Bibliografías temáticas / Classification des postures

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Literatura académica sobre el tema "Classification des postures"

Autor: Grafiati
Publicado: 4 de junio de 2021
Última modificación: 5 de febrero de 2022

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Artículos de revistas sobre el tema "Classification des postures"

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Kim, Yong, Youngdoo Son, Wonjoon Kim, Byungki Jin, and Myung Yun. "Classification of Children’s Sitting Postures Using Machine Learning Algorithms." Applied Sciences 8, no. 8 (2018): 1280. http://dx.doi.org/10.3390/app8081280.

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Sitting on a chair in an awkward posture or sitting for a long period of time is a risk factor for musculoskeletal disorders. A postural habit that has been formed cannot be changed easily. It is important to form a proper postural habit from childhood as the lumbar disease during childhood caused by their improper posture is most likely to recur. Thus, there is a need for a monitoring system that classifies children’s sitting postures. The purpose of this paper is to develop a system for classifying sitting postures for children using machine learning algorithms. The convolutional neural netw
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Kim, Wonjoon, Byungki Jin, Sanghyun Choo, Chang S. Nam, and Myung Hwan Yun. "Designing of smart chair for monitoring of sitting posture using convolutional neural networks." Data Technologies and Applications 53, no. 2 (2019): 142–55. http://dx.doi.org/10.1108/dta-03-2018-0021.

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Purpose Sitting in a chair is a typical act of modern people. Prolonged sitting and sitting with improper postures can lead to musculoskeletal disorders. Thus, there is a need for a sitting posture classification monitoring system that can predict a sitting posture. The purpose of this paper is to develop a system for classifying children’s sitting postures for the formation of correct postural habits. Design/methodology/approach For the data analysis, a pressure sensor of film type was installed on the seat of the chair, and image data of the postu.re were collected. A total of 26 children pa
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Hwang, Jaejin, and Kyung-Sun Lee. "Classification of Whole-Body Postural Discomfort Using Cluster Analysis." International Journal of Environmental Research and Public Health 17, no. 22 (2020): 8314. http://dx.doi.org/10.3390/ijerph17228314.

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The objectives of this study were to evaluate the effect of gender and postures of the neck, trunk, and knee on overall postural discomfort, and to classify combined postures into different postural discomfort groups. A total of 95 participants (42 males and 53 females) performed 45 different static postures, which were a combination of 3 neck angles, 5 trunk angles, and 3 knee angles, and rated the perceived postural discomfort. Non-hierarchical K-means cluster analysis was employed to classify the 45 different combined postures into several postural discomfort groups. Postural discomfort was
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Martins, Leonardo, Rui Lucena, Rui Almeida, et al. "Intelligent Chair Sensor." International Journal of System Dynamics Applications 3, no. 2 (2014): 65–80. http://dx.doi.org/10.4018/ijsda.2014040105.

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In order to develop an intelligent system capable of posture classification and correction the authors developed a chair prototype equipped with air bladders in the chair's seat pad and backrest, which can in turn detect the user posture based on the pressure inside said bladders and change their conformation by inflation or deflation. Pressure maps for eleven standardized postures were gathered in order to automatically detect the user's posture, with resource to neural networks classifiers. First the authors tried to find the best parameters for the neural network classification of our data,
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Klishkovskaia, Tatiana, Andrey Aksenov, Aleksandr Sinitca, Anna Zamansky, Oleg A. Markelov, and Dmitry Kaplun. "Development of Classification Algorithms for the Detection of Postures Using Non-Marker-Based Motion Capture Systems." Applied Sciences 10, no. 11 (2020): 4028. http://dx.doi.org/10.3390/app10114028.

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The rapid development of algorithms for skeletal postural detection with relatively inexpensive contactless systems and cameras opens up the possibility of monitoring and assessing the health and wellbeing of humans. However, the evaluation and confirmation of posture classifications are still needed. The purpose of this study was therefore to develop a simple algorithm for the automatic classification of human posture detection. The most affordable solution for this project was through using a Kinect V2, enabling the identification of 25 joints, so as to record movements and postures for data
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KWON, YURI, JI-WON KIM, JAE-HOON HEO, HYEONG-MIN JEON, EUI-BUM CHOI, and GWANG-MOON EOM. "CLASSIFICATION OF SPINAL POSTURES DURING CROSS-LEGGED SITTING ON THE FLOOR." Journal of Mechanics in Medicine and Biology 19, no. 08 (2019): 1940056. http://dx.doi.org/10.1142/s0219519419400566.

