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New University of Lisbon, Portugal

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36 Papers

Eye-pointer Coordination in a Decision-making Task Under Uncertainty

Topics: Biometrics; Physiological Processes and Biosignal Modeling, Non-Linear Dynamics

Authors: Catia Cepeda ¹ , Maria Dias ² , Dina Rindlisbacher ³ , Marcus Cheetham ³ and Hugo Gamboa ²

Affiliations: ¹ LIBPhys (Laboratory for Instrumentation, Biomedical Engineering and Radiation Physics), Faculdade de Ciências e Tecnologia, Universidade Nova de Lisboa, Caparica, Portugal, Department of Internal Medicine, University Hospital Zurich, Zurich, Switzerland , ² LIBPhys (Laboratory for Instrumentation, Biomedical Engineering and Radiation Physics), Faculdade de Ciências e Tecnologia, Universidade Nova de Lisboa, Caparica, Portugal , ³ Department of Internal Medicine, University Hospital Zurich, Zurich, Switzerland, University Research Priority Program "Dynamics of Healthy Aging", University Zurich, Zurich, Switzerland

Digital Object Identifier: 10.5220/0008867500300037

Proceedings of BIOSIGNALS 2020, ISBN: 978-989-758-398-8

Quick Abstract

Eye-tracking (ET) systems, which capture eye movements, are often used to measure human behavior while interacting with a user interface. Given the high costs and challenges of acquiring, installing and ensuring good calibration of ET systems, the use of pointer (or mouse) tracking is gaining interest as a viable alternative in research on human-computer interaction. In this study, we measured and evaluated temporal and spatial relationships between eye and pointer movements in a standardized task that allowed us to examine the relationship between eye and pointer movements while participants made decisions under conditions of high and low uncertainty. We collected data from N=81 participants and applied a range of metrics to a total of 5205 decision trials. The overall findings show that the convergence between eye and pointer movements is consistently high. Importantly, there are differences in levels of convergence depending on the temporal, spatial and combined temporo-spatial metrics used. There are also differences in eye-pointer convergence depending on the relative level of decision uncertainty in the task. In conclusion, the present findings favour the use of pointer tracking to analyse human-computer interaction in more complex tasks.

Predicting Response Uncertainty in Online Surveys: A Proof of Concept

Topics: Detection and Identification; Pattern Recognition & Machine Learning for Biosignal Data

Authors: Maria Dias ¹ , Catia Cepeda ² , Dina Rindlisbacher ³ , Edouard Battegay ³ , Marcus Cheetham ³ and Hugo Gamboa ¹

Affiliations: ¹ LIBPhys (Laboratory for Instrumentation, Biomedical Engineering and Radiation Physics), Faculdade de Ciências e Tecnologia, Universidade Nova de Lisboa, Caparica and Portugal , ² LIBPhys (Laboratory for Instrumentation, Biomedical Engineering and Radiation Physics), Faculdade de Ciências e Tecnologia, Universidade Nova de Lisboa, Caparica, Portugal, Department of Internal Medicine, University Hospital Zurich, Zurich and Switzerland , ³ Department of Internal Medicine, University Hospital Zurich, Zurich, Switzerland, Center of Competence Multimorbidity, University of Zurich, Zurich and Switzerland

Digital Object Identifier: 10.5220/0007381801550162

Proceedings of BIOSIGNALS 2019, ISBN: 978-989-758-353-7

Quick Abstract

Online questionnaire-based research is growing at a fast pace. Mouse-tracking methods provide a potentially important data source for this research by enabling the capture of respondents’ online behaviour while answering questionnaire items. This behaviour can give insight into respondents’ perceptual, cognitive and affective processes. The present work focused on the potential use of mouse movements to indicate uncertainty when answering questionnaire items and used machine learning methods as a basis to model these. N=79 participants completed an online questionnaire while mouse data was tracked. Mouse movement features were extracted and selected for model training and testing. Using logistic regression and k-fold cross-validation, the model achieved an estimated performance accuracy of 89%. The findings show that uncertainty is indicated by an increase in the number of horizontal direction inversions and the distance covered by the mouse and by longer interaction times with and a higher number of revisits to questionnaire items that evoked uncertainty. Future work should validate these methods further.

