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Статті в журналах з теми "Microsoft Kinect v2"
Cai, Laisi, Ye Ma, Shuping Xiong, and Yanxin Zhang. "Validity and Reliability of Upper Limb Functional Assessment Using the Microsoft Kinect V2 Sensor." Applied Bionics and Biomechanics 2019 (February 11, 2019): 1–14. http://dx.doi.org/10.1155/2019/7175240.
Повний текст джерелаGray, Aaron D., Brad W. Willis, Marjorie Skubic, Zhiyu Huo, Swithin Razu, Seth L. Sherman, Trent M. Guess, et al. "Development and Validation of a Portable and Inexpensive Tool to Measure the Drop Vertical Jump Using the Microsoft Kinect V2." Sports Health: A Multidisciplinary Approach 9, no. 6 (August 28, 2017): 537–44. http://dx.doi.org/10.1177/1941738117726323.
Повний текст джерелаKurillo, Gregorij, Evan Hemingway, Mu-Lin Cheng, and Louis Cheng. "Evaluating the Accuracy of the Azure Kinect and Kinect v2." Sensors 22, no. 7 (March 23, 2022): 2469. http://dx.doi.org/10.3390/s22072469.
Повний текст джерелаCaruso, L., R. Russo, and S. Savino. "Microsoft Kinect V2 vision system in a manufacturing application." Robotics and Computer-Integrated Manufacturing 48 (December 2017): 174–81. http://dx.doi.org/10.1016/j.rcim.2017.04.001.
Повний текст джерелаGuffanti, Diego, Alberto Brunete, Miguel Hernando, Javier Rueda, and Enrique Navarro Cabello. "The Accuracy of the Microsoft Kinect V2 Sensor for Human Gait Analysis. A Different Approach for Comparison with the Ground Truth." Sensors 20, no. 16 (August 7, 2020): 4405. http://dx.doi.org/10.3390/s20164405.
Повний текст джерелаAyed, Ines, Antoni Jaume-i-Capó, Pau Martínez-Bueso, Arnau Mir, and Gabriel Moyà-Alcover. "Balance Measurement Using Microsoft Kinect v2: Towards Remote Evaluation of Patient with the Functional Reach Test." Applied Sciences 11, no. 13 (June 30, 2021): 6073. http://dx.doi.org/10.3390/app11136073.
Повний текст джерелаBenedetti, Elisa, Roberta Ravanelli, Monica Moroni, Andrea Nascetti, and Mattia Crespi. "Exploiting Performance of Different Low-Cost Sensors for Small Amplitude Oscillatory Motion Monitoring: Preliminary Comparisons in View of Possible Integration." Journal of Sensors 2016 (2016): 1–10. http://dx.doi.org/10.1155/2016/7490870.
Повний текст джерелаBanh, Tien Long, and Van Bien Bui. "First Experiences with Microsoft Kinect V2 for 3D Modelling of Mechanical Parts." Applied Mechanics and Materials 889 (March 2019): 329–36. http://dx.doi.org/10.4028/www.scientific.net/amm.889.329.
Повний текст джерелаKoontz, Alicia Marie, Ahlad Neti, Cheng-Shiu Chung, Nithin Ayiluri, Brooke A. Slavens, Celia Genevieve Davis, and Lin Wei. "Reliability of 3D Depth Motion Sensors for Capturing Upper Body Motions and Assessing the Quality of Wheelchair Transfers." Sensors 22, no. 13 (June 30, 2022): 4977. http://dx.doi.org/10.3390/s22134977.
Повний текст джерелаGau, Michael-Lian, Huong Yong Ting, Jackie Tiew-Wei Ting, Marcella Peter, and Khairunnisa Ibrahim. "Sarawak Traditional Dance Motion Analysis and Comparison using Microsoft Kinect V2." Green Intelligent Systems and Applications 2, no. 1 (April 17, 2022): 42–52. http://dx.doi.org/10.53623/gisa.v2i1.78.
Повний текст джерелаДисертації з теми "Microsoft Kinect v2"
Axtelius, Andreas, and Simon Asplund. "Designing technology promoting increased user body awareness: Using Microsoft Kinect V2." Thesis, KTH, Maskinkonstruktion (Inst.), 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-176277.
