Auswahl der wissenschaftlichen Literatur zum Thema „Human Fall detection“
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Zeitschriftenartikel zum Thema "Human Fall detection"
Zhang, Duo, Xusheng Zhang, Shengjie Li, Yaxiong Xie, Yang Li, Xuanzhi Wang und Daqing Zhang. „LT-Fall“. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 7, Nr. 1 (27.03.2022): 1–24. http://dx.doi.org/10.1145/3580835.
Der volle Inhalt der QuelleSarthak 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, Nr. 10 (02.11.2023): 1169–76. http://dx.doi.org/10.17762/ijritcc.v11i10.8638.
Der volle Inhalt der QuelleSahithi, Challa, Chennepalli HimaBindu, Harika C und Jyothi M C. „Fall Detection“. International Research Journal of Computer Science 10, Nr. 04 (31.05.2023): 85–87. http://dx.doi.org/10.26562/irjcs.2023.v1004.09.
Der volle Inhalt der QuelleZheng, Kun, Bin Li, Yu Li, Peng Chang, Guangmin Sun, Hui Li und Junjie Zhang. „Fall detection based on dynamic key points incorporating preposed attention“. Mathematical Biosciences and Engineering 20, Nr. 6 (2023): 11238–59. http://dx.doi.org/10.3934/mbe.2023498.
Der volle Inhalt der QuelleShrivastava, Rashmi, und Manju Pandey. „Human Fall Detection Using Efficient Kernel and Eccentric Approach“. International Journal of E-Health and Medical Communications 12, Nr. 1 (Januar 2021): 62–80. http://dx.doi.org/10.4018/ijehmc.2021010105.
Der volle Inhalt der QuelleZi, Xing, Kunal Chaturvedi, Ali Braytee, Jun Li und Mukesh Prasad. „Detecting Human Falls in Poor Lighting: Object Detection and Tracking Approach for Indoor Safety“. Electronics 12, Nr. 5 (06.03.2023): 1259. http://dx.doi.org/10.3390/electronics12051259.
Der volle Inhalt der QuelleRibeiro, Osvaldo, Luis Gomes und Zita Vale. „IoT-Based Human Fall Detection System“. Electronics 11, Nr. 4 (15.02.2022): 592. http://dx.doi.org/10.3390/electronics11040592.
Der volle Inhalt der QuelleKan, Xi, Shenghao Zhu, Yonghong Zhang und Chengshan Qian. „A Lightweight Human Fall Detection Network“. Sensors 23, Nr. 22 (09.11.2023): 9069. http://dx.doi.org/10.3390/s23229069.
Der volle Inhalt der QuelleMartínez-Villaseñor, Lourdes, Hiram Ponce und Ricardo Abel Espinosa-Loera. „Multimodal Database for Human Activity Recognition and Fall Detection“. Proceedings 2, Nr. 19 (22.10.2018): 1237. http://dx.doi.org/10.3390/proceedings2191237.
Der volle Inhalt der QuelleAbduljabbar Ali, Mohammed, Abir Jaafar Hussain und Ahmed T. Sadiq. „Human Fall Down Recognition Using Coordinates Key Points Skeleton“. International Journal of Online and Biomedical Engineering (iJOE) 18, Nr. 02 (16.02.2022): 88–104. http://dx.doi.org/10.3991/ijoe.v18i02.28017.
Der volle Inhalt der QuelleDissertationen zum Thema "Human Fall detection"
DROGHINI, DIEGO. „Ambient Intelligence: Computational Audio Processing For Human Fall Detection“. Doctoral thesis, Università Politecnica delle Marche, 2019. http://hdl.handle.net/11566/263538.
