Literatura académica sobre el tema "Human Fall detection"
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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 and is thus lying on the ground after the fall because of losing consciousness or becoming incapacitated due to severe injury. In this paper, we define a life-threatening fall as a behavior that involves a falling down followed by a long-lie of humans on the ground, and for the first time point out that a fall detection system should focus on detecting life-threatening falls instead of detecting any random falls. Accordingly, we design and implement LT-Fall, a mmWave-based life-threatening fall detection and alarming system. LT-Fall detects and reports both fall and fall-like behaviors in the first stage and then identifies life-threatening falls by continuously monitoring the human status after fall in the second stage. We propose a joint spatio-temporal localization technique to detect and locate the micro-motions of the human, which solves the challenge of mmWave's insufficient spatial resolution when the human is static, i.e., lying on the ground. Extensive evaluation on 15 volunteers demonstrates that compared to the state-of-the-art work (92% precision and 94% recall), LT-Fall achieves zero false alarms as well as a precision of 100% and a recall of 98.8%.
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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 people who live alone or have mobility or balance impairments that make them more likely to fall may find these systems to be especially helpful.
 The difficulty of categorising various actions as part of a system created to meet the demand for a wearable device to collect data for fall and near-fall analysis is addressed in this study. Three common activities—standing, walking, and lying down—four distinct fall trajectories—forward, backward, left, and right—as well as near-fall circumstances are recognised and detected.
 Overall, fall detection systems play a significant role in the care of elderly people by lowering the chance of falls and its unfavourable effects. In order to better meet the demands of this vulnerable group, it's expected that as the older population grows, there will be a greater demand for fall detection systems and ongoing technological developments.
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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 an aged assistance system that detects falls and notifies the caretaker. One of the key characteristics is that it sends an email notification along with a call to the caretaker once a fall is detected.
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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 training network, and a fall detection model based on this mechanism. We accomplish this by fusing the human dynamic key point information with the original human posture image. We first propose the concept of dynamic key points to account for incomplete pose key point information in the fall state. We then introduce an attention expectation that predicates the original attention mechanism of the depth model by automatically labeling dynamic key points. Finally, the depth model trained with human dynamic key points is used to correct the detection errors of the depth model with raw human pose images. Our experiments on the Fall Detection Dataset and the UP-Fall Detection Dataset demonstrate that our proposed fall detection algorithm can effectively improve the accuracy of fall detection and provide better support for elderly care.</p> </abstract>
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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 features have been calculated using the preprocessed ADL dataset that has been calculated from the dataset of ADL (activities of daily living) activities acquired from the smartphones. An enhancement of the chi-square kernel-based support vector machine has been proposed here for classifying ADL activities from fall activities. Using the proposed algorithm, 98.81% sensitivity and 98.65% specificity have been achieved. This fall detection model achieved 100% accuracy on the FARSEEING dataset.
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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 enhancement techniques. The proposed approach is divided into two parts. First, the captured frames are optimized using a dual illumination estimation algorithm. Next, a deep-learning-based tracking framework that includes detection by YOLOv7 and tracking by the Deep SORT algorithm is proposed to perform fall detection. On the Le2i fall and UR fall detection (URFD) datasets, we evaluate the proposed method and demonstrate the effectiveness of fall detection in dark night environments with obstacles.
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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 solution is non-intrusive and can be deployed not only in homes but also in hospitals, rehabilitation facilities, and elderly homes. The solution uses a three-layered computation architecture composed of edge, fog, and cloud. A mathematical model using the Morlet wavelet and an artificial intelligence model using artificial neural networks are used for human fall classification; both approaches are compared. The results showed that the combination of both models is possible and brings benefits to the system, achieving an accuracy of 92.5% without false negatives.
