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- The i-DREAMS intervention strategies to reduce driver fatigue and sleepiness for different transport modesPublication . Pilkington-Cheney, Fran; Afghari, Amir Pooyan; Filtness, Ashleigh; Papadimitriou, Eleonora; Lourenço, André; Brijs, TomDriver sleepiness and fatigue are important contributors to many transport incidents and significantly increase crash risk. Recently, detection systems have been developed which aim to monitor the state of the driver and detect increasing levels of fatigue. However, there has been less focus on appropriate intervention strategies for drivers once fatigue and sleepiness have been detected. This paper describes the i-DREAMS fatigue intervention strategies, which aim to keep drivers within a safe driving zone. Interventions will be provided both in real-time and post-trip and can be customized to be used with a variety of transport modes. Real-time interventions will measure fatigue through trip duration, and driver sleepiness through heart-rate variability (HRV) information, obtained by means of sensors in the steering wheel or a wearable device, and attributed to Karolinska Sleepiness Score (KSS) bands. Thresholds for warnings will map onto phases of a 'Safety Tolerance Zone' and will be dynamic changing as the driver state and driving situation develops. Post-trip interventions will aggregate data throughout the duration of the drive and aim to provide customized feedback and coping tips related to driver levels of fatigue and sleepiness, to improve driving behavior. Goals and challenges will add a gamified aspect to the post-trip interventions. The next stage of the development of the i-DREAMS fatigue intervention strategy is to test the concept in a series of simulator and field trials. Future research should explore acceptance and compliance of interventions and frequency of alerts.
- Driving simulator for performance monitoring with physiological sensorsPublication . Raimundo, Diogo; Lourenço, André; Abrantes, ArnaldoThis paper describes a driving monitoring system based on a car driving simulator, specifically built for this project, using inexpensive off-the-shelf game technology (software and hardware). The project aims to study the effect of drowsiness (caused by fatigue, alcohol consumption, etc.) in driving performance and how it can be detected in advance in order to mitigate accidents. To achieve this, the system is continuously monitoring driver's physiological signals (e.g., heart rate) as well as her/his driving behavior. Electrocardiography (ECG) signal plays a central role in this project, since a feature derived from it - the HRV (Heart Rate Variability), more concretely its power spectral distribution - is used to continuously estimate the driver's degree of awareness and therefore for generation of alarms. A preliminary evaluation of the proposed system is discussed through the presentation of some experimental results.
- Lockdown measures for COVID-19 outbreak and variation in physical activity in patients with heart failure and cardiac implantable devicesPublication . Silva Cunha, Pedro; Laranjo, Sérgio; Lourenço, André; Rodrigues, Lourenço; Cardoso, Isabel; Portugal, Guilherme; Valente, Bruno; Delgado, Ana Sofia; Ferreira, Rui Cruz; Abreu, Ana; Oliveira, MarioAims: The present study analysed the patterns of physical activity pre-, during and post-lockdown measures for COVID-19 pandemic in patients with chronic heart failure (CHF) and cardiac implantable electronic devices (CIED) under remote monitoring (RM), and assessed the physical activity patterns during these periods. Methods: The raw data from 95 patients with CHF (age 67,7 +/- 15,1 years, 71,5% male) corresponding to 2238 RM transmissions of the Medtronic Carelink (TM) network platform was obtained. The physical exercise profiles and the impact of the lockdown measures on the physical behaviour during and after the measures were analysed. According to the level of activity duration in the pre-lockdown, lockdown and post-lockdown periods, the patterns of behaviour were identified (non-recoverees, incomplete recoverees, recoverees and full-recoverees). Conclusion: RM of CHF patients with CIED using the Carelink (TM) network is useful for close follow-up and identification of heart failure risk status variations. After relieving the confinement measures there were two groups of patients that did not recover the previous physical activity levels. Physical inactivity in patients with CHF can have a significant impact on outcomes.
- ECG simulator with configurable skin-electrode impedance and artifacts emulationPublication . Almeida, Daniel; Costa, João; Lourenço, AndréElectrocardiograms (ECG) recorded from everyday objects, such as wearables, fitness machines or smart steering wheels are becoming increasingly common. Applications are diverse and include health monitoring, athletic performance optimization, identification, authentication, and entertainment. In this study we report the design and implementation of an innovative ECG simulator, providing simulation of signal related artifacts and a dynamically adjustable skin-electrode interface model. The ECG simulator includes a unique combination of features: emulation of time dependent skinelectrode impedance, adjustable differential and common-mode interference, generation of lead-off events and analog front-end output digitalization. The skin-electrode capacitance range is 1 nF-255 nF and the resistance span is 4 kΩ-996 kΩ. System’s functionality is demonstrated using a commercially available ECG front-end. The simulated SNR degradation introduced by the ECG simulator is under 0.1 dB. Results show that the skin-electrode interface can have a significant impact in the acquired waveforms. Impedance electrode imbalance, specifically of the resistive component, can generate artifacts which can be misinterpreted has arrhythmias. The proposed device can be useful for hardware and software ECG development and for training physicians and nurses to readily recognize skin-electrode impedance related artifacts.
- ECG signals for biometric applications: are we there yet?Publication . Carreiras, Carlos; Lourenço, André; Fred, Ana Luísa Nobre; Ferreira, RuiThe potential of the electrocardiographic (ECG) signal as a biometric trait has been ascertained in the literature over the past decade. The inherent characteristics of the ECG make it an interesting biometric modality, given its universality, intrinsic aliveness detection, continuous availability, and inbuilt hidden nature. These properties enable the development of novel applications, where non-intrusive and continuous authentication are critical factors. Examples include, among others, electronic trading platforms, the gaming industry, and the auto industry, in particular for car sharing programs and fleet management solutions. However, there are still some challenges to overcome in order to make the ECG a widely accepted biometric. In particular, the questions of uniqueness (inter-subject variability) and permanence over time (intra-subject variability) are still largely unanswered. In this paper we focus on the uniqueness question, presenting a preliminary study of our biometric recognition system, testing it on a database encompassing 618 subjects. We also performed tests with subsets of this population. The results reinforce that the ECG is a viable trait for biometrics, having obtained an Equal Error Rate of 9.01% and an Error of Identification of 15.64% for the entire test population.