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One of the most frequent sitting styles of Asians in everyday life is a cross-legged sitting. The cross-legged sitting results in higher compression load in spine than sitting on a chair, so a proper sitting posture is more needed. The purpose of this study was to classify the spinal posture during cross-legged sitting from the seat pressure pattern for future usage in the posture monitoring system. Twenty young men participated in this study. The seat pressure was measured for three spinal postures of flat, slump, and lordosis when subjects were instructed to pose a certain posture while seat
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Alinia, Parastoo, Ali Samadani, Mladen Milosevic, Hassan Ghasemzadeh, and Saman Parvaneh. "Pervasive Lying Posture Tracking." Sensors 20, no. 20 (2020): 5953. http://dx.doi.org/10.3390/s20205953.

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Automated lying-posture tracking is important in preventing bed-related disorders, such as pressure injuries, sleep apnea, and lower-back pain. Prior research studied in-bed lying posture tracking using sensors of different modalities (e.g., accelerometer and pressure sensors). However, there remain significant gaps in research regarding how to design efficient in-bed lying posture tracking systems. These gaps can be articulated through several research questions, as follows. First, can we design a single-sensor, pervasive, and inexpensive system that can accurately detect lying postures? Seco
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Laudanski, Annemarie F., and Stacey M. Acker. "Classification of high knee flexion postures using EMG signals." Work 68, no. 3 (2021): 701–9. http://dx.doi.org/10.3233/wor-203404.

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BACKGROUND: High knee flexion postures are often adopted in occupational settings and may lead to increased risk of knee osteoarthritis. Pattern recognition algorithms using wireless electromyographic (EMG) signals may be capable of detecting and quantifying occupational exposures throughout a working day. OBJECTIVE: To develop a k-Nearest Neighbor (kNN) algorithm for the classification of eight high knee flexion activities frequently observed in childcare. METHODS: EMG signals from eight lower limb muscles were recorded for 30 participants, signals were decomposed into time- and frequency-dom
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Zhao, Mingming, Georges Beurier, Hongyan Wang, and Xuguang Wang. "Exploration of Driver Posture Monitoring Using Pressure Sensors with Lower Resolution." Sensors 21, no. 10 (2021): 3346. http://dx.doi.org/10.3390/s21103346.

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Pressure sensors are good candidates for measuring driver postural information, which is indicative for identifying driver’s intention and seating posture. However, monitoring systems based on pressure sensors must overcome the price barriers in order to be practically feasible. This study, therefore, was dedicated to explore the possibility of using pressure sensors with lower resolution for driver posture monitoring. We proposed pressure features including center of pressure, contact area proportion, and pressure ratios to recognize five typical trunk postures, two typical left foot postures
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Tam, Andy Yiu-Chau, Bryan Pak-Hei So, Tim Tin-Chun Chan, Alyssa Ka-Yan Cheung, Duo Wai-Chi Wong, and James Chung-Wai Cheung. "A Blanket Accommodative Sleep Posture Classification System Using an Infrared Depth Camera: A Deep Learning Approach with Synthetic Augmentation of Blanket Conditions." Sensors 21, no. 16 (2021): 5553. http://dx.doi.org/10.3390/s21165553.

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Surveillance of sleeping posture is essential for bed-ridden patients or individuals at-risk of falling out of bed. Existing sleep posture monitoring and classification systems may not be able to accommodate the covering of a blanket, which represents a barrier to conducting pragmatic studies. The objective of this study was to develop an unobtrusive sleep posture classification that could accommodate the use of a blanket. The system uses an infrared depth camera for data acquisition and a convolutional neural network to classify sleeping postures. We recruited 66 participants (40 men and 26 w
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Tesis sobre el tema "Classification des postures"

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Mousse, Ange Mikaël. "Reconnaissance d'activités humaines à partir de séquences multi-caméras : application à la détection de chute de personne." Thesis, Littoral, 2016. http://www.theses.fr/2016DUNK0453/document.