Related Ontology Subjects/Areas/Topics (3)

Biomedical Engineering

Biomedical Signal Processing

Detection and Identification

Design and Evolution of an Opto-electronic Device for VOCs Detection

Topics: Biomedical Instrumentation; Biomedical Sensors; Health Monitoring Devices; Biomaterials; Diagnostic devices

Authors: Ana Carolina Pádua ¹ , Susana Palma ¹ , Jonas Gruber ² , Hugo Gamboa ³ and Ana Cecília Roque ¹

Affiliations: ¹ Universidade NOVA de Lisboa, Portugal , ² Instituto de Química da Universidade de São Paulo, Brazil , ³ Faculdade de Ciências e Tecnologia da Universidade NOVA de Lisboa, Portugal

Digital Object Identifier: 10.5220/0006558100480055

Proceedings of BIODEVICES 2018, ISBN: 978-989-758-277-6

Quick Abstract

Electronic noses (E-noses) are devices capable of detecting and identifying Volatile Organic Compounds (VOCs) in a simple and fast method. In this work, we present the development process of an opto-electronic device based on sensing films that have unique stimuli-responsive properties, altering their optical and electrical properties, when interacting with VOCs. This interaction results in optical and electrical signals that can be collected, and further processed and analysed. Two versions of the device were designed and assembled. E-nose V1 is an optical device, and E-nose V2 is a hybrid opto-electronic device. Both E-noses architectures include a delivery system, a detection chamber, and a transduction system. After the validation of the E-nose V1 prototype, the E-nose V2 was implemented, resulting in an easy-to-handle, miniaturized and stable device. Results from E-nose V2 indicated optical signals reproducibility, and the possibility of coupling the electrical signals to the optical response for VOCs sensing.

Related Ontology Subjects/Areas/Topics (9)

Biomaterials	Biomedical Instruments and Devices	Health Monitoring Devices
Biomedical Engineering	Biomedical Sensors	Human-Computer Interaction
Biomedical Instrumentation	Devices	Physiological Computing Systems

Development of the Decision Support System in Treatment of Arterial Hypertension - Application of Artificial Neural Networks for Evaluation of Heart Rate Variability Signals

Authors: Anton Dolganov ¹ , Vladimir Kublanov ¹ , David Belo ² and Hugo Gamboa ²

Affiliations: ¹ Ural Federal University, Russian Federation , ² Faculdade de Ciências e Tecnologia da Universidade Nova de Lisboa, Portugal

Digital Object Identifier: 10.5220/0006728903250331

Proceedings of NENT 2018, ISBN: 978-989-758-279-0

Quick Abstract

The article presents first step of the decision support system development in treatment of arterial hypertension. Results of arterial hypertension diagnostic task by short-term signals of heart rate variability are presented. The tilt test study was used as the functional load. The peculiarity of this work is application of neural networks for this task. The different number of hidden layers in the neural networks and different number of neurons in hidden layers were tested in this study. The classification accuracy of the neural networks was compared with those of simple machine learning classifiers. The following steps of the decision support system development are declared.

Automated Detection of Mind Wandering: A Mobile Application

Topics: Medical Signal Acquisition, Analysis and Processing; Wearable Sensors and Systems; Real-Time Systems

Authors: Marcus Cheetham ¹ , Cátia Cepeda ² and Hugo Gamboa ³

Affiliations: ¹ University of Zurich and Nungin University, Switzerland , ² FCT-UNL, Portugal , ³ FCT-UNL and PLUX Wireless Biosignals S.A, Portugal

