A prototype for the automatic measurement of the hand dimensions using the Microsoft Kinect V2

Tarabini, Marco; Marchisotti, Daniele; Sala, R.; Marzaroli, Pietro; Giberti, Hermes; Sculati, Michele

doi:10.1109/memea.2018.8438789

Cited by 5 publications

(12 citation statements)

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“…The minimum working distance of Kinect is 0.70 m. In these conditions, the field of view implies an observed area that is much larger than the hand size (a suitable measurement area can be defined by the size of an A4 piece of paper). In order to limit the effects of the different reflectivity of the reference surface and of the human skin, the subject is required to wear black silk gloves and to keep the instrument far from obstacles that may reflect or scatter IR radiation, as described in [23].…”

Section: Sensors' Characteristicsmentioning

confidence: 99%

“…The automation of the measurement process allows for the reduction of the variability due to the observer; although, the lack of a standardised model for the identification of specific hand parameters might introduce a discrepancy between different measurement algorithms. The dimensions, areas, and volumes of the hand were computed with the algorithms that are extensively described in [23]: the different hand features are identified using simple image processing algorithms that are based on the extraction of the hand silhouette identified from the binarized 2D image of the hand by detecting the borders of the hand. The flowchart of the method is shown in Figure 2.…”

Section: Algorithmsmentioning

confidence: 99%

“…artefacts. Conventional image processing algorithms (image derivatives or contours extraction starting from the binary image) were used to identify the hand features parameters described in Section 2.1 [23].…”

Section: Algorithmsmentioning

confidence: 99%

“…The hand thickness measured by the 3D cameras was compared to that which was measured by a triangulation laser sensor Micro-epsilon optoNCDT 1402, with a measurement range of 50 mm and a resolution of 5 µm [23]. The results reported in this work refer to the thickness measured at the centre of the middle fingernail; the finger represents a challenging position for measuring the thickness, given that small pressure variations at the fingertip result in a large variation of the measured height.…”

Section: Thicknessmentioning

confidence: 99%

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Automatic measurement of the hand dimensions using consumer 3D cameras

et al. 2020

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<p class="Abstract">This article describes the metrological characterisation of two prototypes that use the point clouds acquired by consumer 3D cameras for the measurement of the human hand geometrical parameters. The initial part of the work is focused on the general description of algorithms that allow for the derivation of dimensional parameters of the hand. Algorithms were tested on data acquired using Microsoft Kinect v2 and Intel RealSense D400 series sensors. The accuracy of the proposed measurement methods has been evaluated in different tests aiming to identify bias errors deriving from point-cloud inaccuracy and at the identification of the effect of the hand pressure and the wrist flexion/extension. Results evidenced an accuracy better than 1 mm in the identification of the hand’s linear dimension and better than 20 cm<sup>3 </sup>for hand volume measurements. The relative uncertainty of linear dimensions, areas, and volumes was in the range of 1-10 %. Measurements performed with the Intel RealSense D400 were, on average, more repeatable than those performed with Microsoft Kinect. The uncertainty values limit the use of these devices to applications where the requested accuracy is larger than 5 % (volume measurements), 3 % (area measurements), and 1 mm (hands’ linear dimensions and thickness).</p>

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Section: Sensors' Characteristicsmentioning

confidence: 99%

Section: Algorithmsmentioning

confidence: 99%

Section: Algorithmsmentioning

confidence: 99%

Section: Thicknessmentioning

confidence: 99%

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Automatic measurement of the hand dimensions using consumer 3D cameras

et al. 2020

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“…We can consider that knowing this limitation, the equipment can be used for well-thought-out situations. Also, it is central to notice the low-cost aspect is beneficial for applications that have cost constraints (GLEASON et al, 2018;TARABINI et al, 2018;VITALI;RIZZI, 2018).…”

Section: Introductionmentioning

confidence: 99%

Avaliação de posturas para antropometria digital considerando tempo, conforto e qualidade da malha obtida

Sierra¹,

Barbosa²,

Rocha³

et al. 2019

Blucher Design Proceedings

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Ao realizar estudos antropométricos digitais em larga escala, além da seleção dos equipamentos, os pesquisadores devem considerar a participação do usuário e o seu próprio trabalho de pós-processamento. Tomou-se como hipótese que ao usar scanners de baixo custo, as principais influências na qualidade da malha são as posturas adotadas e o tempo de varredura, o que afeta a capacidade de manter a postura. Para testar a hipótese, desenvolveu-se um experimento para determinar as melhores posturas, considerando as normas, limitações do equipamento, qualidade da malha obtida, tempo necessário para realizar as digitalizações, análises ergonômicas da postura, conforto dos participantes e nosso hardware de digitalização. Em suma, 16 participantes assinaram acordos de consentimento e foram escaneados três vezes, uma em cada postura, em ordem aleatória, declarando seu nível de conforto no final de cada uma. A plataforma de digitalização utilizada funciona com 3 Kinect 360. Em seguida, as malhas foram reparadas e classificadas. A postura selecionada foi a que proporcionou a melhor qualidade de malha e foi considerada a mais confortável para os participantes. Encontrou-se que as posturas adotadas tiveram mais influência na qualidade da malha, do que o tempo ou o número de voltas para a digitalização. Digitalização corporal 3D, antropometria digital, ergonomia, postura, conforto

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