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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 method incorporates both the GSConv module and the GDCN module to reconfigure the neck network of YOLOv5s. The objective behind this modification is to diminish the model size, curtail floating-point computations during feature channel fusion, and bolster feature extraction efficacy, thereby enhancing hardware adaptability. We also integrate a normalization-based attention module (NAM) into the framework, which concentrates on salient fall-related data and deemphasizes less pertinent information. This strategic refinement augments the algorithm’s precision. By embedding the SCYLLA Intersection over Union (SIoU) loss function, our model benefits from faster convergence and heightened detection precision. We evaluated our model using the Multicam dataset and the Le2i Fall Detection dataset. Our findings indicate a 1.2% enhancement in detection accuracy compared with the conventional YOLOv5s framework. Notably, our model realized a 20.3% decrease in parameter tally and a 29.6% drop in floating-point operations. A comprehensive instance analysis and comparative assessments underscore the method’s superiority and efficacy.
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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 (IMUs), ambient infrared presence/absence sensors, and an electroencephalogram Helmet. It will allow human activity recognition researchers to do experiments considering different combination of sensors.
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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 scenarios, falls are diverse and result in high detection instability. This study proposes a novel vision-based technique for fall detection and analyzes an extracted skeleton to define human postures. OpenPose can be used to get skeletal information about the human body. It identifies a fall using three critical parameters: the center of the value of the head and shoulder coordinates, the critical points of the shoulder coordinates, and the distance between the center of the skeleton's head and the floor with the angle between the center of the shoulders and the ground. Our proposed methodology was effective, with a classification accuracy of 97.7%.
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 sviluppato uno speciale sensore acus- tico a pavimento (FAS) che stato utilizzato per registrare il set di dati insieme ad un array di microfoni. Sono stati proposti differenti approcci che lavorano con una diversa base di conoscenze in base al compito specifico: in primo lu- ogo sono stati descritti due approcci supervisionati che hanno evidenziato le peculiarit del rilevamento della caduta audio e hanno dimostrato l’efficacia del sensore proposto. Per via delle poche cadute umane reali a disposizione per lo sviluppo di tali sistemi, sono stati proposti approcci non supervisionati che non necessitano di esempi della classe target nella fase di apprendimento. stato di- mostrato che gli approcci non supervisionati hanno prestazioni migliori rispetto ai sistemi dello stato dell’arte, ma funzionano bene in scenari poco complessi. Infine, sono stati sviluppati e descritti metodi che funzionano in condizioni pi realistiche. Viene proposto un sistema in cui l’utente interviene correggendo il funzionamento del sistema per una notevole riduzione dei falsi allarmi. Poi viene proposto un approccio di apprendimento one-shot che, senza l’intervento dell’utente, pu ottenere risultati promettenti utilizzando solo alcuni esempi di caduta umana nella fase di addestramento. La tesi si conclude con una valu- tazione approfondita di un approccio basato su un Autoencoder siamese. stato dimostrato che questo approccio migliore di tutti i sistemi precedentemente proposti quando valutatti in scenari complessi.<br>At 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.
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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 improve the state of the art in automated human fall detection algorithms through the use of a machine learning based algorithm combined with novel data annotation and feature extraction methods. Most wearable fall detection algorithms are based on thresholds set by observational analysis for various fall types. However, such algorithms do not generalise well for unseen datasets. This has thus led to many fall detection systems with claims of high performance but with high rates of False Positive and False Negative when evaluated on unseen datasets. A more appropriate approach, as proposed in this thesis, is a machine learning based algorithm for fall detection. The work in this thesis uses a C4.5 Decision Tree algorithm and computes input features based on three fall stages: pre-impact, impact and post-impact. By computing features based on these three fall stages, the fall detection algorithm can learn patterns unique to falls. In total, thirteen features were selected across the three fall stages out of an original set of twenty-eight features. Further to the identification of fall stages and selection of appropriate features, an annotation technique named micro-annotation is proposed that resolves annotation-related ambiguities in the evaluation of fall detection algorithms. Further analysis on factors that can impact the performance of a machine learning based algorithm were investigated. The analysis defines a design space which serves as a guideline for a machine learning based fall detection algorithm. The factors investigated include sampling frequency, the number of subjects used for training, and sensor location. The optimal values were found to be10Hz, 10 training subjects, and a single sensor mounted on the chest. Protocols for falls and Activities of Daily Living (ADL) were designed such that the developed algorithms are able to cope under a variety of real world activities and events. A total of 50 subjects were recruited to participate in the data gathering exercise. Four common types of falls in the sagittal and coronal planes were simulated by the volunteers; and falls in the sagittal plane were additionally induced by applying a lateral force to blindfolded volunteers. The algorithm was evaluated based on leave one subject out cross validation in order to determine its ability to generalise to unseen subjects. The current state of the art in the literature shows fall detectors with an F-measure below 90%. The commercial Tynetec fall detector provided an F-measure of only 50% when evaluated here. Overall, the fall detection algorithm using the proposed micro-annotation technique and fall stage features provides an F-measure of 93% at 10Hz, exceeding the performance provided by the current state of the art.