Digital Object Identifier: 10.5220/0005702401980205

Proceedings of BIOSIGNALS 2016/BIOSTEC, ISBN: 978-989-758-170-0

Quick Abstract

There is growing interest in mindfulness-based training of attention. A particular challenge for novices is learning to sustain focused attention while ensuring that the mind does not wander. This paper presents the development of a tool for the automated detection of episodes of mind wandering (MW), on the basis of biosignals, while normal healthy participants engaged in brief mindfulness-based training (BMT) of attention. BMT required five 20-minute training sessions on consecutive days and entailed practice of breath-focused attention, a typical exercise in mindfulness-based techniques of stress-reduction. Heart rate, respiratory rate, electrodermal and electromyographic activity were measured, and participants pressed a button to indicate the subjective detection of MW during training. The data showed that BMT did not influence our measures of stress but BMT was effective in reducing the frequency of subjectively detected MW events. The algorithm for offline detection of MW achieved an accuracy of 85%. Based on this algorithm, a mobile application was developed for automated MW detection in real-time. The application requires the use of easily placeable sensors, provides a new approach to the real-time MW detection, and could be developed further for use in MW-related investigations and interventions (such as mindfulness-based training of focused attention).

Related Ontology Subjects/Areas/Topics (11)

Applications and Services	Devices	Physiological Computing Systems
Biomedical Engineering	Health Information Systems	Real-Time Systems
Biomedical Signal Processing	Human-Computer Interaction	Wearable Sensors and Systems
Computer Vision, Visualization and Computer Graphics	Medical Image Detection, Acquisition, Analysis and Processing

Electromyographic Signal Dynamic Behavior in Neuropathies - Spectral Parameters Evaluation and Classification

Topics: Pattern Recognition; Medical Signal Acquisition, Analysis and Processing; Time and Frequency Response; Detection and Identification

Authors: Maria Marta Santos ¹ , Ana Luisa Gomes ¹ , Hugo Gamboa ¹ , Mamede Carvalho ² , Susana Pinto ² and Carla Quintão ³

Affiliations: ¹ Universidade Nova de Lisboa and PLUX - Wireless Biosignals, Portugal , ² Universidade de Lisboa, Portugal , ³ Universidade Nova de Lisboa and Universidade de Lisboa, Portugal

Digital Object Identifier: 10.5220/0005215602270234

Proceedings of BIOSIGNALS 2015/BIOSTEC, ISBN: 978-989-758-069-7

Quick Abstract

Amyotrophic Lateral Sclerosis (ALS) is a neurodegenerative disease characterized by motor neurons degeneration, which reduces muscular force, being very difficult to diagnose. Mathematical methods, such as Coherence, Phase Locking Factor (PLF), Fractal Dimension (FD), Lempel-Ziv (LZ) techniques, Detrended Fluctuation Analysis (DFA) and Multiscale Entropy (MSE) are used to analyze the surface electromiographic signal’s chaotic behavior and evaluate different muscle groups’ synchronization. Surface electromiographic signal acquisitions were performed in upper limb muscles, being the analysis executed for instants of contraction recorded from patients and control groups. Results from LZ, DFA and MSE analysis present capability to distinguish between the patient and the control groups, whereas coherence, PLF and FD algorithms present results very similar for both groups. LZ, DFA and MSE algorithms appear then to be a good measure of corticospinal pathways integrity. A classification algorithm was applied to the results in combination with extracted features from the surface electromiographic signal, with an accuracy percentage higher than 70% for 118 combinations for at least one classifier. The classification results demonstrate capability to distinguish both groups. These results can demonstrate a major importance in the disease diagnose, once surface electromyography (sEMG) may be used as an auxiliary diagnose method.

Related Ontology Subjects/Areas/Topics (21)

Applications and Services	Health Engineering and Technology Applications	Pattern Recognition
Artificial Intelligence	Human-Computer Interaction	Physiological Computing Systems
Biomedical Engineering	Informatics in Control, Automation and Robotics	Sensor Networks
Biomedical Signal Processing	Medical Image Detection, Acquisition, Analysis and Processing	Signal Processing, Sensors, Systems Modeling and Control
Computer Vision, Visualization and Computer Graphics	Methodologies and Methods	Soft Computing
Data Manipulation	Neurocomputing	Time and Frequency Response
Detection and Identification	Neurotechnology, Electronics and Informatics	Time-Frequency Analysis

Human Activity Recognition Based on Novel Accelerometry Features and Hidden Markov Models Application

