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Preterm birth is a major health issue. As part of their life-saving care, most preterm infants require hospitalization and are inevitably exposed to repetitive skin-breaking procedures. The long-term effects of neonatal repetitive pain on cognitive and emotional behaviors involving hypothalamic-pituitary-adrenal (HPA) axis function in young and adult rats are unknown. From P8 to P85, mechanical hypersensitivity of the bilateral hindpaws was observed in the Needle group (P < 0.001). Compared with the Tactile group, the Needle group took longer to find the platform on P30 than on P29 (P = 0.03), with a decreased number of original platform site crossings during the probe trial of the Morris water maze test (P = 0.026). Moreover, the Needle group spent more time and took longer distances in the central area than the Tactile group in the Open-field test, both in prepubertal and adult rats (P < 0.05). The HPA axis function in the Needle group differed from the Tactile group (P < 0.05), with decreased stress responsiveness in prepuberty and puberty (P < 0.05) and increased stress responsiveness in adulthood (P < 0.05). This study indicates that repetitive pain that occurs during a critical period may cause severe consequences, with behavioral and neuroendocrine disturbances developing through prepuberty to adult life.

This paper presents a novel approach for performing intuitive gesture based interaction using depth data acquired by Kinect. The main challenge to enable immersive gestural interaction is dynamic gesture recognition. This problem can be formulated as a combination of two tasks; gesture recognition and gesture pose estimation. Incorporation of fast and robust pose estimation method would lessen the burden to a great extent. In this paper we propose a direct method for real-time hand pose estimation. Based on the range images, a new version of optical flow constraint equation is derived, which can be utilized to directly estimate 3D hand motion without any need of imposing other constraints. Extensive experiments illustrate that the proposed approach performs properly in real-time with high accuracy. As a proof of concept, we demonstrate the system performance in 3D object manipulation On two different setups; desktop computing, and mobile platform. This reveals the system capability to accommodate different interaction procedures. In addition, a user study is conducted to evaluate learnability, user experience and interaction quality in 3D gestural interaction in comparison to 2D touchscreen interaction.

Facial expressions are considered as a reliable indicator in neonatal pain assessment. This paper proposes a novel recognition method for neonatal pain expression. The method can utilize the feature descriptors based on the weighted local binary pattern (LBP) and the classifier based on sparse representation. Firstly, the normalized facial image is described using a feature vector, which is histogram sequence obtained by concatenating the weighted histograms of the LBP feature maps of all the local blocks. Then, the principalc component analysis (PCA) method is used to reduce the dimensions of the feature vector of training and test samples. Finally, the over-complete dictionary is built and the classifier based on sparse representation is used to classify test samples into four classes of facial expressions: calm, crying, mild pain, and severe pain. The objective of this study is to assist the clinicians in assessing neonatal pain by utilizing computer-based image analysis techniques. Experimental results on neonate facial image database show the effectiveness of the proposed method. The classification accuracy rate reaches 84.50%.

3D gesture analysis is a highly desired feature of future interaction design. Specifically, in augmented environments, intuitive interaction with the physical space seems unavoidable and 3D gestural interaction might be the most effective alternative for the current input facilities. This paper, introduces a novel solution for real-time 3D gesture analysis using an extremely large gesture database. This database includes the images of various articulated hand gestures with the annotated 3D position/orientation parameters of the hand joints. Our unique search algorithm is based on the hierarchical scoring of the low-level edge-orientation features between the query input and database and retrieving the best match. Once the best match is found from the database in real-time, the pre-calculated 3D parameters can instantly be used for gesture-based interaction.

This paper presents a novel approach for performing intuitive 3D gesture-based interaction using depth data acquired by Kinect. Unlike current depth-based systems that focus only on classical gesture recognition problem, we also consider 3D gesture pose estimation for creating immersive gestural interaction. In this paper, we formulate gesture-based interaction system as a combination of two separate problems, gesture recognition and gesture pose estimation. We focus on the second problem and propose a direct method for recovering hand motion parameters. Based on the range images, a new version of optical flow constraint equation is derived, which can be utilized to directly estimate 3D hand motion without any need of imposing other constraints. Our experiments illustrate that the proposed approach performs properly in real-time with high accuracy. As a proof of concept, we demonstrate the system performance in 3D object manipulation. This application is intended to explore the system capabilities in real-time biomedical applications. Eventually, system usability test is conducted to evaluate the learnability, user experience and interaction quality in 3D interaction in comparison to 2D touch-screen interaction.