Der volle Inhalt der QuelleAt present, Ambient Intelligence represents the challenge of the future. To obtain an ecosystem that is fully functional and calibrated to the user need, numerous systems, each of them dedicated to a specific task, must be integrated. One of these sub-systems is the human fall detection. Both research community and governments gave particular attention to the human fall detection because the fall is the first cause of death for people over 65. In this thesis, the human fall detection is addressed from an audio perspective: a dataset named A3FALL, composed of a corpus of several audio fall events of every-day objects and both simulated and real human falls recorded in 3 different rooms, has been presented. In particular, a special floor acoustic sensor (FAS) has been developed from this purpose and used to record the dataset together with an array of a microphone array. Different approaches that work with a different knowledge base according to the specific task have been proposed: first, two supervised approaches have been described that have highlighted the peculiarities of the audio drop detection and demonstrated the effectiveness of the proposed sensor. The human falls hardly available for systems development, unsupervised systems have been proposed that do not need examples of the target class in the learning phase. It has been shown that unsupervised approaches have better performance than the art state systems, but they do work well in not very complex scenarios. Finally, methods that work under more realistic conditions have been developed and described. A system where the user intervenes by correcting the system’s operation for a considerable reduction of false alarms is proposed. Then a few-shot learning approach that without any user intervention can achieve promising results using only a few examples of human fall in the training phase has been presented. The thesis concludes with an extensive evaluation of a Siamese Convolutional Autoencoder based approach. It has been shown that this approach outperforms all the previously proposed systems when assessed in a complex scenario.
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.
Der volle Inhalt der QuelleMastorakis, 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/.
Der volle Inhalt der QuelleNa, 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.
Der volle Inhalt der QuelleRuneskog, 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.
Der volle Inhalt der QuelleDjupinlärningstekniken Kroppshållningsestimation är ett lovande medel gällande att följa en person och identifiera dess kroppshållning. Eftersom kroppshållning och balans är två närliggande koncept, kan användning av kroppshållningsestimation appliceras till fallprevention. Genom att härleda läget för en persons tyngdpunkt och därefter läget för dess tryckcentrum, kan utvärdering en persons balans genomföras utan att använda kraftplattor eller sensorer och att enbart använda kameror. I denna studie har en kroppshållningsestimationmodell tillsammans med en fördefinierad kroppsviktfördelning använts för att extrahera läget för en persons tryckcentrum i realtid. Den föreslagna metoden använder två olika metoder för att utvinna djupseende av bilderna från kameror - stereoskopi genom användning av två RGB-kameror eller genom användning av en RGB-djupseende kamera. Det estimerade läget av tryckcentrat jämfördes med läget av samma parameter utvunnet genom användning av tryckplattan Wii Balance Board. Eftersom den föreslagna metoden var ämnad att fungera i realtid och utan hjälp av en GPU, blev valet av kroppshållningsestimationsmodellen inriktat på att maximera mjukvaruhastighet. Därför användes tre olika modeller - en mindre och snabbare modell vid namn Lightweight Pose Network, en större och mer träffsäker modell vid namn High-Resolution Network och en model som placerar sig någonstans mitt emellan de två andra modellerna gällande snabbhet och träffsäkerhet vid namn Pose Resolution Network. Den föreslagna metoden visade lovande resultat för utvinning av balansparametrar i realtid, fastän den största felfaktorn visade sig vara djupseendetekniken. Resultaten visade att användning av en mindre och snabbare kroppshållningsestimationsmodellen påvisar att hålla måttet i jämförelse med större och mer träffsäkra modeller vid användning i realtid och utan användning av externa dataprocessorer.
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.
Der volle Inhalt der QuelleAslamy, 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.
Der volle Inhalt der QuelleAccidental falls among the elderly is a major public health problem. As a result, a variety of systems have been developed for remote monitoring of the elderly to permit early detection of falls. The majority of the research that has been done so far in fall accidents has focused on developing new more successful algorithms spe- cifically to identify fall from non-fall. Although the statistics show that mortality and injuries caused by falls are increasing every year in conjunction with the in- creasing proportion of older people in the population. This thesis is about improving the current fall detection devices by covering the gaps and meet the needs of the current fall detection techniques. The improve- ments that have been identified is to provide a secure assessment of the patient's health and be able to call for aid more quickly when a fall occurs. Another im- provement is the mobility for the elderly to be outdoors and have the ability to per- form daily activities without being limited by the location position. In summary it can be said that a multisensor system in form of a prototype has been designed to cover the deficiencies and improvements that have been identi- fied. Apart from detection of falls and body movements through an accelerometer sensor the prototype does also include a sensor for detecting vital signs in form of ECG. It also supports cellular and wireless network communication in form of GPRS and Wi-Fi to enable freedom of movement for the elderly. Furthermore, the prototype includes a sensor for GPS that provides information about location position.
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.
