HUMAN GENDER CLASSIFICATION USING KINECT SENSOR: A REVIEW

Human Gender Classification using Kinect sensor aims to classifying people’s gender based on their outward appearance. Application areas of Kinect sensor technology includes security, marketing, healthcare, and gaming. However, because of the changes in pose, attire, and illumination, gender determination with the Kinect sensor is not a trivial task. It is based on a variety of characteristics, including biological, social network, face, and body aspects. In recent years, gender classification that utilizes the Kinect sensor became a popular and essential way for accurate gender classification. A variety of methods and approaches, like machine learning, convolutional neural networks, sport vector machine (SVM), etc., have been used for gender classification using a Kinect sensor. This paper presents the state of the art for gender classification, with a focus on the features, databases, procedures, and algorithms used in it. A review of recent studies on this subject using the Kinect sensor and other technologies is provided, together with information on the variables that affect the classification\u27s accuracy. In addition, several publicly accessible databases or datasets are used by researchers to classify people by gender are covered. Finlay, this overview offers insightful information about the potential future avenues for research on Kinect-based human gender classification

Tractor cabin ergonomics analyses by means of Kinect motion capture technology

Kinect is the de facto standard for real-time depth sensing and motion capture cameras. The sensor is here proposed for exploiting body tracking during driving operations. The motion capture system was developed taking advantage of the Microsoft software development kit (SDK), and implemented for real-time monitoring of body movements of a beginner and an expert tractor drivers, on different tracks (straight and with curves) and with different driving conditions (manual and assisted steering). Tests show how analyses can be done not only in terms of absolute movements, but also in terms of relative shifts, allowing for quantification of angular displacements or rotations

Recognizing complex gestures via natural interfaces

Natural interfaces have revolutionized the way we interact with computers. They have provided in many fields a comfortable and efficient mechanism that requires no computer knowledge, nor artificial controlling devices, but allow as to interoperate via natural gestures. Diverse fields such as entertainment, remote control, medicine, fitness exercise are finding improvements with the introduction of this technology. However, most of these sensorial interfaces only provide support for basic gestures. In this work we show how it is possible to construct your own complex gestures using the underlying capabilities of these sensor devices.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tec

Assessment of joint parameters in a Kinect sensor based rehabilitation game

Copyright © 2019 ASME. A Kinect sensor based basketball game is developed for delivering post-stroke exercises in association with a newly developed elbow exoskeleton. Few interesting features such as audio-visual feedback and scoring have been added to the game platform to enhance patient’s engagement during exercises. After playing the game, the performance score has been calculated based on their reachable points and reaching time to measure their current health conditions. During exercises, joint parameters are measured using the motion capture technique of Kinect sensor. The measurement accuracy of Kinect sensor is validated by two comparative studies where two healthy subjects were asked to move elbow joint in front of Kinect sensor wearing the developed elbow exoskeleton. In the first study, the joint information collected from Kinect sensor was compared with the exoskeleton based sensor. In the next study, the length of upperarm and forearm measured by Kinect were compared with the standard anthropometric data. The measurement errors between Kinect and exoskeleton are turned out to be in the acceptable range; 1% for subject 1 and 0.44% for subject 2 in case of joint angle; 5.55% and 3.58% for subject 1 and subject 2 respectively in case of joint torque. The average errors of Kinect measurement as compared to the anthropometric data of the two subjects are 16.52% for upperarm length and 9.87% for forearm length. It shows that Kinect sensor can measure the activity of joint movement with a minimum margin of error