Повний текст джерелаDetta examensarbete undersöker möjligheten att använda Microsoft Kinect V2 som sensor vid design av teknik med syfte att öka en användares kroppsförståelse. Då den digitala tekniken både fysiskt samt funktionellt kommer närmare användarens kropp har ett intresse väckts rörande hur dessa digitala apparater bör förhålla sig till användaren. Detta kräver nya perspektiv som tillåter att designen inkluderar en användares kropp och dennes förhållande till sin kropp. Detta examensarbete utfördes vid Mobile Life, ett forskningscenter med fokus på människadatorinteraktion. Mobile Life använder sig av ett nytt förhållningsätt baserat på Somaestetik för att möta utmaningen med kroppsnära teknik. Tidigare har prototyper konstruerats med syftet att förstärka Feldenkreis-övningar med syfte att dra slutsatser angående fundamentala riktlinjer vid design av kroppsnära teknik. I detta examensarbete har även förhållningsättet hämtats från Somaestetik. Utgånsgpunkten för arbetet ligger dock i hur Kinect V2:s funktionalitet kan användas för att inkludera kroppen i design av teknik. Genom att använda metoderna ”Research through design” och ”double diamond model” utvecklades en rad prototyper med syfte att undersöka olika tillvägagångsätt. Inledningsvis utforskades Kinect V2 genom tre inriktningar. Dessa var andningscykeln, puls och små rörelser relaterade till balans. Med dessa tre utgångpunkter utvecklades fyra prototyper. De mest lovande versionerna av prototyperna utvärderades genom användartester där användarreflektion låg till grund för vidare utveckling under en iterationsbaserad prototypframtagningsprocess. Resultatet av examensarbetet visar att Kinect V2 är en sensor väl anpassad för denna typ av design. Studien visar att Kinect V2 har förmågan att finna små rörelser som är relaterade till en användares andningscykel. Algoritmen som utvecklades för att mäta andningsförloppet bedömdes dock otillräcklig för vidare implementation på grund av sporadiskt beteende. Kinect V2 har god förmåga att mäta små balansrelaterade rörelser och implementerades som styrsignal för en av de utvecklade prototyperna. Baserat på slutsatser från användartester genomförda under examensarbetet samt resultat från de prototyper som tidigare utvecklats av Mobile Life föreslås ett designkoncept för vidare utforskning. Detta koncept lyder: ”Ett system med mål att hjälpa en användare reflektera över en specifik del av sin kropp måste i sin interaktion med användaren framhäva eller förmedla en liknande förnimmelse som de användaren upplever.”
Tsakiraki, Eleni. "Real-time Head Motion Tracking for Brain Positron Emission Tomography using Microsoft Kinect V2." Thesis, KTH, Skolan för teknik och hälsa (STH), 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-189973.
Повний текст джерелаFong, Katherine KaYan. "IR-Depth Face Detection and Lip Localization Using Kinect V2." DigitalCommons@CalPoly, 2015. https://digitalcommons.calpoly.edu/theses/1425.
Повний текст джерелаSoldati, Luca. "Analisi sperimentale sull'utilizzo del Microsoft Kinect One come sistema di Body Tracking per la realta virtuale in riabilitazione." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2016. http://amslaurea.unibo.it/10177/.
Повний текст джерелаRocha, César Bruno Gomes. "Monitorização dos modelos de quebra-mares com o sensor Microsoft Kinect V2." Master's thesis, 2017. http://hdl.handle.net/10451/27530.
