Abstract
In recent years, safer and more reliable biometric methods have been developed. Apart from the need for enhanced security, the media and entertainment sectors have also been applying biometrics in the emerging market of user-adaptable objects/systems to make these systems more user-friendly. However, the complexity of some state-of-the-art biometric systems (e.g., iris recognition) or their high false rejection rate (e.g., fingerprint recognition) is neither compatible with the simple hardware architecture required by reduced-size devices nor the new trend of implementing smart objects within the dynamic market of the Internet of Things (IoT). It was recently shown that an individual can be recognized by extracting features from their electrocardiogram (ECG). However, most current ECG-based biometric algorithms are computationally demanding and/or rely on relatively large (several seconds) ECG samples, which are incompatible with the aforementioned application fields. Here, we present a computationally low-cost method (patent pending), including simple mathematical operations, for identifying a person using only three ECG morphology-based characteristics from a single heartbeat. The algorithm was trained/tested using ECG signals of different duration from the Physionet database on more than 60 different training/test datasets. The proposed method achieved maximal averaged accuracy of 97.450% in distinguishing each subject from a ten-subject set and false acceptance and rejection rates (FAR and FRR) of 5.710 +/- 1.900% and 3.440 +/- 1.980%, respectively, placing Beat-ID in a very competitive position in terms of the FRR/FAR among state-of-the-art methods. Furthermore, the proposed method can identify a person using an average of 1.020 heartbeats. It therefore has FRR/FAR behavior similar to obtaining a fingerprint, yet it is simpler and requires less expensive hardware. This method targets low-computational/energy-cost scenarios, such as tiny wearable devices (e.g., a smart object that automatically adapts its configuration to the user). A hardware proof-of concept implementation is presented as an annex to this paper.

Abstract
Current mobile revolution is leading to an increase of wearable health devices development and consequently a growth in ambulatory monitoring area. These systems can be applied in ambulatory diseases management and diagnosis, personal health monitoring or sports performance enhancement, providing physiological and body-area ambiance data during daily normal activities. Nowadays several devices in the market have this type of technology, being one of them the VitalJacket (R) (VJ (R)), a product from Biodevices, S.A. This device is a medical certified smart t-shirt with textile embedded electronics for ambulatory monitoring of electrocardiogram (ECG), Heart Rate (HR) and Accelerometer (Acc) data that is in the market since 2008. In this work we report the result of a university-company joint R&D effort that aimed to propose a reference extension architecture - called VitalLogger - for VJ (R) wearable health device that enables sensors seamless "plug-in" to the company product. As a proof of concept of this novel adaptable architecture, we jointly implemented a prototype that extends VJ (R) technology to incorporate new sensing modules in a seamless way. Both hardware and firmware were re-designed and extended to create a reference extension architecture, enabling easy integration of different sensing modules according to the user monitoring preference/scenario. Besides the ECG module already incorporated in VJ (R), a module with a Blood Oxygen Saturation (SpO2) sensor and an ambiance temperature and relative humidity sensor was developed and added to the extended prototype VitalLogger. This prototype was successfully built and tested, showing excellent results. The VitalLogger adaptable extension architecture has shown its scalability by enabling seamless "plug-in" sensing modules thus creating the possibility of swift creations of new products with different sensors combinations to adapt to different monitoring scenarios.

Abstract
Stress can impact multiple psychological and physiological human domains. In order to better understand the effect of stress on cognitive performance, and whether this effect is related to an autonomic response to stress, the Trier Social Stress Test (TSST) was used as a testing platform along with a 2-Choice Reaction Time Task. When considering the nature and importance of Air Traffic Controllers (ATCs) work and the fact that they are subjected to high levels of stress, this study was conducted with a sample of ATCs (n = 11). Linear Heart Rate Variability (HRV) features were extracted from ATCs electrocardiogram (ECG) acquired using a medical-grade wearable ECG device (Vital Jacket((R)) (1-Lead, Biodevices S.A, Matosinhos, Portugal)). Visual Analogue Scales (VAS) were also used to measure perceived stress. TSST produced statistically significant changes in some HRV parameters (Average of normal-to-normal intervals (AVNN), Standard Deviation of all NN (SDNN), root mean square of differences between successive rhythm-to-rhythm (RR) intervals (RMSSD), pNN20, and LF/HF) and subjective measures of stress, which recovered after the stress task. Although these short-term changes in HRV showed a tendency to normalize, an impairment on cognitive performance was evident. Despite that participant's reaction times were lower, the accuracy significantly decreased, presenting more errors after performing the acute stress event. Results can also point to the importance of the development of quantified occupational health (qOHealth) devices to allow for the monitoring of stress responses.