Topics: Biometrics; Pattern Recognition; Wearable Sensors and Systems; Time and Frequency Response

Authors: Ana Luísa Gomes ¹ , Vítor Paixão ² and Hugo Gamboa ³

Affiliations: ¹ Universidade Nova de Lisboa, Portugal , ² Champalimaud Centre for the Unknown, Portugal , ³ Universidade Nova de Lisboa and PLUX - Wireless Biosignals, Portugal

Digital Object Identifier: 10.5220/0005215800760085

Proceedings of BIOSIGNALS 2015/BIOSTEC, ISBN: 978-989-758-069-7

Quick Abstract

The Human Activity Recognition (HAR) systems require objective and reliable methods that can be used in the daily routine and must offer consistent results according to the performed activities. In this work, a framework for human activity recognition in accelerometry (ACC) based on our previous work and with new features and techniques was developed. The new features set covered wavelets, the CUIDADO features implementation and the Log Scale Power Bandwidth creation. The Hidden Markov Models were also applied to the clustering output. The Forward Feature Selection chose the most suitable set from a 423th dimensional feature vector in order to improve the clustering performances and limit the computational demands. K-means, Affinity Propagation, DBSCAN and Ward were applied to ACC databases and showed promising results in activity recognition: from 73.20% 7.98% to 89.05% +/- 7.43% and from 70.75% +/- 10.09% to 83.89% +/- 13.65% with the Hungarian accuracy (HA) for the FCHA and PAMAP databases, respectively. The Adjust Rand Index (ARI) was also applied as clustering evaluation method. The developed algorithm constitutes a contribution for the development of reliable evaluation methods of movement disorders for diagnosis and treatment applications.

Related Ontology Subjects/Areas/Topics (26)

Applications	Health Information Systems	Physiological Computing Systems
Artificial Intelligence	Human-Computer Interaction	Sensor Networks
Biomedical Engineering	Informatics in Control, Automation and Robotics	Signal Processing, Sensors, Systems Modeling and Control
Biomedical Signal Processing	Methodologies and Methods	Soft Computing
Biometrics	Multimedia	Telecommunications
Biometrics and Pattern Recognition	Multimedia Signal Processing	Time and Frequency Response
Data Manipulation	Neurocomputing	Time-Frequency Analysis
Devices	Neurotechnology, Electronics and Informatics	Wearable Sensors and Systems
Health Engineering and Technology Applications	Pattern Recognition

Multi-biosignals Analysis - The Effect of Peripheral Nerve Stimulation on Skin Conductance and Heart Rate Variability

Topics: Medical Signal Acquisition, Analysis and Processing; Time and Frequency Response

Authors: Tiago Araújo ¹ , Pedro Dias ¹ , Neuza Nunes ² and Hugo Gamboa ¹

Affiliations: ¹ New University of Lisbon, Portugal , ² PLUX - Wireless Biosignals S.A., Portugal

Digital Object Identifier: 10.5220/0005217902350239

Proceedings of BIOSIGNALS 2015/BIOSTEC, ISBN: 978-989-758-069-7

Quick Abstract

Objectives: This study aims to evaluate the influence of standard electrical stimulation on human electrophysiology. Methods: A total of 10 healthy subjects were submitted to the same protocol. The electrical stimuli were applied on the median nerve of the left wrist. Blood Volume Pulse (BVP) and Electrodermal Activity (EDA) signals were acquired from the index finger through an oximeter and from both the abductor pollicis muscle and the 3rd palmar interosseous muscle of the right hand, respectively. Nerve stimulation was performed using increasing intensities current: range from 5 to 30 mA, with 1mA step and applying 20 stimuli per step. Heart Rate (HR) and Heart Rate Variability (HRV) were computed, from the analysis of the latency between BVP pulses, in basal state and during stimulation. EDA parameters response latency, response rise time and readaptation slope were computed for each burst. Discussion: Electrical stimulation reveals to influence several parameters of the Autonomic Nervous System (ANS). It was easily detected an EDA rise response for each of the applied bursts and also an increase of the HRV during stimulation.

