Relevant bibliographies by topics / Microsoft Kinect v2

Academic literature on the topic 'Microsoft Kinect v2'

Author: Grafiati
Published: 10 December 2022
Last updated: 28 January 2023

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Journal articles on the topic "Microsoft Kinect v2"

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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.

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Objective. To quantify the concurrent accuracy and the test-retest reliability of a Kinect V2-based upper limb functional assessment system. Approach. Ten healthy males performed a series of upper limb movements, which were measured concurrently with Kinect V2 and the Vicon motion capture system (gold standard). Each participant attended two testing sessions, seven days apart. Four tasks were performed including hand to contralateral shoulder, hand to mouth, combing hair, and hand to back pocket. Upper limb kinematics were calculated using our developed kinematic model and the UWA model for Kinect V2 and Vicon. The interdevice coefficient of multiple correlation (CMC) and the root mean squared error (RMSE) were used to evaluate the validity of the kinematic waveforms. Mean absolute bias and Pearson’s r correlation were used to evaluate the validity of the angles at the points of target achieved (PTA) and the range of motion (ROM). The intersession CMC and RMSE and the intraclass correlation coefficient (ICC) were used to assess the test-retest reliability of Kinect V2. Main Results. Both validity and reliability are found to be task-dependent and plane-dependent. Kinect V2 had good accuracy in measuring shoulder and elbow flexion/extension angular waveforms (CMC>0.87), moderate accuracy of measuring shoulder adduction/abduction angular waveforms (CMC=0.69-0.82), and poor accuracy of measuring shoulder internal/external angles (CMC<0.6). We also found high test-retest reliability of Kinect V2 in most of the upper limb angular waveforms (CMC=0.75-0.99), angles at the PTA (ICC=0.65-0.91), and the ROM (ICC=0.68-0.96). Significance. Kinect V2 has great potential as a low-cost, easy implemented device for assessing upper limb angular waveforms when performing functional tasks. The system is suitable for assessing relative within-person change in upper limb motions over time, such as disease progression or improvement due to intervention.
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Gray, 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.

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Background: Noncontact anterior cruciate ligament (ACL) injury in adolescent female athletes is an increasing problem. The knee-ankle separation ratio (KASR), calculated at initial contact (IC) and peak flexion (PF) during the drop vertical jump (DVJ), is a measure of dynamic knee valgus. The Microsoft Kinect V2 has shown promise as a reliable and valid marker-less motion capture device. Hypothesis: The Kinect V2 will demonstrate good to excellent correlation between KASR results at IC and PF during the DVJ, as compared with a “gold standard” Vicon motion analysis system. Study Design: Descriptive laboratory study. Level of Evidence: Level 2. Methods: Thirty-eight healthy volunteer subjects (20 male, 18 female) performed 5 DVJ trials, simultaneously measured by a Vicon MX-T40S system, 2 AMTI force platforms, and a Kinect V2 with customized software. A total of 190 jumps were completed. The KASR was calculated at IC and PF during the DVJ. The intraclass correlation coefficient (ICC) assessed the degree of KASR agreement between the Kinect and Vicon systems. Results: The ICCs of the Kinect V2 and Vicon KASR at IC and PF were 0.84 and 0.95, respectively, showing excellent agreement between the 2 measures. The Kinect V2 successfully identified the KASR at PF and IC frames in 182 of 190 trials, demonstrating 95.8% reliability. Conclusion: The Kinect V2 demonstrated excellent ICC of the KASR at IC and PF during the DVJ when compared with the Vicon system. A customized Kinect V2 software program demonstrated good reliability in identifying the KASR at IC and PF during the DVJ. Clinical Relevance: Reliable, valid, inexpensive, and efficient screening tools may improve the accessibility of motion analysis assessment of adolescent female athletes.
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Kurillo, 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.

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The Azure Kinect represents the latest generation of Microsoft Kinect depth cameras. Of interest in this article is the depth and spatial accuracy of the Azure Kinect and how it compares to its predecessor, the Kinect v2. In one experiment, the two sensors are used to capture a planar whiteboard at 15 locations in a grid pattern with laser scanner data serving as ground truth. A set of histograms reveals the temporal-based random depth error inherent in each Kinect. Additionally, a two-dimensional cone of accuracy illustrates the systematic spatial error. At distances greater than 2.5 m, we find the Azure Kinect to have improved accuracy in both spatial and temporal domains as compared to the Kinect v2, while for distances less than 2.5 m, the spatial and temporal accuracies were found to be comparable. In another experiment, we compare the distribution of random depth error between each Kinect sensor by capturing a flat wall across the field of view in horizontal and vertical directions. We find the Azure Kinect to have improved temporal accuracy over the Kinect v2 in the range of 2.5 to 3.5 m for measurements close to the optical axis. The results indicate that the Azure Kinect is a suitable substitute for Kinect v2 in 3D scanning applications.
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Caruso, 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.