Повний текст джерелаO presente estudo tem como objetivo determinar os movimentos dos centros dos blocos de quebramares, movimentos resultantes da incidência ondas diretamente na estrutura onde os blocos se situam. A metodologia foi aplicada a um modelo de quebra-mar e as ondas foram geradas em laboratório, num ambiente controlado. Os dados necessários para o estudo do movimento foram obtidos utilizado o sensor da Microsoft Kinect V2, o que permitiu verificar a viabilidade da aplicação deste sensor para este tipo de monitorização. No decorrer do estudo foram desenvolvidos processos para aquisição e processamento dos dados, que tinham, como objetivo último o cálculo dos movimentos dos centros dos blocos. A metodologia foi aplicada, com sucesso, em duas situações. Em ambas foram determinados os centros geométricos de dois blocos o que permitiu estimar o movimento destes: num o deslocamento foi na ordem dos 4 cm e no outro caso na ordem dos 8 cm. Sendo deslocamentos superiores à dimensão dos blocos (a qual é da ordem de 3 cm), são considerados deslocamentos assinaláveis / relevantes. Também resultante do estudo foi a verificação que o Kinect v2 é um sensor que consegue dar resposta aos requisitos deste tipo de ensaio pois permitiu recolha de dados que foram utilizados para determinar, com sucesso, os deslocamentos dos centros geométricos de blocos. Este sensor foi utilizado num outro cenário: o estudo da deformação do modelo do leito do rio Mondego após descargas do descarregador de cheias de uma barragem. Neste caso em concreto, o objetivo seria a determinação das diferenças entre nuvens de pontos geradas a partir dos dados recolhidos pelo Kinect V2. Verificou-se que os resultados corresponderam às espectativas e conseguiu-se determinar, com sucesso, a erosão correspondente às descargas simuladas nos ensaios. Transformando os resultados para a dimensão real verificou-se que o leito do rio sofreria uma erosão na ordem dos 11 m e seria criada de uma barra de assoreamento no leito do rio.
This study the objective of determining the movements of the centers of breakwaters blocks, movements which would be generated by the incidence waves created in the laboratory and in a controlled environment. To acquire information required for motion study, we used the Microsoft Kinect V2 sensor, verifying the feasibility for this type of monitoring. During the study they were created processes of acquisition and processing of data, aimed for more automation as possible of the methodology applied for the calculation of the movements. In the course of the study it has been possible to determine the geometric center of the tetrapod’s successfully in two separate case studies having been a movement in the first case in the order of 4 cm, and in the second case in the order of 8 cm, and all movements exceeding 3 cm are considered highly significant movements, it can be concluded that the use of the Kinect V2 can determine the movements of the geometric centers was a success. It was also discussed a study scenario on the deformation of the Mondego bed after unloading the filled on a dam spillway, again Kinect V2 corresponded to the expectations and was successfully determine the corresponding erosion in the simulated discharges the generated tests, and been an erosion of around 11m and the creation of a silting bar in the river bed.
Частини книг з теми "Microsoft Kinect v2"
Gonzalez, Diego, Luis Imbiriba, and Frederico Jandre. "Could Postural Strategies Be Assessed with the Microsoft Kinect v2?" In IFMBE Proceedings, 725–28. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-10-9038-7_134.
Повний текст джерелаRabbani, Shourav Bin, Amin Ahsan Ali, and M. Ashraful Amin. "Using Microsoft Kinect V2 for Custom Upper-Limb Rehabilitation Exercises." In Advances in Intelligent Systems and Computing, 508–19. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-73689-7_49.
Повний текст джерелаRosa, Benedetta, Filippo Colombo Zefinetti, Andrea Vitali, and Daniele Regazzoni. "RGB-D Sensors as Marker-Less MOCAP Systems: A Comparison Between Microsoft Kinect V2 and the New Microsoft Kinect Azure." In Advances in Simulation and Digital Human Modeling, 359–67. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-79763-8_43.
Повний текст джерелаVan-Bien, Bui, Banh Tien Long, and Nguyen Duc-Toan. "Assessment of the Surface Roughness of Metal Mechanical Parts by Microsoft Kinect V2." In Springer Proceedings in Materials, 283–92. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-45120-2_24.
Повний текст джерелаBhatia, Vibha, Parveen Kalra, and Jagjit Singh Randhawa. "Upper Body Postural Analysis in Sitting Workplace Environment Using Microsoft Kinect V2 Sensor." In Smart Innovation, Systems and Technologies, 575–86. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-5977-4_49.