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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 important issue is the scene occlusions. In the use of visual sensors, an occluded fall is treated as a missed detection as the whereabouts of the person is unknown when occluded. Finally, current studies are based on acted fall datasets on which algorithms are trained. These dataset are unrepresentative of real fall events and illustrate the events without occlusions or other scene in uences. Several fall detection algorithms were developed during the study aiming to achieve accuracy in detection falls while fall-like actions such as lying down remain undetected. Human fall datasets were used for training and testing purposes of A machine learning algorithm using data from depth cameras which captured the fall events from different views. A new pathway was introduced tackling the issues of availability issues of data-driven machine learning approaches which was achieved with the use of simulation data. The use of myoskeletal simulation was then selected as a closer representation of the human body in terms of structure and behaviour. With the use of a simulation model, a personalised estimation of the fall event can be achieved as it is parametrised on a physical characteristic such as the height of the falling person. Alternative technologies such as accelerometers have been used for fall detection to prove the validity of this approach on other modalities. A study regarding the impact of occlusions for fall detection which is one of the issues not properly investigated in current work is proposed and examined. Synthetic occlusions were added to existing depth data from publicly available datasets. The research methodologies were evaluated using the most representative depth video and accelerometer data from existing datasets, as well as YouTube videos of real-fall events. The machine learning methodologies achieved good results on similar body variability datasets. A discussion regarding the proof of concept of the simulation-based approach for fall modelling is mentioned given the comparative results against existing methodologies which achieves better than any existing work evaluated against known datasets. The simulation approach is also evaluated against occluded fall and non-fall event data, proving the further robustness of the approach. This platform can be expanded to analyse any type of fall, or body posture (e.g. elderly), without the use of humans to performs fall events.
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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 young children at home were collected from well-known organisations for child safety. A large number of fall records of toddlers who had sought treatment at a hospital were analysed to identify a toddler’s fall risk factors. The factors include clutter being a tripping or slipping hazard on the floor and a toddler moving around or climbing furniture or room structures. The major technical problem in detecting the risk factors is to classify foreground objects into human and non-human, and novel approaches have been proposed for the classification. Unlike most existing studies, which focus on human appearance such as skin colour for human detection, the approaches addressed in this thesis use cues related to dynamic motions. The first cue is based on the fact that there is relative motion between human body parts while typical indoor clutter does not have such parts with diverse motions. In addition, other motion cues are employed to differentiate a human from a pet since a pet also moves its parts diversely. They are angle changes of ellipse fitted to each object and history of its actual heights to capture the various posture changes and different body size of pets. The methods work well as long as foreground regions are correctly segmented.
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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 extract the location of a persons Center of Pressure in real time. The proposed method utilized two different methods of acquiring depth information from the frames - stereoscopy through two RGB-cameras and with the use of one RGB-depth camera. The estimated location of the Center of Pressure were compared to the location of the same parameter extracted while using the force plate Wii Balance Board. As the proposed method were to operate in real-time and without the use of computational processor enhancement, the choice of human pose estimation model were aimed to maximize software input/output speed. Thus, three models were used - one smaller and faster model called Lightweight Pose Network, one larger and accurate model called High-Resolution Network and one model placing itself somewhere in between the two other models, namely Pose Residual Network. The proposed method showed promising results for a real-time method of acquiring balance parameters. Although the largest source of error were the acquisition of depth information from the cameras. The results also showed that using a smaller and faster human pose estimation model proved to be sufficient in relation to the larger more accurate models in real-time usage and without the use of computational processor enhancement.<br>Djupinlärningstekniken 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.