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Guffanti, 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.

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Several studies have examined the accuracy of the Kinect V2 sensor during gait analysis. Usually the data retrieved by the Kinect V2 sensor are compared with the ground truth of certified systems using a Euclidean comparison. Due to the Kinect V2 sensor latency, the application of a uniform temporal alignment is not adequate to compare the signals. On that basis, the purpose of this study was to explore the abilities of the dynamic time warping (DTW) algorithm to compensate for sensor latency (3 samples or 90 ms) and develop a proper accuracy estimation. During the experimental stage, six iterations were performed using the a dual Kinect V2 system. The walking tests were developed at a self-selected speed. The sensor accuracy for Euclidean matching was consistent with that reported in previous studies. After latency compensation, the sensor accuracy demonstrated considerably lower error rates for all joints. This demonstrated that the accuracy was underestimated due to the use of inappropriate comparison techniques. On the contrary, DTW is a potential method that compensates for the sensor latency, and works sufficiently in comparison with certified systems.
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Ayed, 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.

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To prevent falls, it is important to measure periodically the balance ability of an individual using reliable clinical tests. As Red Green Blue Depth (RGBD) devices have been increasingly used for balance rehabilitation at home, they may also be used to assess objectively the balance ability and determine the effectiveness of a therapy. For this, we developed a system based on the Microsoft Kinect v2 for measuring the Functional Reach Test (FRT); one of the most used balance clinical tools to predict falls. Two experiments were conducted to compare the FRT measures computed by our system using the Microsoft Kinect v2 with those obtained by the standard method, i.e., manually. In terms of validity, we found a very strong correlation between the two methods (r = 0.97 and r = 0.99 (p < 0.05), for experiments 1 and 2, respectively). However, we needed to correct the measurements using a linear model to fit the data obtained by the Kinect system. Consequently, a linear regression model has been applied and examining the regression assumptions showed that the model works well for the data. Applying the paired t-test to the data after correction indicated that there is no statistically significant difference between the measurements obtained by both methods. As for the reliability of the test, we obtained good to excellent within repeatability of the FRT measurements tracked by Kinect (ICC = 0.86 and ICC = 0.99, for experiments 1 and 2, respectively). These results suggested that the Microsoft Kinect v2 device is reliable and adequate to calculate the standard FRT.
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Benedetti, 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.

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We address the problem of low amplitude oscillatory motion detection through different low-cost sensors: a LIS3LV02DQ MEMS accelerometer, a Microsoft Kinect v2 range camera, and a uBlox 6 GPS receiver. Several tests were performed using a one-direction vibrating table with different oscillation frequencies (in the range 1.5–3 Hz) and small challenging amplitudes (0.02 m and 0.03 m). A Mikrotron EoSens high-resolution camera was used to give reference data. A dedicated software tool was developed to retrieve Kinect v2 results. The capabilities of the VADASE algorithm were employed to process uBlox 6 GPS receiver observations. In the investigated time interval (in the order of tens of seconds) the results obtained indicate that displacements were detected with the resolution of fractions of millimeters with MEMS accelerometer and Kinect v2 and few millimeters with uBlox 6. MEMS accelerometer displays the lowest noise but a significant bias, whereas Kinect v2 and uBlox 6 appear more stable. The results suggest the possibility of sensor integration both for indoor (MEMS accelerometer + Kinect v2) and for outdoor (MEMS accelerometer + uBlox 6) applications and seem promising for structural monitoring applications.
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Banh, 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.

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Most of types of laser distance measuring instrument cost hundreds of thousand dollars such as Atos scanner or Depth Camera that gives depth maps of space very fast. However, the handling is too complicated for non-professional users and the utilization of 3D reconstruction is very limited. This paper introduces a workflow of 3D reconstruction using a new cheaper instrument, the Microsoft Kinect. The first experiences with Microsoft Kinect v2 sensor are presented, and the ability of 3D modelling for mechanical parts is investigated. For this purpose, the point cloud on output data as well as a calibration approach are demonstrated.
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Koontz, 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.

