Objectivization of traditional otoneurological examinations based on Kinect sensor Hautant's test based on Kinect

Bewegtes Lernen numerischer Kompetenzen

Psychologische Rundschau ◽

10.1026/0033-3042/a000302 ◽

2016 ◽

Vol 67 (2) ◽

pp. 102-109 ◽

Cited By ~ 6

Author(s):

Tanja Dackermann ◽

Ursula Fischer ◽

Ulrike Cress ◽

Hans-Christoph Nuerk ◽

Korbinian Moeller

Keyword(s):

Kinect Sensor ◽

Körperliche Bewegung

Zusammenfassung. Nicht nur in Konzepten wie der Bewegten Schule ist körperliche Bewegung zur Unterstützung des Lernens von großer Bedeutung. Inzwischen liegen erste empirische Befunde zum positiven Einfluss spezifischer körperlicher Bewegungen vor, wie zum Beispiel dem Einsatz der Finger beim Erstrechnen oder dem Laufen entlang eines Zahlenstrahls. Diese aktuellen Studien deuten darauf hin, dass Bewegung den Erwerb numerischer Konzepte unterstützen kann. Neue bewegungssensitive Eingabemedien (z. B. Tanzmatte, Kinect Sensor) ermöglichen nicht nur solche Bewegungen in der Interaktion mit einer Lernumgebung, sondern machen diese mess- und damit spezifisch nutzbar. Dadurch können Trainings realisiert werden, die gezielt den Zusammenhang von Zahlen und Raum und damit für die Ausprägung des mentalen Zahlenstrahls relevante Prozesse trainieren. Die Entwicklung solcher Trainings ist von besonderer Bedeutung, weil der mentale Zahlenstrahl wichtig für eine erfolgreiche numerisch-mathematische Entwicklung zu sein scheint. In diesem Artikel stellen wir neben den theoretischen Grundlagen eine Zusammenfassung der Ergebnisse verschiedener eigener Arbeiten zu verkörperlichten numerischen Trainings vor.

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Design and Implementation of a Gesture-Driven System for Intelligent Wheelchairs Based on the Kinect Sensor

ROBOT ◽

10.3724/spj.1218.2012.00110 ◽

2012 ◽

Vol 34 (1) ◽

pp. 110 ◽

Cited By ~ 10

Author(s):

Yuan LUO ◽

Yu XIE ◽

Yi ZHANG

Keyword(s):

Kinect Sensor ◽

Design And Implementation ◽

Driven System

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ROI Based Post Image Quality Assessment Technique on Multiple Localized Filtering Method On Kinect HD Sensor Mobile Robot

Kinetik Game Technology Information System Computer Network Computing Electronics and Control ◽

10.22219/kinetik.v3i2.598 ◽

2018 ◽

Vol 3 (2) ◽

pp. 145

Author(s):

Kholilatul Wardani ◽

Aditya Kurniawan

Keyword(s):

Image Quality ◽

Quality Assessment ◽

Image Quality Assessment ◽

Similarity Index ◽

Assessment Model ◽

Sensor Data ◽

Edge Region ◽

Kinect Sensor ◽

Image Region ◽

Filtering Technique

The ROI (Region of Interest) Image Quality Assessment is an image quality assessment model based on the SSI (Structural Similarity Index) index used in the specific image region desired to be assessed. Output assessmen value used by this image assessment model is 1 which means identical and -1 which means not identical. Assessment model of ROI Quality Assessment in this research is used to measure image quality on Kinect sensor capture result used in Mobile HD Robot after applied Multiple Localized Filtering Technique. The filter is applied to each capture sensor depth result on Kinect, with the aim to eliminate structural noise that occurs in the Kinect sensor. Assessment is done by comparing image quality before filter and after filter applied to certain region. The kinect sensor will be conditioned to capture a square black object measuring 10cm x 10cm perpendicular to a homogeneous background (white with RGB code 255,255,255). The results of kinect sensor data will be taken through EWRF 3022 by visual basic 6.0 program periodically 10 times each session with frequency 1 time per minute. The results of this trial show the same similar index (value 1: identical) in the luminance, contrast, and structural section of the edge region or edge region of the specimen. The value indicates that the Multiple Localized Filtering Technique applied to the noise generated by the Kinect sensor, based on the ROI Image Quality Assessment model has no effect on the image quality generated by the sensor.

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Gesture Controlled Mobile Robotic Arm for Elderly and Wheelchair People Assistance Using Kinect Sensor

2018 15th International Multi-Conference on Systems, Signals & Devices (SSD) ◽

10.1109/ssd.2018.8570547 ◽

2018 ◽

Author(s):

M. Ababneh ◽

H. Shaban ◽

D. AlShalabe ◽

D. Khader ◽

H. Mahameed ◽

...

Keyword(s):

Robotic Arm ◽

Kinect Sensor

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Three-Dimensional Morphological Measurement Method for a Fruit Tree Canopy Based on Kinect Sensor Self-Calibration

Agronomy ◽

10.3390/agronomy9110741 ◽

2019 ◽

Vol 9 (11) ◽

pp. 741 ◽

Cited By ~ 4

Author(s):

Haihui Yang ◽

Xiaochan Wang ◽

Guoxiang Sun

Keyword(s):

Intelligent Control ◽

Point Cloud ◽

Three Dimensional ◽

Fruit Tree ◽

Tree Canopy ◽

Morphological Parameters ◽

Kinect Sensor ◽

Self Calibration ◽

Morphological Measurement ◽

Tree Width

Perception of the fruit tree canopy is a vital technology for the intelligent control of a modern standardized orchard. Due to the complex three-dimensional (3D) structure of the fruit tree canopy, morphological parameters extracted from two-dimensional (2D) or single-perspective 3D images are not comprehensive enough. Three-dimensional information from different perspectives must be combined in order to perceive the canopy information efficiently and accurately in complex orchard field environment. The algorithms used for the registration and fusion of data from different perspectives and the subsequent extraction of fruit tree canopy related parameters are the keys to the problem. This study proposed a 3D morphological measurement method for a fruit tree canopy based on Kinect sensor self-calibration, including 3D point cloud generation, point cloud registration and canopy information extraction of apple tree canopy. Using 32 apple trees (Yanfu 3 variety) morphological parameters of the height (H), maximum canopy width (W) and canopy thickness (D) were calculated. The accuracy and applicability of this method for extraction of morphological parameters were statistically analyzed. The results showed that, on both sides of the fruit trees, the average relative error (ARE) values of the morphological parameters including the fruit tree height (H), maximum tree width (W) and canopy thickness (D) between the calculated values and measured values were 3.8%, 12.7% and 5.0%, respectively, under the V1 mode; the ARE values under the V2 mode were 3.3%, 9.5% and 4.9%, respectively; and the ARE values under the V1 and V2 merged mode were 2.5%, 3.6% and 3.2%, respectively. The measurement accuracy of the tree width (W) under the double visual angle mode had a significant advantage over that under the single visual angle mode. The 3D point cloud reconstruction method based on Kinect self-calibration proposed in this study has high precision and stable performance, and the auxiliary calibration objects are readily portable and easy to install. It can be applied to different experimental scenes to extract 3D information of fruit tree canopies and has important implications to achieve the intelligent control of standardized orchards.

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