Abstract
First responders such as firefighters are exposed to extreme stress and fatigue situations during their work routines. It is thus desirable to monitor their health using wearable sensing but this is a complex and still unsolved research challenge that requires large amounts of properly annotated physiological signals data. In this paper we show that the information gathered by our Vital Analysis Framework can support the annotation of these vital signals with the stress levels perceived by the target user, confirmed by the analysis of more than 4600 hours of data collected from real firefighters in action, including 717 answers to event questionnaires from a total of 454 different events.

Abstract
First responders are professionals that are exposed to extreme stress and fatigue during extended periods of time. That is why it is necessary to research and develop technological solutions based on wearable sensors that can continuously monitor the health of these professionals in action, namely their stress and fatigue levels. In this paper we present the Vital Analysis smartphone-based framework, integrated into the broader Vital Responder project, that allows the annotation and contextualization of the signals collected during real action. After a contextual study we have implemented and deployed this framework in a firefighter team with 5 elements, from where we have collected over 3300 hours of annotations during 174 days, covering 382 different events. Results are analysed and discussed, validating the framework as a useful and usable tool for annotating biological signals of first responders in action.

Abstract
We present an approach for customized heartbeat classification of electrocardiogram (ECG) signals, based on the construction of one general multi-class classifier and one specific two-class classifier. The general classifier is trained on a global training dataset, containing examples of all possible classes and patterns. On the other hand, the individual-specific classifier is built using a small amount of individual data, which is a binary one-against-the-rest classifier, providing discrimination between normal and abnormal patterns from that individual. Such an individual-specific classifier can be a two-class classifier or a one-class classifier, depending on the availability of abnormal patterns in the individual training dataset. The classifications from the two classifiers are fused to obtain a final decision. The proposed approach is applied to the study of ECG heartbeat classification problem, significantly outperforming state-of-the-art methods. The proposed method can also be useful in anomaly detection of other biomedical signals.

Abstract
Wearable health monitoring devices have been widely explored to enable continuous monitoring of physiological vital signals, such as electrocardiogram (ECG). In this work, we investigate the applicability of ECG signals from such wearable devices in human identification. In the 5-subject study we undertook, the proposed method exhibits near-100% recognition rates based on single heartbeats, even with a six-month interval between the training and testing data. This indicates that ECG signals can be used as robust biometrics and as an automatic login solution for such wearable health monitoring devices. © 2011 ACM.

Abstract
We propose a denoising and segmentation technique for the second heart sound (S2). To denoise, Matching Pursuit (MP) was applied using a set of non-linear chirp signals as atoms. We show that the proposed method can be used to segment the phonocardiogram of the second heart sound into its two clinically meaningful components: the aortic (A2) and pulmonary (P2) components.

Abstract
Video encoding standards, namely MPEG-2, store large amounts of information obtained for compression purposes that can be accessed with minimal decoding. This paper shows that, with proper filtering of motion vectors and DCT coefficients, accurate segmentation results can be achieved by combining both reliable motion estimation and background subtraction. We further present a fine segmentation step that exploits specific blob characteristics to reduce segmentation noise and solve some occlusion problems. Examples using real videos from underground station CCTV cameras show that compressed domain information can be the key for successful surveillance applications where very fast algorithms with high accuracy are required.