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La vision artificielle est un domaine de recherche en pleine évolution. Les nouvelles stratégies permettent d'avoir des réseaux de caméras intelligentes. Cela induit le développement de beaucoup d'applications de surveillance automatique via les caméras. Les travaux développés dans cette thèse concernent la mise en place d'un système de vidéosurveillance intelligente pour la détection de chutes en temps réel. La première partie de nos travaux consiste à pouvoir estimer de façon robuste la surface d'une personne à partir de deux (02) caméras ayant des vues complémentaires. Cette estimation est
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Eraso, Natalia. "Gramática tanimuka, una lengua de la Amazonía colombiana." Thesis, Lyon 2, 2015. http://www.theses.fr/2015LYO20076/document.

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Cette grammaire du tanimuka, langue tucano d’Amazonie colombienne, s’organise en quatre parties. La première partie, ‘le tanimuka : la langue et ses locuteurs’, est divisée en trois chapitres (1 à 3), qui établissent le profil sociolinguistique de la langue, et décrivent les conditions d’enquête de terrain. La deuxième partie, divisée en deux chapitres (5 et 6), présente une description de la phonologie et morphophonologie de la langue, avec une description du phénomène d’épenthèse syllabique dans le verbe. La troisième partie (chapitres 7 et 8) présente d’abord une étude générale de la gramma
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Fidahoussen, Hassanaly Chaïna. "Entre postures, textes et contexte, pour une réflexion exhaustive sur la littérature orale malgache : pratiques discursives sur le concept d'oralité depuis platon, examen terminologique, génétique et taxinomique de la littérature orale malgache, étude du champ littéraire orale à Madagascare." Paris 13, 2008. http://www.theses.fr/2008PA131036.

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Cette étude sur la littérature orale malgache est une contribution originale qui cherche à combler un vide théorique, du moins en partie, sur divers aspects peu soulevés ou non, aussi bien par les malgachisants que d’autres chercheurs. Dans un double mouvement entre rappels théoriques et examen du cas malgache, nous embrassons une amplitude extrêmement vaste pour explorer des questions aussi bien universelles que proprement malgaches. À cet effet, nous remontons dans le temps aux époques présocratique et platonicienne, premiers à avoir rationalisé l’art et la littérature. Nous parcourons égale
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Aloqlah, Mohammed. "A Headband-Integrated Wireless Accelerometer System for Real-Time Posture Classification and Safety Monitoring." Case Western Reserve University School of Graduate Studies / OhioLINK, 2010. http://rave.ohiolink.edu/etdc/view?acc_num=case1278985875.

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Safi, Khaled. "Human postural stability analysis : application to Parkinsonian subjects." Thesis, Paris Est, 2016. http://www.theses.fr/2016PESC1066/document.

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L’analyse de la stabilité posturale chez l’homme a fait l’objet, ces dernières années, d’un intérêt grandissant au sein de la communauté scientifique. Le système postural permet de maintenir la stabilité du corps humain en posture statique ou dynamique. Cette capacité à maintenir cette stabilité devient critique dans le cas des sujets Parkinsoniens. La maladie de Parkinson a en effet une forte incidence sur la stabilité posturale. Un moyen efficace pour évaluer l’équilibre postural consiste à analyser les déplacements dans le plan horizontal du centre de pression du corps humain en posture ort
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Zhang, Jian. "Support Vector Machines (SVMs) Based Framework for Classification of Fallers and Non-Fallers." Diss., Virginia Tech, 2014. http://hdl.handle.net/10919/64189.

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The elderly population is growing at a rapid pace, and falls are a significant problem facing adults aged 65 and older in terms of both human suffering and economic losses. Falls are the leading cause of mortality among older adults, and non-fatal falls result in reduced function and poor quality of life for older adults. Although much is known about the mechanisms and contributing risk factors relevant to falls, falls still remain a significant problem associated with this age group. Therefore, new strategies and knowledge need to be introduced to understand and prevent falls. Studies show
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Fu, Teng. "IntelliChair : a non-intrusive sitting posture and sitting activity recognition system." Thesis, Abertay University, 2015. https://rke.abertay.ac.uk/en/studentTheses/5b60a500-c3fc-4a79-9028-d7909e01b78c.

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Current Ambient Intelligence and Intelligent Environment research focuses on the interpretation of a subject’s behaviour at the activity level by logging the Activity of Daily Living (ADL) such as eating, cooking, etc. In general, the sensors employed (e.g. PIR sensors, contact sensors) provide low resolution information. Meanwhile, the expansion of ubiquitous computing allows researchers to gather additional information from different types of sensor which is possible to improve activity analysis. Based on the previous research about sitting posture detection, this research attempts to furthe
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Durango, Melisa de Jesus Barrera. "SIRAH : sistema de reconhecimento de atividades humanas e avaliação do equilibrio postural /." Ilha Solteira, 2017. http://hdl.handle.net/11449/151317.