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Wheelchair users must use proper technique when performing sitting-pivot-transfers (SPTs) to prevent upper extremity pain and discomfort. Current methods to analyze the quality of SPTs include the TransKinect, a combination of machine learning (ML) models, and the Transfer Assessment Instrument (TAI), to automatically score the quality of a transfer using Microsoft Kinect V2. With the discontinuation of the V2, there is a necessity to determine the compatibility of other commercial sensors. The Intel RealSense D435 and the Microsoft Kinect Azure were compared against the V2 for inter- and intra-sensor reliability. A secondary analysis with the Azure was also performed to analyze its performance with the existing ML models used to predict transfer quality. The intra- and inter-sensor reliability was higher for the Azure and V2 (n = 7; ICC = 0.63 to 0.92) than the RealSense and V2 (n = 30; ICC = 0.13 to 0.7) for four key features. Additionally, the V2 and the Azure both showed high agreement with each other on the ML outcomes but not against a ground truth. Therefore, the ML models may need to be retrained ideally with the Azure, as it was found to be a more reliable and robust sensor for tracking wheelchair transfers in comparison to the V2.
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Gau, 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.

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This research project aimed to develop a software program or an interactive dance motion analysis application that utilizes modern technology to preserve and maintain the Sarawak traditional dance culture. The software program employs the Microsoft Kinect V2 to collect the digital dance data. The proposed method analyses the collected dance data for comparison purposes only. The comparison process was executed by displaying a traditional dance on the screen where the user who wants to learn the traditional dance can follow it and obtain results on how similar the dance is compared to the recorded dance data. The comparison of the performed and recorded dance data was visualized in graph form. The comparison graph showed that the Microsoft Kinect V2 sensors were capable of comparing the dance motion but with minor glitches in detecting the joint orientation. Using better depth sensors would make the comparison more accurate and less likely to have problems with figuring out how the joints move.
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Dissertations / Theses on the topic "Microsoft Kinect v2"

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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.

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This master thesis investigates Kinect V2’s ability to be used as a sensor when designing technology with the aim of increasing the body awareness of a user. With a transition to an increasing number of different devices operating close to the user’s body, an interest in this subject has increased alongside it. These technologies demand a new approach which includes the user’s body into the design. This thesis was conducted at Mobile Life, a research center focused on the field human-computer interaction. Mobile Life uses a new approach based on Somaesthetics to include the complexity of humans in the interaction, mainly by exploring the therapy form Feldenkrais. Building prototypes based on Feldenkrais exercises, integrating technology into them, Mobile Life explores new ideas for fundamental user design. In this thesis the perspective of Somaesthetics is kept, but the functionality of Kinect V2 is used as a base point when investigating how to include the body in technology design. By utilizing research through design and the double diamond model, different prototypes were built to investigate various approaches. Initially three paths were chosen to investigate Kinect V2 from; the characteristics of breathing, heart rate and small movements related to balance. Based on this, four different prototypes were built. The most promising prototypes were then evaluated in user tests and the input was analyzed and used in the next prototype iteration. The thesis concludes that Kinect V2 is a potent sensor when facing the challenge of including the body in the interaction. It has the ability to detect the small movements related to a user’s respiratory cycle. However, the implemented algorithm was not capable of sufficiently mapping the breathing to an actuator with the requirements set up for the prototype. Small movements related to balance were measured without issue and the noise present in the sampled signal was filtered successfully without any delay affecting the prototype performance. Based on the knowledge gained during the master thesis, a new design concept is proposed for future investigation. This concept states that: “To build a system that helps the user reflect on a specific part of their body, the system must highlight or provide a similar sensation as the one felt in the user’s own body.”
Detta 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.”
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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.

