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This book constitutes the proceedings of the First International Conference on Physiological Computing Systems, PhyCS 2014, held in Lisbon, Portugal, in January 2014. The 10 papers presented in this volume were carefully reviewed and selected from 52 submissions. They are organized in topical sections named: methodologies and methods; devices; applications; and human factors.



Methodologies and Methods


Wavelet Lifting over Information-Based EEG Graphs for Motor Imagery Data Classification

The imagination of limb movements offers an intuitive paradigm for the control of electronic devices via brain computer interfacing (BCI). The analysis of electroencephalographic (EEG) data related to motor imagery potentials has proved to be a difficult task. EEG readings are noisy, and the elicited patterns occur in different parts of the scalp, at different instants and at different frequencies. Wavelet transform has been widely used in the BCI field as it offers temporal and spectral capabilities, although it lacks spatial information. In this study we propose a tailored second generation wavelet to extract features from these three domains. This transform is applied over a graph representation of motor imaginary trials, which encodes temporal and spatial information. This graph is enhanced using per-subject knowledge in order to optimise the spatial relationships among the electrodes, and to improve the filter design. This method improves the performance of classifying different imaginary limb movements maintaining the low computational resources required by the lifting transform over graphs. By using an online dataset we were able to positively assess the feasibility of using the novel method in an online BCI context.
Javier Asensio-Cubero, John Q. Gan, Ramaswamy Palaniappan

Extracting Emotions and Communication Styles from Prosody

According to many psychological and social studies, vocal messages contain two distinct channels—an explicit, linguistic channel, and an implicit, paralinguistic channel. In particular, the latter contains information about the emotional state of the speaker, providing clues about the implicit meaning of the message. Such information can improve applications requiring human-machine interactions (for example, Automatic Speech Recognition systems or Conversational Agents), as well as support the analysis of human-human interactions (for example, clinic or forensic applications). PrEmA, the tool we present in this work, is able to recognize and classify both emotions and communication style of the speaker, relying on prosodic features. In particular, recognition of communication-styles is, to our knowledge, new, and could be used to infer interesting clues about the state of the interaction. PrEmA uses two LDA-based classifiers, which rely on two sets of prosodic features. Experimenting PrEmA with Italian speakers we obtained \(Ac=71\,\%\) for emotions and \(Ac=86\,\%\) for communication styles.
Licia Sbattella, Luca Colombo, Carlo Rinaldi, Roberto Tedesco, Matteo Matteucci, Alessandro Trivilini

Multiresolution Feature Extraction During Psychophysiological Inference: Addressing Signals Asynchronicity

Predicting the psychological state of the user using physiological measures is one of the main objectives of physiological computing. While numerous works have addressed this task with great success, a large number of challenges remain to be solved in order to develop recognition approaches that can precisely and reliably feed human-computer interaction systems. This chapter focuses on one of these challenges which is the temporal asynchrony between different physiological signals within one recognition model. The chapter proposes a flexible and suitable method for feature extraction based on empirical optimisation of windows’ latency and duration. The approach is described within the theoretical framework of the psychophysiological inference and its common implementation using machine learning. The method has been experimentally validated (46 subjects) and results are presented. Empirically optimised values for the extraction windows are provided.
François Courtemanche, Aude Dufresne, Élise L. LeMoyne



Paper-Based Inkjet Electrodes

Experimental Study for ECG Applications
Electrocardiographic (ECG) acquisition has evolved imensly over the last decade in particular with regards to sensing technology. From classical silver/silver chloride (Ag/AgCl) electrodes, to textile electrodes, and recently paper-based electrodes. In this paper we study a new type of silver/silver chloride (Ag/AgCl) electrodes based on a paper substrate that are produced using an inkjet printing technique. The cost reduction, easy-to-produce methodology, and easier recycling increase the potencial of application of these electrodes and opens this technology for everyday life use. We performed a comparison between this new type of electrode, with classical gelled Ag/AgCl electrodes and dry Ag/AgCl electrodes. We also compared the performance of each electrode when acquired using a professional-grade gold standard device, and a low cost platform. Experimental results showed that data acquired using our proposed inkjet printed electrode is highly correlated with data obtained through conventional electrodes. Moreover, the electrodes are robust to both high-end and low-end data acquisition devices.
Ana Priscila Alves, João Martins, Hugo Plácido da Silva, André Lourenço, Ana Fred, Hugo Ferreira

An EOG-Based Automatic Sleep Scoring System and Its Related Application in Sleep Environmental Control

