Abstract: In designing a kinetic façade, it is hard for the designer to make digital models due to its complex geometry with motion. This paper aims to present a methodology of converting a point cloud of a physical model into a single digital model with a certain topology and motion. The method uses a Microsoft Kinect sensor, and color markers were defined and applied to three paper folding-inspired designs. Although the resulted digital model cannot represent the whole folding range of the physical model, the method supports the designer to conduct a performance-oriented design process with the rough physical model in the reduced folding range.
Abstract: Proprioceptive neuromuscular facilitation exercises are a series of stretching techniques that are commonly used in rehabilitation and exercise therapy. Assessment of these exercises for true maneuvering requires extensive experience in this field and could not be down with patients themselves. In this paper, we developed software that uses Microsoft Kinect sensor, a spherical color marker, and real-time image processing methods to evaluate patient’s performance in generating true patterns of movements. The software also provides the patient with a visual feedback by showing his/her avatar in a Virtual Reality environment along with the correct path of moving hand, wrist and marker. Primary results during PNF exercise therapy of a patient in a room environment shows the ability of the system to identify any deviation of maneuvering path and direction of the hand from the one that has been performed by an expert physician.
Abstract: Autism spectrum disorder is a complex developmental disability. It is defined by a certain set of behaviors. Persons with Autism Spectrum Disorders (ASD) frequently engage in stereotyped and repetitive motor movements. The objective of this article is to propose a method to automatically detect this unusual behavior. Our study provides a clinical tool which facilitates for doctors the diagnosis of ASD. We focus on automatic identification of five repetitive gestures among autistic children in real time: body rocking, hand flapping, fingers flapping, hand on the face and hands behind back. In this paper, we present a gesture recognition system for children with autism, which consists of three modules: model-based movement tracking, feature extraction, and gesture recognition using artificial neural network (ANN). The first one uses the Microsoft Kinect sensor, the second one chooses points of interest from the 3D skeleton to characterize the gestures, and the last one proposes a neural connectionist model to perform the supervised classification of data. The experimental results show that our system can achieve above 93.3% recognition rate.
Abstract: Eyes are an essential and conspicuous organ of the human body. Human eyes are outward and inward portals of the body that allows to see the outside world and provides glimpses into ones inner thoughts and feelings. Inevitable blindness and visual impairments may results from eye-related disease, trauma, or congenital or degenerative conditions that cannot be corrected by conventional means. The study emphasizes innovative tools that will serve as an aid to the blind and visually impaired (VI) individuals. The researchers fabricated a prototype that utilizes the Microsoft Kinect for Windows and Arduino microcontroller board. The prototype facilitates advanced gesture recognition, voice recognition, obstacle detection and indoor environment navigation. Open Computer Vision (OpenCV) performs image analysis, and gesture tracking to transform Kinect data to the desired output. A computer vision technology device provides greater accessibility for those with vision impairments.
Abstract: This paper describes the process used in the
automation of the Maritime UAV commands using the Kinect sensor.
The AR Drone is a Quadrocopter manufactured by Parrot [1] to be
controlled using the Apple operating systems such as iPhones and
Ipads. However, this project uses the Microsoft Kinect SDK and
Microsoft Visual Studio C# (C sharp) software, which are compatible
with Windows Operating System for the automation of the navigation
and control of the AR drone.
The navigation and control software for the Quadrocopter runs on
a windows 7 computer. The project is divided into two sections; the
Quadrocopter control system and the Kinect sensor control system.
The Kinect sensor is connected to the computer using a USB cable
from which commands can be sent to and from the Kinect sensors.
The AR drone has Wi-Fi capabilities from which it can be connected
to the computer to enable transfer of commands to and from the
Quadrocopter.
The project was implemented in C#, a programming language that
is commonly used in the automation systems. The language was
chosen because there are more libraries already established in C# for
both the AR drone and the Kinect sensor.
The study will contribute toward research in automation of
systems using the Quadrocopter and the Kinect sensor for navigation
involving a human operator in the loop. The prototype created has
numerous applications among which include the inspection of vessels
such as ship, airplanes and areas that are not accessible by human
operators.