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Improving inventory management is essential to retailer profitability. This paper proposes a supervised learning approach for Out-of-Stock (OOS) detection by Texture, Color and Geometry features in high-resolution panoramic images of grocery retail shelves. Cascade classifiers are used to detect labels that can potentially be used to confirm the presence of the OOS cases. The image acquisition setup includes a camera cart that shoots from multi-viewpoints aiming a parallel motion to the shelf. The correction of perspective distortion is applied to handle the different camera translation motions while stitching together images with a high-level of similarity. From the generated panoramas, the proposed OOS detection is followed by classification with Support Vector Machines. The experimental tests were performed throughout the retail environment with real data obtained from supermarket shelves containing labels near the visible ruptures. Results show a detection accuracy of 84.5% for OOS and a sensitivity of 86.6% for label detection

The study of stress and fatigue among First Responders is a major step in mitigating this public health problem. Blood pressure, heart rate variability and fatigue related arrhythmia are three of the main "windows" to study stress and fatigue. In this paper we present a wearable medical device, capable of acquiring an electrocardiogram and estimating blood pressure in real time, through a pulse wave transit time approach. The system is based on an existent certified wearable medical device called "Vital Jacket" and is aimed to become a tool to allow cardiologists in studying stress and fatigue among first response professionals.

Falls are not an inevitable consequence of ageing. Several fall risk factors can be identified and effective fall prevention techniques applied, which offer an opportunity to reduce falls among older persons. In this paper, the smartphone is proposed as an alternative to traditional methods in the assessment of fall risk factors, including decline in balance, reduced lower limb strength and fear of falling. As such, clinical fall risk assessment tests were adapted to the smartphone in order to measure One Leg Standing, Sit to Stand and Falls Efficacy Scale. Experimental results of the system support the feasibility of a reliable phone-based fall predictor, which constitutes an alternative to evaluate fall risk factors in ageing.