|Commenced in January 2007||Frequency: Monthly||Edition: International||Paper Count: 12|
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.
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.
In the field of reverse engineering and creative industries, applying 3D scanning process to obtain geometric forms of the objects is a mature and common technique. For instance, organic objects such as faces and nonorganic objects such as products could be scanned to acquire the geometric information for further application. However, although the data resolution of 3D scanning device is increasing and there are more and more abundant complementary applications, the penetration rate of 3D scanning for the public is still limited by the relative high price of the devices. On the other hand, Kinect, released by Microsoft, is known for its powerful functions, considerably low price, and complete technology and database support. Therefore, related studies can be done with the applying of Kinect under acceptable cost and data precision. Due to the fact that Kinect utilizes optical mechanism to extracting depth information, limitations are found due to the reason of the straight path of the light. Thus, various angles are required sequentially to obtain the complete 3D information of the object when applying a single Kinect for 3D scanning. The integration process which combines the 3D data from different angles by certain algorithms is also required. This sequential scanning process costs much time and the complex integration process often encounter some technical problems. Therefore, this paper aimed to apply multiple Kinects simultaneously on the field of developing a rapid 3D mannequin scan platform and proposed suggestions on the number and angles of Kinects. In the content, a method of establishing the coordination based on the relation between mannequin and the specifications of Kinect is proposed, and a suggestion of angles and number of Kinects is also described. An experiment of applying multiple Kinect on the scanning of 3D mannequin is constructed by Microsoft API, and the results show that the time required for scanning and technical threshold can be reduced in the industries of fashion and garment design.
The paper describes a Chinese shadow play animation system based on Kinect. Users, without any professional training, can personally manipulate the shadow characters to finish a shadow play performance by their body actions and get a shadow play video through giving the record command to our system if they want. In our system, Kinect is responsible for capturing human movement and voice commands data. Gesture recognition module is used to control the change of the shadow play scenes. After packaging the data from Kinect and the recognition result from gesture recognition module, VRPN transmits them to the server-side. At last, the server-side uses the information to control the motion of shadow characters and video recording. This system not only achieves human-computer interaction, but also realizes the interaction between people. It brings an entertaining experience to users and easy to operate for all ages. Even more important is that the application background of Chinese shadow play embodies the protection of the art of shadow play animation.
This paper proposes a three-dimensional motion capture and feedback system of flying disc throwing action learners with use of Kinect device. Rather than conventional 3-D motion capture system, Kinect has advantages of cost merit, easy system development and operation. A novice learner of flying disc is trained to keep arm movement in steady height, to twist the waist, and to stretch the elbow according to the waist angle. The proposing system captures learners- body movement, checks their skeleton positions in pre-motion / motion / post-motion in several ways, and displays feedback messages to refine their actions.
It is important to give input information without other device in AR system. One solution is using hand for augmented reality application. Many researchers have proposed different solutions for hand interface in augmented reality. Analyze Histogram and connecting factor is can be example for that. Various Direction searching is one of robust way to recognition hand but it takes too much calculating time. And background should be distinguished with skin color. This paper proposes a hand tracking method to control the 3D object in augmented reality using depth device and skin color. Also in this work discussed relationship between several markers, which is based on relationship between camera and marker. One marker used for displaying virtual object and three markers for detecting hand gesture and manipulating the virtual object.