Повний текст джерелаСЕРЕДИН, ОЛЕГ СЕРГЕЕВИЧ, АНДРЕЙ ВАЛЕРИЕВИЧ КОПЫЛОВ та ДЕНИС СЕРГЕЕВИЧ РОДИОНОВ. "ИСПОЛЬЗОВАНИЕ MICROSOFT KINECT V2 ДЛЯ ДЕТЕКТИРОВАНИЯ ПАДЕНИЙ ЧЕЛОВЕКА". У ИНТЕЛЛЕКТУАЛИЗАЦИЯ ОБРАБОТКИ ИНФОРМАЦИИ, 140–41. TORUS PRESS, 2018. http://dx.doi.org/10.30826/idp201864.
Повний текст джерелаТези доповідей конференцій з теми "Microsoft Kinect v2"
Liu, Liang, and Sanjay Mehrotra. "Bed angle detection in hospital room using Microsoft Kinect V2." In 2016 IEEE 13th International Conference on Wearable and Implantable Body Sensor Networks (BSN). IEEE, 2016. http://dx.doi.org/10.1109/bsn.2016.7516273.
Повний текст джерелаLiu, Liang, and Sanjay Mehrotra. "Patient walk detection in hospital room using Microsoft Kinect V2." In 2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). IEEE, 2016. http://dx.doi.org/10.1109/embc.2016.7591701.
Повний текст джерелаNabipour, Maryam Sadat, Nima Arteghzadeh, S. Ali A. Moosavian, and Ali Nasr. "Visual servoing in a cable robot using Microsoft Kinect v2 sensor." In 2016 4th International Conference on Robotics and Mechatronics (ICROM). IEEE, 2016. http://dx.doi.org/10.1109/icrom.2016.7886803.
Повний текст джерелаLiu, Liang, and Sanjay Mehrotra. "Patient Associated Motion Detection with Optical Flow Using Microsoft Kinect V2." In 2017 IEEE/ACM International Conference on Connected Health: Applications, Systems and Engineering Technologies (CHASE). IEEE, 2017. http://dx.doi.org/10.1109/chase.2017.99.
Повний текст джерелаLiu, Liang, Melody Yin, and Sanjay Mehrotra. "Preventing venous thromboembolism in patients admitted by hospital using Microsoft Kinect V2." In 2015 IEEE Biomedical Circuits and Systems Conference (BioCAS). IEEE, 2015. http://dx.doi.org/10.1109/biocas.2015.7348283.
Повний текст джерелаCueva C., Wilson F., S. Hugo M. Torres, and M. John Kern. "7 DOF industrial robot controlled by hand gestures using microsoft kinect v2." In 2017 IEEE 3rd Colombian Conference on Automatic Control (CCAC). IEEE, 2017. http://dx.doi.org/10.1109/ccac.2017.8276455.
Повний текст джерелаTeke, Burak, Minna Lanz, Joni-Kristian Kamarainen, and Antti Hietanen. "Real-time and Robust Collaborative Robot Motion Control with Microsoft Kinect ® v2." In 2018 14th IEEE/ASME International Conference on Mechatronic and Embedded Systems and Applications (MESA). IEEE, 2018. http://dx.doi.org/10.1109/mesa.2018.8449156.
Повний текст джерелаShoryabi, Milad, Ali Foroutannia, and Alireza Rowhanimanesh. "A 3D Deep Learning Approach for Classification of Gait Abnormalities Using Microsoft Kinect V2 Sensor." In 2021 26th International Computer Conference, Computer Society of Iran (CSICC). IEEE, 2021. http://dx.doi.org/10.1109/csicc52343.2021.9420611.
Повний текст джерелаTarabini, Marco, Daniele Marchisotti, Remo Sala, Pietro Marzaroli, Hermes Giberti, and Michele Sculati. "A prototype for the automatic measurement of the hand dimensions using the Microsoft Kinect V2." In 2018 IEEE International Symposium on Medical Measurements and Applications (MeMeA). IEEE, 2018. http://dx.doi.org/10.1109/memea.2018.8438789.
Повний текст джерелаLiu, Liang, and Sanjay Mehrotra. "Detecting Out-of-Bed Activities to Prevent Pneumonia for Hospitalized Patient Using Microsoft Kinect V2." In 2016 IEEE First International Conference on Connected Health: Applications, Systems and Engineering Technologies (CHASE). IEEE, 2016. http://dx.doi.org/10.1109/chase.2016.74.
Повний текст джерела