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Orientador: Alexandre César Rodrigues da Silva<br>Resumo: O reconhecimento de atividades humanas abrange diversas técnicas de classificação que permitem identificar padrões específicos do comportamento humano no momento da ocorrência. A identificação é realizada analisando dados gerados por diversos sensores corporais, entre os quais destaca-se o acelerômetro, pois responde tanto à frequência como à intensidade dos movimentos. A identificação de atividades é uma área bastante explorada. Porém, existem desafios que necessitam ser superados, podendo-se mencionar a necessidade de sistemas leves,
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Mühlhan, Markus, Michael Marxen, Julia Landsiedel, Hagen Malberg, and Sebastian Zaunseder. "The effect of body posture on cognitive performance: a question of sleep quality." Saechsische Landesbibliothek- Staats- und Universitaetsbibliothek Dresden, 2014. http://nbn-resolving.de/urn:nbn:de:bsz:14-qucosa-147476.

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Nearly all functional magnetic resonance imaging (fMRI) studies are conducted in the supine body posture, which has been discussed as a potential confounder of such examinations. The literature suggests that cognitive functions, such as problem solving or perception, differ between supine and upright postures. However, the effect of posture on many cognitive functions is still unknown. Therefore, the aim of the present study was to investigate the effects of body posture (supine vs. sitting) on one of the most frequently used paradigms in the cognitive sciences: the N-back working memory parad
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Boughnim, Nabil. "Traitement du signal pour la reconnaissance de gestes et application à une interface homme machine sans contact." Ecole centrale de Marseille, 2013. http://www.theses.fr/2013ECDM0004.

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Libros sobre el tema "Classification des postures"

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Bezdek, James C. Classification of posture maintenance data with fuzzy clustering algorithms: Final report. Research Institute for Computing and Information Systems, University of Houston-Clear Lake, 1992.

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Bezdek, James C. Classification of posture maintenance data with fuzzy clustering algorithms: Interim progress report. Research Institute for Computing and Information Systems, University of Houston-Clear Lake, 1991.

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Carr, Lucinda J. An overview of cerebral palsy. Oxford University Press, 2011. http://dx.doi.org/10.1093/med/9780199550647.003.013003.

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♦ Cerebral palsy is a permanent disorder of movement or posture due to non-progressive lesions of the immature brain♦ Magnetic resonance imaging is generally recommended and can provide important prognostic information♦ Only a minority of cases are due to birth trauma♦ Modern classification systems incorporate function as well as anatomical involvement♦ Management aims to maximize a child’s potential both as an individual and within society. Other associated conditions may impact on quality of life and participation♦ Multidisciplinary involvement is essential with use of physiotherapy, orthoti
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Capítulos de libros sobre el tema "Classification des postures"

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Samaraweera, WJ, SC Premaratne, and AT Dharmaratne. "Deep Learning for Classification of Cricket Umpire Postures." In Communications in Computer and Information Science. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-63823-8_64.

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Zerrouki, N., and A. Houacine. "Automatic Classification of Human Body Postures Based on Curvelet Transform." In Lecture Notes in Computer Science. Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-11758-4_36.

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Ugulino, Wallace, Débora Cardador, Katia Vega, Eduardo Velloso, Ruy Milidiú, and Hugo Fuks. "Wearable Computing: Accelerometers’ Data Classification of Body Postures and Movements." In Advances in Artificial Intelligence - SBIA 2012. Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-34459-6_6.

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Große-Puppendahl, Tobias Alexander, Alexander Marinc, and Andreas Braun. "Classification of User Postures with Capacitive Proximity Sensors in AAL-Environments." In Lecture Notes in Computer Science. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-25167-2_43.

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Pontes, Bruno, Marcio Cunha, Rafael Pinho, and Hugo Fuks. "Human-Sensing: Low Resolution Thermal Array Sensor Data Classification of Location-Based Postures." In Distributed, Ambient and Pervasive Interactions. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-58697-7_33.