- Multi-factor authentication for improved efficiency in ECG - based loginPublication . Neves, Pedro; Nunes, Luís; Lourenço, AndréElectrocardiogram (ECG) based biometrics have proven to be a reliable source of identification. ECG can now be measured off-the-person, requiring nothing more than dry electrodes or conductive fabrics to acquire a usable ECG signal. However, identification still has a relatively poor performance when using large user databases. In this paper we suggest using ECG authentication associated with a smartphone security token in order to improve performance and decrease the time required for the recognition. This paper reports the implementation of this technique in a user authentication scenario for a Windows login using normal Bluetooth (BT) and Bluetooth Low Energy (BLE). This paper also uses Intel Edison's mobility features to create a more versatile environment. Results proved our solution to be feasible and present improvements in authentication times when compared to a simple ECG identification.
- Towards the detection of deception in interactive multimedia environmentsPublication . Silva, Hugo Plácido da; Alves, Ana Priscila; Lourenço, André; Fred, Ana; Montalvão, Inês; Alegre, LeonelA classical application of biosignal analysis has been the psychophysiological detection of deception, also known as the polygraph test, which is currently a part of standard practices of law enforcement agencies and several other institutions worldwide. Although its validity is far from gathering consensus, the underlying psychophysiological principles are still an interesting add-on for more informal applications. In this paper we present an experimental off-the-person hardware setup, propose a set of feature extraction criteria and provide a comparison of two classification approaches, targeting the detection of deception in the context of a role-playing interactive multimedia environment. Our work is primarily targeted at recreational use in the context of a science exhibition, where the main goal is to present basic concepts related with knowledge discovery, biosignal analysis and psychophysiology in an educational way, using techniques that are simple enough to be understood by children of different ages. Nonetheless, this setting will also allow us to build a significant data corpus, annotated with ground-truth information, and collected with non-intrusive sensors, enabling more advanced research on the topic. Experimental results have shown interesting findings and provided useful guidelines for future work. Pattern Recognition
- Evolution, current challenges, and future possibilities in ECG biometricsPublication . Ribeiro Pinto, João; Cardoso, Jaime; Lourenço, AndréFace and fingerprint are, currently, the most thoroughly explored biometric traits, promising reliable recognition in diverse applications. Commercial products using these traits for biometric identification or authentication are increasingly widespread, from smartphones to border control. However, increasingly smart techniques to counterfeit such traits raise the need for traits that are less vulnerable to stealthy trait measurement or spoofing attacks. This has sparked interest on the electrocardiogram (ECG), most commonly associated with medical diagnosis, whose hidden nature and inherent liveness information make it highly resistant to attacks. In the last years, the topic of ECG-based biometrics has quickly evolved toward the commercial applications, mainly by addressing the reduced acceptability and comfort by proposing new off-the-person, wearable, and seamless acquisition settings. Furthermore, researchers have recently started to address the issues of spoofing prevention and data security in ECG biometrics, as well as the potential of deep learning methodologies to enhance the recognition accuracy and robustness. In this paper, we conduct a deep reviewand discussion of 93 state-of-the-art publications on their proposed methods, signal datasets, and publicly available ECG collections. The extracted knowledge is used to present the fundamentals and the evolution of ECG biometrics, describe the current state of the art, and draw conclusions on prior art approaches and current challenges.With this paper, we aim to delve into the current opportunities as well as inspire and guide future research in ECG biometrics.
- Driver drowsiness detection: a comparison between intrusive and non-intrusive signal acquisition methodsPublication . Oliveira, Licínio; Cardoso, Jaime; Lourenço, André; Ahlstrom, ChristerDriver drowsiness is a major cause of road accidents, many of which result in fatalities. A solution to this problem is the inclusion of a drowsiness detector in vehicles to alert the driver if sleepiness is detected. To detect drowsiness, physiologic, behavioral (visual) and vehicle-based methods can be used, however, only measures that can be acquired non-intrusively are viable in a real life application. This work uses data from a real-road experiment with sleep deprived drivers to compare the performance of driver drowsiness detection using intrusive acquisition methods, namely electrooculogram (EOG), with camera based, non-intrusive, methods. A hybrid strategy, combining the described methods with electrocardiogram (ECG) measures, is also evaluated. Overall, the obtained results show that drowsiness detection performance is similar using non-intrusive camera based measures or intrusive EOG measures. The detection performance increases when combining two methods (ECG + visual) or (ECG + EOG).
- A study on compression techniques for off-the-person electrocardiogram signalsPublication . Cerca, António; Lourenço, André; J. Ferreira, ArturThe compression of Electrocardiography (ECG) signals acquired in off-the-person scenarios requires methods that cope with noise and other impairments on the acquisition process. In this paper, after a brief review of common on-the-person ECG signal compression algorithms, we propose and evaluate techniques for this compression task with off-the-person acquired signals, in both lossy and lossless scenarios, evaluated with standard metrics. Our experimental results show that the joint use of Linear Predictive Coding and Lempel-Ziv-Welch is an adequate lossless approach, and the amplitude scaling followed by the Discrete Wavelet Transform achieves the best compression ratio, with a small distortion, among the lossy techniques.