Der volle Inhalt der QuelleAging population is one of the key problems for the vast majority of many countries. The number of elderly people who suffer from multiple diseases and need continuous monitoring of their vital signs increases everyday, resulting in additional healthcare costs. Modern healthcare systems in geriatric medicine often require elderly presence at the hospital which conflict with their demand for independence and privacy. Recent developments on remote e-health monitoring, provides a wide range of solutions. However, most of the devices are designed for specific medical sensing and operate independently from each other. There is still a lack of integrated framework with high interoperability and continuous online monitoring support for further correlation analysis. This thesis is a step towards a remote, complete, and continuous data gathering system for elderly people with various types of health problems. Our research spirit is motivated by immediate demand in a secured and trusted remote wireless health monitoring System for assisted living Elderly people, combining various data sources. To create such a complete system we divide it into subsystems, in order to make it feasible and easy to implement, thus allowing us to update each subsystem individually in the future studies without affecting other integrated subsystems. The main focus is on a complete remote e-health monitoring system. The list of main contributions contains (1) propose a new approach for security of monitored devices and propose a solution to prevent MiTM attacks and reduce energy consumption, (2) we propose reliable fall detection,(3) investigating and developing a novel recognition method of daily activities for monitored elderly patient, (4) propose an approach to enhance the reliability of the system and to reduce false alarms and unnecessary interventions, (5) propose and develop a sign language to text converter algorithm using multi-sensor fusion analysis. As a result, we expect to provide a monitoring system with reliable accuracy in the detection of abnormal events, and raise an alarm upon detection of such events to seek help and assistance
Liao, Kai-Chieh, und 廖楷捷. „Human Tracking System and Fall Detection“. Thesis, 2008. http://ndltd.ncl.edu.tw/handle/58887516245737991221.
Der volle Inhalt der Quelle國立中正大學
電機工程所
96
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 activity environment. The application of computer vision systems has widely spread along with the progress of the computer software and hardware. In the recent ten years, the researchers show their interest in the research of identifying and analyzing the humans'' motions or behaviors. The academia has a quite good result of study in the distinguishing of human motion posture at present. This thesis proposed a detection system on examining and judging of the pedestrian who falls for the abnormal behavior or personage''s own independence. This thesis mainly divides into two focal points: the fist is individual distinguish personages and the second is the detection of fall. Firstly, the personage''s information and definition of the upper part of the body information were found, this result was carry on and be the method for human tracking. Due to the complexional information of human, the group of other moving objects could be leach out. Besides, the part of fall detection, we detect the person in video who falls based on the information of its horizontal, vertical projections. The function of this system can be used on human tracking and fall detection, and it could be achieved without define models and large number of mathematics operation.
Liu, Yin-Chu, und 劉殷助. „Hidden Conditonal Random Fields for Human Fall Detection“. Thesis, 2014. http://ndltd.ncl.edu.tw/handle/55383181007118280640.
Der volle Inhalt der Quelle國立臺灣科技大學
電子工程系
102
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 squat. Experimental results indicate that the proposed human fall detection system can achieve a high detection rate and low false alarm rate. Also, the proposed system outperforms Hidden Markov Chain and Cuboids in terms of detection rate.
Bücher zum Thema "Human Fall detection"
Clark, Mary Higgins. Cradle Will Fall. Audio Literature, 1993.
Den vollen Inhalt der Quelle findenClark, Mary Higgins. Cradle Will Fall. Turtleback Books Distributed by Demco Media, 1991.
Den vollen Inhalt der Quelle findenClark, Mary Higgins. The Cradle Will Fall. Pocket Books, 1997.
Den vollen Inhalt der Quelle findenClark, Mary Higgins. The Cradle Will Fall. Pocket, 1991.
Den vollen Inhalt der Quelle findenClark, Mary Higgins. The Cradle Will Fall. Audio Literature, 1991.
Den vollen Inhalt der Quelle findenClark, Mary Higgins. The Cradle Will Fall. Pocket Books, 2003.
Den vollen Inhalt der Quelle findenClark, Mary Higgins. The Cradle Will Fall. Buccaneer Books, 1993.
Den vollen Inhalt der Quelle findenClark, Mary Higgins. The Cradle Will Fall. Tandem Library, 1999.
