down and sq
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Libros sobre el tema "Human Fall detection"

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Clark, Mary Higgins. Cradle Will Fall. Audio Literature, 1993.

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Clark, Mary Higgins. Cradle Will Fall. Turtleback Books Distributed by Demco Media, 1991.

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Clark, Mary Higgins. The Cradle Will Fall. Pocket Books, 1997.

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Clark, Mary Higgins. The Cradle Will Fall. Pocket, 1991.

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Clark, Mary Higgins. The Cradle Will Fall. Audio Literature, 1991.

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Clark, Mary Higgins. The Cradle Will Fall. Pocket Books, 2003.

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Clark, Mary Higgins. The Cradle Will Fall. Buccaneer Books, 1993.

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Clark, Mary Higgins. The Cradle Will Fall. Tandem Library, 1999.

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Poe, Edgar Allan. Selected Tales. Edited by David Van Leer. Oxford University Press, 2008. http://dx.doi.org/10.1093/owc/9780199535774.001.0001.

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Since their first publication in the 1830s and 1840s, Edgar Allan Poe's extraordinary Gothic tales have established themselves as classics of horror fiction and have also created many of the conventions which still dominate the genre of detective fiction. Yet, as well as being highly enjoyable, Poe's tales are works of very real intellectual exploration. Abandoning the criteria of characterization and plotting in favour of blurred boundaries between self and other, will and morality, identity and memory, Poe uses the Gothic to question the integrity of human existence. Indeed, Poe is less inte
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Duncan, Karen A. Female Sexual Predators. ABC-CLIO, LLC, 2010. http://dx.doi.org/10.5040/9798400650475.

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This unprecedented look at female sexual predators explains why and how they prey on our children and youths and what adults–and children and youths themselves-should understand to prevent victimization. In Female Sexual Predators: Understanding and Identifying Them to Protect Our Children and Youths, social worker and therapist Karen A. Duncan helps adults be proactive so children will not fall prey to this violation. Vignettes pulled from news headlines and interviews with female sexual predators Duncan has encountered in her own practice are used to help readers understand these crimes and
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Capítulos de libros sobre el tema "Human Fall detection"

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Soni, Pramod Kumar, and Ayesha Choudhary. "Automated Fall Detection Using Computer Vision." In Intelligent Human Computer Interaction. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-04021-5_20.

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Nahian, M. Jaber Al, Mehedi Hasan Raju, Zarin Tasnim, Mufti Mahmud, Md Atiqur Rahman Ahad, and M. Shamim Kaiser. "Contactless Fall Detection for the Elderly." In Contactless Human Activity Analysis. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-68590-4_8.

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Kepski, Michal, and Bogdan Kwolek. "Human Fall Detection Using Kinect Sensor." In Proceedings of the 8th International Conference on Computer Recognition Systems CORES 2013. Springer International Publishing, 2013. http://dx.doi.org/10.1007/978-3-319-00969-8_73.

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Demiröz, Barış Evrim, Albert Ali Salah, and Lale Akarun. "Coupling Fall Detection and Tracking in Omnidirectional Cameras." In Human Behavior Understanding. Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-11839-0_7.

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Nizam, Yoosuf, and M. Mahadi Abdul Jamil. "A Novel Approach for Human Fall Detection and Fall Risk Assessment." In Challenges and Trends in Multimodal Fall Detection for Healthcare. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-38748-8_10.

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Halder, Kumar Saikat, Ashwani Singla, and Ranjit Singh. "Novel Algorithm on Human Body Fall Detection." In Learning and Analytics in Intelligent Systems. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-24322-7_28.

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Chen, Ziwei, Yiye Wang, and Wankou Yang. "Video Based Fall Detection Using Human Poses." In Big Data. Springer Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-9709-8_19.

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Patel, Viraj, Suraj Kaple, and Vishal R. Satpute. "Indoor Human Fall Detection Using Deep Learning." In Advancements in Interdisciplinary Research. Springer Nature Switzerland, 2022. http://dx.doi.org/10.1007/978-3-031-23724-9_22.

