scholarly journals Recognition and Assessment of Tae Kwon Do Moves using Kinect Camera Sensor

2019 ◽  
Vol 182 (47) ◽  
pp. 20-27
Author(s):  
Ahmad Ashari ◽  
M. Idham ◽  
Y. Fendryan
Keyword(s):  
Tae Kwon Do ◽  
Kinect Camera ◽  
Camera Sensor

Kinect camera sensor-based object tracking and following of four wheel independent steering automatic guided vehicle using Kalman filter

2015 ◽  
Author(s):  
Amruta V. Gulalkari ◽  
Dongbo Sheng ◽  
Pandu Sandi Pratama ◽  
Hak Kyeong Kim ◽  
Gi Sig Byun ◽  
...  
Keyword(s):  
Kalman Filter ◽  
Object Tracking ◽  
Kinect Camera ◽  
Automatic Guided Vehicle ◽  
Camera Sensor

Motion Recognition System with Dynamic Time Warping Method using Kinect Camera Sensor

2021 ◽  
Author(s):  
Sri Wahyuni ◽  
Rismayani ◽  
Indo Intan ◽  
Ahyuna Ahyuna ◽  
Suryani Suryani
Keyword(s):  
Dynamic Time Warping ◽  
Recognition System ◽  
Time Warping ◽  
Motion Recognition ◽  
Kinect Camera ◽  
Dynamic Time ◽  
Camera Sensor

A Study on the Way of Training for physical fitness for players of Tae Kwon Do Demonstration and Gyurugi(Competition)

2017 ◽  
Vol 26 (4) ◽  
pp. 1217-1225
Author(s):  
Tae-Won Kwon ◽  
Hye-Soo Cho
Keyword(s):  
Physical Fitness ◽  
Tae Kwon Do ◽  
The Way

Indian Sign Language Recognition on PYNQ Board

2020 ◽  
Vol 13 ◽  
Author(s):  
Sukhendra Singh ◽  
G. N. Rathna ◽  
Vivek Singhal
Keyword(s):  
Sign Language ◽  
Hand Gesture ◽  
Language Recognition ◽  
Sign Language Recognition ◽  
Hand Gestures ◽  
Depth Images ◽  
Kinect Camera ◽  
Impaired People ◽  
Web Camera ◽  
Indian Sign Language

Introduction: Sign language is the only way to communicate for speech-impaired people. But this sign language is not known to normal people so this is the cause of barrier in communicating. This is the problem faced by speech impaired people. In this paper, we have presented our solution which captured hand gestures with Kinect camera and classified the hand gesture into its correct symbol. Method: We used Kinect camera not the ordinary web camera because the ordinary camera does not capture its 3d orientation or depth of an image from camera however Kinect camera can capture 3d image and this will make classification more accurate. Result: Kinect camera will produce a different image for hand gestures for ‘2’ and ‘V’ and similarly for ‘1’ and ‘I’ however, normal web camera will not be able to distinguish between these two. We used hand gesture for Indian sign language and our dataset had 46339, RGB images and 46339 depth images. 80% of the total images were used for training and the remaining 20% for testing. In total 36 hand gestures were considered to capture alphabets and alphabets from A-Z and 10 for numeric, 26 for digits from 0-9 were considered to capture alphabets and Keywords. Conclusion: Along with real-time implementation, we have also shown the comparison of the performance of the various machine learning models in which we have found out the accuracy of CNN on depth- images has given the most accurate performance than other models. All these resulted were obtained on PYNQ Z2 board.

scholarly journals Advanced Statistical Analysis of 3D Kinect Data: Mimetic Muscle Rehabilitation Following Head and Neck Surgeries Causing Facial Paresis

Sensors ◽  
2020 ◽  
Vol 21 (1) ◽  
pp. 103
Author(s):  
Jan Kohout ◽  
Ludmila Verešpejová ◽  
Pavel Kříž ◽  
Lenka Červená ◽  
Karel Štícha ◽  
...  
Keyword(s):  
Statistical Analysis ◽  
Functional Data ◽  
Muscle Function ◽  
Evaluation Process ◽  
Multiple Logistic Regression ◽  
Facial Paresis ◽  
Kinect Camera ◽  
3D Camera ◽  
Mimetic Muscles ◽  
Data Analytic

An advanced statistical analysis of patients’ faces after specific surgical procedures that temporarily negatively affect the patient’s mimetic muscles is presented. For effective planning of rehabilitation, which typically lasts several months, it is crucial to correctly evaluate the improvement of the mimetic muscle function. The current way of describing the development of rehabilitation depends on the subjective opinion and expertise of the clinician and is not very precise concerning when the most common classification (House–Brackmann scale) is used. Our system is based on a stereovision Kinect camera and an advanced mathematical approach that objectively quantifies the mimetic muscle function independently of the clinician’s opinion. To effectively deal with the complexity of the 3D camera input data and uncertainty of the evaluation process, we designed a three-stage data-analytic procedure combining the calculation of indicators determined by clinicians with advanced statistical methods including functional data analysis and ordinal (multiple) logistic regression. We worked with a dataset of 93 distinct patients and 122 sets of measurements. In comparison to the classification with the House–Brackmann scale the developed system is able to automatically monitor reinnervation of mimetic muscles giving us opportunity to discriminate even small improvements during the course of rehabilitation.

Distributed Coverage Control for Mobile Camera Sensor Networks with Anisotropic Perception

2021 ◽  
pp. 1-1
Author(s):  
Qier An ◽  
Yuan Shen
Keyword(s):  
Sensor Networks ◽  
Coverage Control ◽  
Camera Sensor

Trusted Video Streams in Camera Sensor Networks

2020 ◽  
Author(s):  
Sven-Jannik Wohnert ◽  
Kai Hendrik Wohnert ◽  
Eldar Almamedov ◽  
Volker Skwarek
Keyword(s):  
Sensor Networks ◽  
Video Streams ◽  
Camera Sensor

scholarly journals Integration of opencv raspberry pi 3b+ and camera sensor in access control of vehicle ignition key system

2020 ◽  
Vol 909 ◽  
pp. 012002
Author(s):  
Lukman Medriavin Silalahi ◽  
Imelda Uli Vistalina Simanjuntak ◽  
Freddy Artadima Silaban ◽  
Setiyo Budiyanto ◽  
Heryanto ◽  
...  
Keyword(s):  
Access Control ◽  
Raspberry Pi ◽  
Camera Sensor

Minimum (k, ω)-angle barrier coverage in wireless camera sensor networks

2016 ◽  
Vol 21 (3) ◽  
pp. 179 ◽  
Author(s):  
Biaofei Xu ◽  
Yuqing Zhu ◽  
Deying Li ◽  
Donghyun Kim ◽  
Weili Wu
Keyword(s):  
Sensor Networks ◽  
Barrier Coverage ◽  
Wireless Camera Sensor Networks ◽  
Camera Sensor
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