Related Ontology Subjects/Areas/Topics (9)

Applications and Services	Computer Vision, Visualization and Computer Graphics	Signal Processing, Sensors, Systems Modeling and Control
Biomedical Engineering	Informatics in Control, Automation and Robotics	Time and Frequency Response
Biomedical Signal Processing	Medical Image Detection, Acquisition, Analysis and Processing	Time-Frequency Analysis

New Visualization Model for Large Scale Biosignals Analysis

Topics: Medical Signal Acquisition, Analysis and Processing; Cybernetics and User Interface Technologies; Monitoring and Telemetry

Authors: Catarina Cavaco ¹ , Ricardo Gomes ¹ , Hugo Gamboa ² and Ricardo Matias ³

Affiliations: ¹ Faculdade de Ciências e Tecnologias - Universidade Nova de Lisboa, Portugal , ² Faculdade de Ciências e Tecnologias - Universidade Nova de Lisboa and PLUX Wireless Biosignals, Portugal , ³ Escola Superior de Saúde - Instituto Politécnico de Setúbal and Faculdade de Motricidade Humana - Universidade de Lisboa, Portugal

Digital Object Identifier: 10.5220/0005207201900197

Proceedings of BIOSIGNALS 2015/BIOSTEC, ISBN: 978-989-758-069-7

Quick Abstract

The development of new resources in the medical field, such as wearable sensors, allowed the improvement of biosignals monitoring. Acquired data is then an important source of information to clinicians and researchers. Thus, extracting useful information from data is a task of the greatest importance that involves a variety of concepts and methods, from which stands out data visualization. However, these methods present several limitations mainly when dealing with big data. In this paper we present an innovative web-based application for biosignals visualization and exploration in a fast and user friendly way overcoming the detected limitations. Three case studies are presented and a usability study supports the reliability of the implemented work.

Related Ontology Subjects/Areas/Topics (11)

Applications and Services	Cybernetics and User Interface Technologies	Medical Image Detection, Acquisition, Analysis and Processing
Biomedical Engineering	Devices	Monitoring and Telemetry
Biomedical Signal Processing	Human-Computer Interaction	Physiological Computing Systems
Computer Vision, Visualization and Computer Graphics	Information and Systems Security

Quadriceps Muscle Fatigue and Comfort Generated by Neuromuscular Electrical Stimulation with Current Modulated Waveforms

Topics: Medical Signal Acquisition, Analysis and Processing; Motion Control

Authors: Tiago Araújo ¹ , Ana Anjos ² , Neuza Nunes ³ , Pedro Rebelo ² and Hugo Gamboa ¹

Affiliations: ¹ New University of Lisbon, Portugal , ² Polytecnic Institute of Lisbon, Portugal , ³ PLUX - Wireless Biosignals S.A., Portugal

Digital Object Identifier: 10.5220/0005267400920098

Proceedings of BIOSIGNALS 2015/BIOSTEC, ISBN: 978-989-758-069-7

Quick Abstract

Introduction: Neuromuscular electrical stimulation (NMES) is used by physical therapists in the clinic. The efficacy of NMES is limited by the rapid onset muscle fatigue. The role of NMES parameters is muscle fatigue is not clear. Objective: To determine the effects of shape waveform on muscle fatigue, during NMES. Methods: Twelve healthy subjects participated in the study. Subjects were assigned to 1 of 3 groups, randomly. Group assignment determined the order in which they were tested using 3 different shape waveforms. Maximal voluntary isometric contraction (MVIC) was measured during the first session. Fatigue test was applied with amplitude required to elicit 50% of the MVIC. In each 3 testing sessions torque of contraction and level comfort were measured, and percent fatigue was calculated. Analysis of variance tests for dependent samples was used to determine the effect of shape waveform on muscle fatigue and comfort scores Results: The results showed no one shape waveform was most fatigable and that SQ wave induced more uncomfortable stimulus.

Related Ontology Subjects/Areas/Topics (7)

Animation and Simulation	Biomedical Signal Processing	Medical Image Detection, Acquisition, Analysis and Processing
Applications and Services	Computer Vision, Visualization and Computer Graphics	Motion Control
Biomedical Engineering