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Xu, Tao, and Yun Zhou. "Fall Detection Based on Skeleton Data." In Human Aspects of IT for the Aged Population. Applications, Services and Contexts. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-58536-9_38.

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Zhao, Kewei, Kebin Jia, and Pengyu Liu. "Fall Detection Algorithm Based on Human Posture Recognition." In Advances in Intelligent Information Hiding and Multimedia Signal Processing. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-50212-0_15.

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Actas de conferencias sobre el tema "Human Fall detection"

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Ali, Syed Farooq, Alizaa Fatima, Noman Nazar, Muhammad Muaz, and Fatima Idrees. "Human fall detection." In 2013 16th International Multi Topic Conference (INMIC). IEEE, 2013. http://dx.doi.org/10.1109/inmic.2013.6731332.

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Colon, Luis N. Valcourt, Yueng DeLaHoz, and Miguel Labrador. "Human fall detection with smartphones." In 2014 6th IEEE Latin-American Conference on Communications (LATINCOM). IEEE, 2014. http://dx.doi.org/10.1109/latincom.2014.7041879.

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Sase, Priyanka S., and Smriti H. Bhandari. "Human Fall Detection using Depth Videos." In 2018 5th International Conference on Signal Processing and Integrated Networks (SPIN). IEEE, 2018. http://dx.doi.org/10.1109/spin.2018.8474181.

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Korumilli, Manasa, Koppula Sai Lasya, Naveen Cheggoju, Vipin Kamble, and Vishal R. Satpute. "Human Fall Detection using Skeleton Features." In 2023 2nd International Conference on Paradigm Shifts in Communications Embedded Systems, Machine Learning and Signal Processing (PCEMS). IEEE, 2023. http://dx.doi.org/10.1109/pcems58491.2023.10136111.

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Ramirez, H., S. A. Velastin, E. Fabregas, I. Meza, D. Makris, and G. Farias. "Fall Detection using Human Skeleton Features." In 11th International Conference of Pattern Recognition Systems (ICPRS 2021). Institution of Engineering and Technology, 2021. http://dx.doi.org/10.1049/icp.2021.1465.

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Chen, Yie-Tarng, Yu-Ching Lin, and Wen-Hsien Fang. "A hybrid human fall detection scheme." In 2010 17th IEEE International Conference on Image Processing (ICIP 2010). IEEE, 2010. http://dx.doi.org/10.1109/icip.2010.5650127.

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Jain, Simran, and K. Sitara. "Human Fall Detection in Surveillance Videos." In 2022 3rd International Conference for Emerging Technology (INCET). IEEE, 2022. http://dx.doi.org/10.1109/incet54531.2022.9824941.

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Kaudki, Bharati, and Anil Surve. "Human Fall Detection Using RFID Technology." In 2018 9th International Conference on Computing, Communication and Networking Technologies (ICCCNT). IEEE, 2018. http://dx.doi.org/10.1109/icccnt.2018.8494022.

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Lu, Hong, Bohong Yang, Rui Zhao, Pengliang Qu, and Wenqiang Zhang. "Intelligent Human Fall Detection for Home Surveillance." In 2014 IEEE 11th Intl Conf on Ubiquitous Intelligence & Computing and 2014 IEEE 11th Intl Conf on Autonomic & Trusted Computing and 2014 IEEE 14th Intl Conf on Scalable Computing and Communications and Its Associated Workshops (UIC-ATC-ScalCom). IEEE, 2014. http://dx.doi.org/10.1109/uic-atc-scalcom.2014.56.

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Wang, Xiang, and Kebin Jia. "Human Fall Detection Algorithm Based on YOLOv3." In 2020 IEEE 5th International Conference on Image, Vision and Computing (ICIVC). IEEE, 2020. http://dx.doi.org/10.1109/icivc50857.2020.9177447.

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