Currently, the most common way to control an electric wheelchair is to use joystick. However, there are some individuals unable to operate joystick-driven electric wheelchairs due to sever physical disabilities, like quadriplegia patients. This paper proposes a novel head pose estimation method to assist such patients. Head motion parameters are employed to control and drive an electric wheelchair. We introduce a direct method for estimating user head motion, based on a sequence of range images captured by Kinect. In this work, we derive new version of the optical flow constraint equation for range images. We show how the new equation can be used to estimate head motion directly. Experimental results reveal that the proposed system works with high accuracy in real-time. We also show simulation results for navigating the electric wheelchair by recovering user head motion.

In recent years, emergence of telerehabilitation systems for home-based therapy has altered healthcare systems. Telerehabilitation enables therapists to observe patients status via Internet, thus a patient does not have to visit rehabilitation facilities for every rehabilitation session. Despite the fact that telerehabilitation provides great opportunities, there are two major issues that affect effectiveness of telerehabilitation: relegation of the patient at home, and loss of direct supervision of the therapist. Since patients have no actual interaction with other persons during the rehabilitation period, they will become isolated and gradually lose their social skills. Moreover, without direct supervision of therapists, rehabilitation exercises can be performed with bad compensation strategies that lead to a poor quality recovery. To resolve these issues, we propose telelife, a new concept for future rehabilitation systems. The idea is to use media technology to create a totally new immersive media experience for rehabilitation. In telerehabilitation patients locally execute exercises, and therapists remotely monitor patients' status. In telelife patients, however, remotely perform exercises and therapists locally monitor. Thus, not only telelife enables rehabilitation at distance, but also improves the patients' social competences, and provides direct supervision of therapists. In this paper we introduce telelife to enhance telerehabilitation, and investigate technical challenges and possible methods to achieve telelife.

Audio and video are two powerful media forms to shorten the distance between audience/viewer and actors or players in the TV and films. The recent research shows that today people are using more and more multimedia contents on mobile devices, such as tablets and smartphones. Therefore, an important question emerges - how can we render high-quality, personal immersive experiences to consumers on these systems? To give audience an immersive engagement that differs from `watching a play', we have designed a study to render complete immersive media which include the `emotional information' based on augmented vibrotactile-coding on the back of the user along with audio-video signal. The reported emotional responses to videos viewed with and without haptic enhancement, show that participants exhibited an increased emotional response to media with haptic enhancement. Overall, these studies suggest that the effectiveness of our approach and using a multisensory approach increase immersion and user satisfaction.

Movement disorders prevent many people fromenjoying their daily lives. As with other diseases, diagnosisand analysis are key issues in treating such disorders.Computer vision-based motion capture systems are helpfultools for accomplishing this task. However Classical motiontracking systems suffer from several limitations. First theyare not cost effective. Second these systems cannot detectminute motions accurately. Finally they are spatially limitedto the lab environment where the system is installed. In thisproject, we propose an innovative solution to solve the abovementionedissues. Mounting the camera on human body, webuild a convenient, low cost motion capture system that canbe used by the patient in daily-life activities. We refer tothis system as active motion capture, which is not confinedto the lab environment. Real-time experiments in our labrevealed the robustness and accuracy of the system.

Movement disorders forbid many people from enjoying their daily lives. As with other diseases, diagnosis and analysis are key issues in treating such disorders. Computer vision-based motion capture systems are helpful tools for accomplishing this task. However Classical motion tracking systems suffer from several limitations. First they are not cost effective. Second these systems cannot detect minute motions accurately. Finally they are spatially limited to the lab environment where the system is installed. In this project, we propose an innovative solution to solve the above-mentioned issues. Mounting the camera on human body, we build a convenient, low cost motion capture system that can be used by the patient while practicing daily-life activities. We refer to this system as active motion capture, which is not confined to the lab environment. Real-time experiments in our lab revealed the robustness and accuracy of the system.