Den vollen Inhalt der Quelle findenPoe, Edgar Allan. Selected Tales. Herausgegeben von David Van Leer. Oxford University Press, 2008. http://dx.doi.org/10.1093/owc/9780199535774.001.0001.
Der volle Inhalt der QuelleDuncan, Karen A. Female Sexual Predators. ABC-CLIO, LLC, 2010. http://dx.doi.org/10.5040/9798400650475.
Der volle Inhalt der QuelleBuchteile zum Thema "Human Fall detection"
Soni, Pramod Kumar, und Ayesha Choudhary. „Automated Fall Detection Using Computer Vision“. In Intelligent Human Computer Interaction, 220–29. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-04021-5_20.
Der volle Inhalt der QuelleNahian, M. Jaber Al, Mehedi Hasan Raju, Zarin Tasnim, Mufti Mahmud, Md Atiqur Rahman Ahad und M. Shamim Kaiser. „Contactless Fall Detection for the Elderly“. In Contactless Human Activity Analysis, 203–35. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-68590-4_8.
Der volle Inhalt der QuelleKepski, Michal, und Bogdan Kwolek. „Human Fall Detection Using Kinect Sensor“. In Proceedings of the 8th International Conference on Computer Recognition Systems CORES 2013, 743–52. Heidelberg: Springer International Publishing, 2013. http://dx.doi.org/10.1007/978-3-319-00969-8_73.
Der volle Inhalt der QuelleDemiröz, Barış Evrim, Albert Ali Salah und Lale Akarun. „Coupling Fall Detection and Tracking in Omnidirectional Cameras“. In Human Behavior Understanding, 73–85. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-11839-0_7.
Der volle Inhalt der QuelleNizam, Yoosuf, und 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, 237–59. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-38748-8_10.
Der volle Inhalt der QuelleHalder, Kumar Saikat, Ashwani Singla und Ranjit Singh. „Novel Algorithm on Human Body Fall Detection“. In Learning and Analytics in Intelligent Systems, 214–21. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-24322-7_28.
Der volle Inhalt der QuelleChen, Ziwei, Yiye Wang und Wankou Yang. „Video Based Fall Detection Using Human Poses“. In Big Data, 283–96. Singapore: Springer Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-9709-8_19.
Der volle Inhalt der QuellePatel, Viraj, Suraj Kaple und Vishal R. Satpute. „Indoor Human Fall Detection Using Deep Learning“. In Advancements in Interdisciplinary Research, 235–42. Cham: Springer Nature Switzerland, 2022. http://dx.doi.org/10.1007/978-3-031-23724-9_22.
Der volle Inhalt der QuelleXu, Tao, und Yun Zhou. „Fall Detection Based on Skeleton Data“. In Human Aspects of IT for the Aged Population. Applications, Services and Contexts, 475–83. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-58536-9_38.
Der volle Inhalt der QuelleZhao, Kewei, Kebin Jia und Pengyu Liu. „Fall Detection Algorithm Based on Human Posture Recognition“. In Advances in Intelligent Information Hiding and Multimedia Signal Processing, 119–26. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-50212-0_15.
Der volle Inhalt der QuelleKonferenzberichte zum Thema "Human Fall detection"
Ali, Syed Farooq, Alizaa Fatima, Noman Nazar, Muhammad Muaz und Fatima Idrees. „Human fall detection“. In 2013 16th International Multi Topic Conference (INMIC). IEEE, 2013. http://dx.doi.org/10.1109/inmic.2013.6731332.
Der volle Inhalt der QuelleColon, Luis N. Valcourt, Yueng DeLaHoz und 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.
Der volle Inhalt der QuelleSase, Priyanka S., und 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.
Der volle Inhalt der QuelleKorumilli, Manasa, Koppula Sai Lasya, Naveen Cheggoju, Vipin Kamble und 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.
Der volle Inhalt der QuelleRamirez, H., S. A. Velastin, E. Fabregas, I. Meza, D. Makris und 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.
Der volle Inhalt der QuelleChen, Yie-Tarng, Yu-Ching Lin und 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.
Der volle Inhalt der QuelleJain, Simran, und 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.
Der volle Inhalt der QuelleKaudki, Bharati, und 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.
Der volle Inhalt der QuelleLu, Hong, Bohong Yang, Rui Zhao, Pengliang Qu und 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.
Der volle Inhalt der QuelleWang, Xiang, und 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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