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Kotakehara, Yuki, Koh Kakusho, Satoshi Nishiguchi, Masaaki Iiyama, and Masayuki Murakami. "The Classification of Different Situations in a Lecture Based on Students’ Observed Postures." In Cross-Cultural Design. Culture and Society. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-22580-3_33.

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Goshorn, Deborah, Juan Wachs, and Mathias Kölsch. "The Multi-level Learning and Classification of Multi-class Parts-Based Representations of U.S. Marine Postures." In Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications. Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-10268-4_59.

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Meshchikhin, I. A., and S. S. Gavriushin. "Methodology for the Classification of Human Locomotion’s and Postures for the Control System of a Bionic Prosthesis." In Advances in Intelligent Systems, Computer Science and Digital Economics II. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-80478-7_11.

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Lloyd, Matthew G., and Satish R. Raj. "Pathophysiology and Classification of PoTS." In Postural Tachycardia Syndrome. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-54165-1_5.

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Andersen, George J. "Classification of Visual Processes for the Control of Posture and Locomotion." In Multisensory Control of Posture. Springer US, 1995. http://dx.doi.org/10.1007/978-1-4615-1931-7_16.

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Actas de conferencias sobre el tema "Classification des postures"

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Zhao, Chihang, Bailing Zhang, Jie Lian, Jie He, Tao Lin, and Xiaoxiao Zhang. "Classification of Driving Postures by Support Vector Machines." In Graphics (ICIG). IEEE, 2011. http://dx.doi.org/10.1109/icig.2011.184.

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Zakarria, Noor Syuhadah, and Loh Wei Ping. "Static cycling postures classification analysis: A data mining approach." In THE 4TH INNOVATION AND ANALYTICS CONFERENCE & EXHIBITION (IACE 2019). AIP Publishing, 2019. http://dx.doi.org/10.1063/1.5121143.

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Mealin, Sean, Ignacio X. Domínguez, and David L. Roberts. "Semi-supervised classification of static canine postures using the Microsoft Kinect." In ACI '16: Third International Conference on Animal-Computer Interaction. ACM, 2016. http://dx.doi.org/10.1145/2995257.3012024.

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Wenlong Tang and E. S. Sazonov. "Highly accurate classification of postures and activities by a shoe-based monitor through classification with rejection." In 2012 34th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). IEEE, 2012. http://dx.doi.org/10.1109/embc.2012.6346499.

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Wientapper, Folker, Katrin Ahrens, Harald Wuest, and Ulrich Bockholt. "Linear-projection-based classification of human postures in time-of-flight data." In 2009 IEEE International Conference on Systems, Man and Cybernetics - SMC. IEEE, 2009. http://dx.doi.org/10.1109/icsmc.2009.5346892.

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Takimoto, Hironori, Seiki Yoshimori, Yasue Mitsukura, and Minoru Fukumi. "Classification of hand postures based on 3D vision model for human-robot interaction." In 2010 RO-MAN: The 19th IEEE International Symposium on Robot and Human Interactive Communication. IEEE, 2010. http://dx.doi.org/10.1109/roman.2010.5598646.

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Kiasari, Mohammad Ahangar, Seung You Na, and Jin Young Kim. "Classification of Human Postures Using Ultra-Wide Band Radar Based on Neural Networks." In 2014 International Conference on IT Convergence and Security (ICITCS). IEEE, 2014. http://dx.doi.org/10.1109/icitcs.2014.7021751.

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Harm, Holger, Oliver Amft, Daniel Roggen, and Gerhard Tröster. "SMASH: A Distributed Sensing and Processing Garment for the Classification of Upper Body Postures." In 3rd International ICST Conference on Body Area Networks. ICST, 2008. http://dx.doi.org/10.4108/icst.bodynets2008.2955.

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Tang, Hui, Jie He, Youfeng Zheng, Jun Zhang, and Lin Wei. "Video-based detection and classification of driving postures by feature distance extraction and BP neutral network." In Fourth International Workshop on Pattern Recognition, edited by Zhenxiang Chen, Xudong Jiang, and Guojian Chen. SPIE, 2019. http://dx.doi.org/10.1117/12.2540471.

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Seo, JoonOh, Kaiqi Yin, and SangHyun Lee. "Automated Postural Ergonomic Assessment Using a Computer Vision-Based Posture Classification." In Construction Research Congress 2016. American Society of Civil Engineers, 2016. http://dx.doi.org/10.1061/9780784479827.082.

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