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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 nuvarande falldetekteringsanord- ningar genom att täcka de brister och tillgodose de behov som finns i nuvarande tekniker. De förbättringar som har kartlagts är att kunna ge en säkrare bedömning av patientens hälsa och kunna påkalla snabbare hjälpinsats när en fallolycka har inträffat. En annan förbättring är rörelsefriheten för äldre. De ska kunna befinna sig utomhus och ha möjlighet att utföra sina dagliga aktiviteter utan att vara be- gränsade av platspositionen. Sammanfattningsvis kan det konstateras att ett multisensorsystem i form av en prototyp har konstruerats för att täcka de brister som har kartlagts. Utöver att pro- totypen detekterar fall och kroppsrörelser med hjälp av en accelerometersensor innehåller den även en sensor för detektering av livstecken i from av EKG. Den in- nehåller dessutom cellulära och trådlösa nätverksanslutningar i form av GPRS och Wi-Fi för att möjliggöra rörelsefriheten hos äldre. Vidare innehåller prototypen en sensor för GPS som ger information om platsposition.<br>Accidental 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.
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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 plupart des appareils sont conçus pour une détection médicale spécifique et fonctionnent indépendamment les uns des autres. Il y a toujours un manque de cadre intégré avec une interopérabilité élevée et un support de surveillance en ligne continu pour une analyse de corrélation plus approfondie. Cette thèse est une étape vers un système de collecte de données à distance, complet et continu pour les personnes âgées présentant divers types de problèmes de santé. Notre esprit de recherche est motivé par la demande immédiate d'un système de surveillance de la santé à distance sans fil sécurisé et fiable pour les personnes âgées en résidence assistée, combinant diverses sources de données. Pour créer un système aussi complet, nous le divisons en sous-systèmes, afin de le rendre réalisable et facile à mettre en œuvre, nous permettant ainsi de mettre à jour chaque sous-système individuellement dans les études futures sans affecter les autres sous-systèmes intégrés. L'accent est mis sur un système complet de surveillance à distance de l'e- santé. La liste des principales contributions contient (1) proposer une nouvelle approche pour la sécurité des appareils surveillés et proposer une solution pour prévenir les attaques MiTM et réduire la consommation d'énergie, (2) nous proposons une détection de chute fiable, (3) étudier et développer une nouvelle méthode de reconnaissance des activités quotidiennes des patients âgés surveillés, (4) proposer une approche pour améliorer la fiabilité du système et réduire les fausses alarmes et les interventions inutiles, (5) proposer et développer un algorithme de conversion de la langue des signes en texte utilisant une analyse de fusion multi-capteurs. En conséquence, nous prévoyons de fournir un système de surveillance avec une précision fiable dans la détection d'événements anormaux et de déclencher une alarme lors de la détection de tels événements pour demander de l'aide et de l'assistance<br>Aging 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
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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 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.
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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 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.
Libros sobre el tema "Human Fall detection"
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Clark, Mary Higgins. Cradle Will Fall. Turtleback Books Distributed by Demco Media, 1991.
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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 interested in solving puzzles or in moral retribution than in exposing the misconceptions that make things seem ‘mysterious’ in the first place. Attentive to the historical and political dimensions of these very American tales, this new critical edition selects twenty-four tales and places the most popular - ‘The Fall of the House of Usher’, ‘The Masque of the Red Death’, The Murders in the Rue Morgue; and ‘The Purloined Letter’ - alongside less well-known travel narratives, metaphysical essays and political satires.
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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 the women who commit them, as well as the impact these crimes can have on victims. The women profiled were in positions of authority at churches, schools, sports institutions, and the home. Victims explain how these women exploited their positions of trust, planned their crimes, groomed their victims, deceived adults into not detecting their behavior, and how they did not stop even when they recognized the danger and the harm to themselves and their victims. Duncan addresses the issue of maternal sexual abuse answering questions about mothers who willingly sexual abuse their own children and at times commit child sexual abuse with other adults, as well as women who sexually abuse girls. Four types of female sex offenders are presented within the emerging research on this topic, along with questions regarding assessment, treatment, and management of female sex offenders in the community. It also addresses the controversial issues of female pedophilia and female sexual deviance within the context of what we know about human sexuality.
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.
Texto completoActas 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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