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The scope of the current research work was to evaluate the potential of the latest version of Microsoft Kinect sensor (Kinect v2) as an external tracking device for head motion during brain imaging with brain Positron Emission Tomography (PET). Head movements constitute a serious degradation factor in the acquired PET images. Although there are algorithms implementing motion correction using known motion data, the lack of effective and reliable motion tracking hardware has prevented their widespread adoption. Thus, the development of effective external tracking instrumentation is a necessity. Kinect was tested both for Siemens High-Resolution Research Tomograph (HRRT) and for Siemens ECAT HR PET system. The face Application Programming Interface (API) ’HD face’ released by Microsoft in June 2015 was modified and used in Matlab environment. Multiple experimental sessions took place examining the head tracking accuracy of kinect both in translational and rotational movements of the head. The results were analyzed statistically using one-sample Ttests with the significance level set to 5%. It was found that kinect v2 can track the head with a mean spatial accuracy of µ0 < 1 mm (SD = 0,8 mm) in the y-direction of the tomograph’s camera, µ0 < 3 mm (SD = 1,5 mm) in the z-direction of the tomograph’s camera and µ0 < 1 ◦ (SD < 1 ◦ ) for all the angles. However, further validation needs to take place. Modifications are needed in order for kinect to be used when acquiring PET data with the HRRT system. The small size of HRRT’s gantry (over 30 cm in diameter) makes kinect’s tracking unstable when the whole head is inside the gantry. On the other hand, Kinect could be used to track the motion of the head inside the gantry of the HR system.
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Fong, Katherine KaYan. "IR-Depth Face Detection and Lip Localization Using Kinect V2." DigitalCommons@CalPoly, 2015. https://digitalcommons.calpoly.edu/theses/1425.

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Face recognition and lip localization are two main building blocks in the development of audio visual automatic speech recognition systems (AV-ASR). In many earlier works, face recognition and lip localization were conducted in uniform lighting conditions with simple backgrounds. However, such conditions are seldom the case in real world applications. In this paper, we present an approach to face recognition and lip localization that is invariant to lighting conditions. This is done by employing infrared and depth images captured by the Kinect V2 device. First we present the use of infrared images for face detection. Second, we use the face’s inherent depth information to reduce the search area for the lips by developing a nose point detection. Third, we further reduce the search area by using a depth segmentation algorithm to separate the face from its background. Finally, with the reduced search range, we present a method for lip localization based on depth gradients. Experimental results demonstrated an accuracy of 100% for face detection, and 96% for lip localization.
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Soldati, 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/.

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In questo progetto di tesi sarà innanzitutto presentato il Kinect One e sarà fatta una panoramica sull’uso della realtà virtuale in ambito riabilitativo. In seguito sarà analizzato l’algoritmo di Body tracking, valutandone il comportamento in diverse situazioni pratiche e poi stimandone la precisione in statica. Sarà presentato un filtraggio per limitare il rumore in tempo reale e valutarne i pro ed i contro in funzione delle caratteristiche impostabili. Saranno presentate inoltre le metodologie con cui gli algoritmi integrati del Kinect permettono di ricavare una stima dell’orientamento delle parti anatomiche nello spazio ed alcune considerazioni circa le implicazioni pratiche di tali metodologie, anche in base alle osservazioni sul campo ottenute durante i mesi di realizzazione di questo progetto. Lo scopo è determinare se e come sia possibile utilizzare il Microsoft Kinect One come unico sistema di motion tracking del paziente in applicazioni cliniche di riabilitazione, quali limiti ci sono nel suo utilizzo e quali categorie di scenari e prove potrebbe supportare.
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Rocha, 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.

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Trabalho de projeto de mestrado, Engenharia Geográfica, Universidade de Lisboa, Faculdade de Ciências, 2017
O 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.
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Book chapters on the topic "Microsoft Kinect v2"

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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.

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Rabbani, 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.

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Rosa, 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.

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Van-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.

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Bhatia, 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.

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СЕРЕДИН, ОЛЕГ СЕРГЕЕВИЧ, АНДРЕЙ ВАЛЕРИЕВИЧ КОПЫЛОВ, and ДЕНИС СЕРГЕЕВИЧ РОДИОНОВ. "ИСПОЛЬЗОВАНИЕ MICROSOFT KINECT V2 ДЛЯ ДЕТЕКТИРОВАНИЯ ПАДЕНИЙ ЧЕЛОВЕКА." In ИНТЕЛЛЕКТУАЛИЗАЦИЯ ОБРАБОТКИ ИНФОРМАЦИИ, 140–41. TORUS PRESS, 2018. http://dx.doi.org/10.30826/idp201864.

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Conference papers on the topic "Microsoft Kinect v2"

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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.

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Liu, 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.

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Nabipour, 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.

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Liu, 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.

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Liu, 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.

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Cueva 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.

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Teke, 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.

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Shoryabi, 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.

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Tarabini, 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.

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Liu, 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.

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