Hand Pointing Detection Using Live Histogram Template of Forehead Skin

Hand pointing detection has multiple applications in many fields such as virtual reality and control devices in smart homes. In this paper, we proposed a novel approach to detect pointing vector in 2D space of a room. After background subtraction, face and forehead is detected. In the second step, forehead skin H-S plane histograms in HSV space is calculated. By using these histogram templates of users skin, and back projection method, skin areas are detected. The contours of hand are extracted using Freeman chain code algorithm. Next step is finding fingertips. Points in hand contour which are candidates for the fingertip can be found in convex defects of convex hull and contour. We introduced a novel method for finding the fingertip based on the special points on the contour and their relationships. Our approach detects hand-pointing vectors in live video from a common webcam with 94%TP and 85%TN.Comment: Accepted for oral presentation in DSP201

Validating an infrared thermal switch as a novel access technology

<p>Abstract</p> <p>Background</p> <p>Recently, a novel single-switch access technology based on infrared thermography was proposed. The technology exploits the temperature differences between the inside and surrounding areas of the mouth as a switch trigger, thereby allowing voluntary switch activation upon mouth opening. However, for this technology to be clinically viable, it must be validated against a gold standard switch, such as a chin switch, that taps into the same voluntary motion.</p> <p>Methods</p> <p>In this study, we report an experiment designed to gauge the concurrent validity of the infrared thermal switch. Ten able-bodied adults participated in a series of 3 test sessions where they simultaneously used both an infrared thermal and conventional chin switch to perform multiple trials of a number identification task with visual, auditory and audiovisual stimuli. Participants also provided qualitative feedback about switch use. User performance with the two switches was quantified using an efficiency measure based on mutual information.</p> <p>Results</p> <p>User performance (p = 0.16) and response time (p = 0.25) with the infrared thermal switch were comparable to those of the gold standard. Users reported preference for the infrared thermal switch given its non-contact nature and robustness to changes in user posture.</p> <p>Conclusions</p> <p>Thermal infrared access technology appears to be a valid single switch alternative for individuals with disabilities who retain voluntary mouth opening and closing.</p

A Full-Body Layered Deformable Model for Automatic Model-Based Gait Recognition

Abstract This paper proposes a full-body layered deformable model (LDM) inspired by manually labeled silhouettes for automatic model-based gait recognition from part-level gait dynamics in monocular video sequences. The LDM is defined for the fronto-parallel gait with 22 parameters describing the human body part shapes (widths and lengths) and dynamics (positions and orientations). There are four layers in the LDM and the limbs are deformable. Algorithms for LDM-based human body pose recovery are then developed to estimate the LDM parameters from both manually labeled and automatically extracted silhouettes, where the automatic silhouette extraction is through a coarse-to-fine localization and extraction procedure. The estimated LDM parameters are used for model-based gait recognition by employing the dynamic time warping for matching and adopting the combination scheme in AdaBoost.M2. While the existing model-based gait recognition approaches focus primarily on the lower limbs, the estimated LDM parameters enable us to study full-body model-based gait recognition by utilizing the dynamics of the upper limbs, the shoulders and the head as well. In the experiments, the LDM-based gait recognition is tested on gait sequences with differences in shoe-type, surface, carrying condition and time. The results demonstrate that the recognition performance benefits from not only the lower limb dynamics, but also the dynamics of the upper limbs, the shoulders and the head. In addition, the LDM can serve as an analysis tool for studying factors affecting the gait under various conditions

Kernel-Based Positioning in Wireless Local Area Networks

The recent proliferation of Location-Based Services (LBSs) has necessitated the development of effective indoor positioning solutions. In such a context, Wireless Local Area Network (WLAN) positioning is a particularly viable solution in terms of hardware and installation costs due to the ubiquity of WLAN infrastructures. This paper examines three aspects of the problem of indoor WLAN positioning using received signal strength (RSS). First, we show that, due to the variability of RSS features over space, a spatially localized positioning method leads to improved positioning results. Second, we explore the problem of access point (AP) selection for positioning and demonstrate the need for further research in this area. Third, we present a kernelized distance calculation algorithm for comparing RSS observations to RSS training records. Experimental results indicate that the proposed system leads to a 17 percent (0.56 m) improvement over the widely used K-nearest neighbor and histogram-based methods