Abstract
Background: Air Traffic Control (ATC) is a complex and demanding process, exposing Air Traffic Controllers (ATCs) to high stress. Recently, efforts have been made in ATC to maintain safety and efficiency in the face of increasing air traffic demands. Computer simulations have been a useful tool for ATC training, improving ATCs skills and consequently traffic safety. Objectives: This study aims to: a) evaluate psychophysiological indices of stress in an ATC simulation environment using a wearable biomonitoring platform. In order to obtain a measure of ATCs stress levels, results from an experimental study with the same participants, that included a stress-induced task were used as a stress ground truth; b) understand if there are differences in stress levels of ATCs with different job functions (“advisors” vs “operationals”) when performing an ATC Refresher Training, in a simulator environment. Methods: Two studies were conducted with ATCs: Study 1, that included a stress-induced task - the Trier Social Stress Test (TSST) and Study 2, that included an ATC simulation task. Linear Heart Rate Variability (HRV) features from ATCs were acquired using a medical grade wearable Electrocardiogram (ECG) device. Self-reports were used to measure perceived stress. Results: TSST was self-reported as being much more stressful than the simulation task. Physiological data supports these results. Results from study 2 showed more stress among the “advisors” group when comparing to the “operational” group. Conclusion: Results point to the importance of the development of quantified Occupational Health (qOHealth) devices to allow monitoring and differentiation of ATCs stress responses. © 2018 Donato and Denaro.

Abstract
Wearable Health Devices (WHDs) are increasingly helping people to better monitor their health status both at an activity/fitness level for self-health tracking and at a medical level providing more data to clinicians with a potential for earlier diagnostic and guidance of treatment. The technology revolution in the miniaturization of electronic devices is enabling to design more reliable and adaptable wearables, contributing for a world-wide change in the health monitoring approach. In this paper we review important aspects in the WHDs area, listing the state-of-the-art of wearable vital signs sensing technologies plus their system architectures and specifications. A focus on vital signs acquired by WHDs is made: first a discussion about the most important vital signs for health assessment using WHDs is presented and then for each vital sign a description is made concerning its origin and effect on heath, monitoring needs, acquisition methods and WHDs and recent scientific developments on the area (electrocardiogram, heart rate, blood pressure, respiration rate, blood oxygen saturation, blood glucose, skin perspiration, capnography, body temperature, motion evaluation, cardiac implantable devices and ambient parameters). A general WHDs system architecture is presented based on the state-of-the-art. After a global review of WHDs, we zoom in into cardiovascular WHDs, analysing commercial devices and their applicability versus quality, extending this subject to smart t-shirts for medical purposes. Furthermore we present a resumed evolution of these devices based on the prototypes developed along the years. Finally we discuss likely market trends and future challenges for the emerging WHDs area.

Abstract
Firefighting is a hazardous profession commonly exposed to high stress that can interfere with firefighter's health and performance. Nevertheless, on-duty stress levels quantitative evaluations are very rare in the literature. In order to investigate firefighters' occupational health in terms of stress perceptions, symptoms, and quantified physiological reactions under real-world conditions, an ambulatory assessment protocol was developed. Therefore, cardiac signal from firefighters (N =6) was continuously monitored during two shifts within a working week with a medical clinically certified equipment (VitalJacket®), allowing continuous electrocardiogram (ECG) and actigraphy measurement. Psychological data were collected with an android application, collecting potential stressful events, stress symptoms, and stress appraisal. A total of 130 hours of medical-quality ECG were collected, from which heart rate variability (HRV) metrics were extracted and analyzed. Statistical significant differences were found in some HRV metrics - AVNN, RMSSD, pNN50 and LF/HF - between events and non-events, showing higher levels of physiological stress during events (p<0.05). Stress symptoms increase from the beginning to the end of the shift (from 1.54 ± 0.52 to 2.01 ± 0.73), however the mean stress self-perception of events was very low (3.22 ± 2.38 in a scale ranging from 0 to 10). Negative and strong correlations were also found between stress symptoms and some time-domain ECG measures (AVNN, SDNN and pNN50). It can be concluded that stress may not always be detected when using merely self-reports. These results enhance the importance of combining both self-report and ambulatory high-quality physiological stress measures in occupational health settings. Future studies should investigate not only what causes stress but also its impact on health and well-being of these professionals, in order to contribute to the design of efficient stress-management interventions. © 2018 IEEE.