Human beings spend approximately one third of their lives sleeping. Conventionally, to evaluate a subjects sleep quality, all-night polysomnogram (PSG) readings are taken and scored by a well-trained expert. Unlike a bulky PSG or EEG recorder on the head, the development of an electrooculogram (EOG)-based automatic sleep-staging system will enable physiological computing systems (PhyCS) to progress toward easy sleep and comfortable monitoring. In this paper, an EOG-based sleep scoring system is proposed. EOG signals are also coupling some of sleep characteristics of EEG signals. Compared to PSG or EEG recordings, EOG has the advantage of easy placement, and can be operated by the user individually at home. The proposed method was found to be more than 83 % accurate when compared with the manual scorings applied to sixteen subjects. In addition to sleep-quality evaluation, the proposed system encompasses adaptive brightness control of light according to online monitoring of the users sleep stages. The experiments show that the EOG-based sleep scoring system is a practicable solution for homecare and sleep monitoring due to the advantages of comfortable recording and accurate sleep staging.
Chih-En Kuo, Sheng-Fu Liang, Yi-Chieh Lee, Fu-Yin Cherng, Wen-Chieh Lin, Peng-Yu Chen, Yen-Chen Liu, Fu-Zen Shaw



Upper Body Joint Angle Measurements for Physical Rehabilitation Using Visual Feedback

In clinical rehabilitation, biofeedback increases patient’s motivation making it one of the most effective motor rehabilitation mechanisms. In this field, it is very helpful for the patient and even for the therapist to know the level of success and performance of the training process. New rehabilitation technologies allow new forms of therapy for patients with Range of Motion (ROM) disorders. The aim of this work is to introduce a simple biofeedback system in a clinical environment for ROM measurements, since there is currently a lack of practical and cost-efficient methods available for this purpose. The Microsoft Kinect™ introduces the possibility of low cost, non intrusive human motion analysis in the rehabilitation field. In this paper we conduct a comparison study of the accuracy in the computation of ROM measurements between the Kinect™ Skeleton Tracking provided by Microsoft and the proposed algorithm based on depth analysis. Experimental results showed that our algorithm is able to overcome the limitations of the Microsoft algorithm when the pose estimation is used as a measuring system making it a valuable rehabilitation tool.
Marília Barandas, Hugo Gamboa, José Manuel Fonseca

Online Classifier Adaptation for the Detection of P300 Target Recognition Processes in a Complex Teleoperation Scenario

The detection of event related potentials and their usage for innovative applications became an increasingly important research topic for brain computer interfaces in the last couple of years. However, brain computer interfaces use methods that need to be trained on subject-specific data before they can be used. This problem must be solved for real-world applications in which humans are multi tasking and hence are to some degree are less predictable in their behavior compared to classical set ups for brain computer interfacing. In this paper, we show the detection and passive usage of the P300 related brain activity in a highly uncontrolled and noisy application scenario. The subjects are multi tasking, i.e., they perform a demanding senso-motor task, i.e., the telemanipulate a real robotic arm while responding to important messages. For telemanipulation, the subject wears an active exoskeleton to control a robotic arm, which is presented to him in a virtual scenario. By online analysis of the subject’s electroencephalogram we detect P300 related target recognition processes to infer on upcoming response behavior on presented task-relevant messages (Targets) or missing of response behavior in case a Target was not recognized. We show that a classifier that is trained to distinguish between brain activity evoked by recognized task-relevant stimuli (recognized Targets) and ignored frequent task-irrelevant stimuli (Standards) can be applied to classify between brain activity evoked by recognized targets and brain activity that is evoked in case that task-relevant stimuli are not recognized (Missed Targets). The applied transfer of classifier results in reduced performance. We show that this draw back of the approach can strongly be improved by using online machine learning tools to adapt the pre-trained classifier to the new class, i.e., to the Missed Target class, that was not used during training of the classifier.
Hendrik Woehrle, Elsa Andrea Kirchner

Impact on Biker Effort of Electric Bicycle Utilization: Results from On-Road Monitoring in Lisbon, Portugal

The objective of this work was to estimate the biker real physiological impacts (more specifically heart rate) of using electric bicycles (EB) instead of conventional bicycles (CB), by developing an appropriate methodology for on-road bio-signals data analysis. From the on-road monitoring data of 6 bikers, 2 routes and 3 bicycles in Lisbon, the results indicate a 57 % average reduction in HR variation from the use of EB, since under high power demanding situations, the electric motor attenuates human effort. The energy expenditure evaluation indicates that the total energy spent reaches ≈70 Wh/km for CB, while for EB that value is of ≈51 Wh/km of human energy (28 % lower than the CB) and ≈9 Wh/km of electricity consumption, resulting in a total of ≈60 Wh/km. As a result, using the EB allow a 14 % reduction in the total energy per km compared to the CB.
Gonçalo Duarte, Magno Mendes, Patrícia Baptista

Human Factors


Learning Effective Models of Emotions from Physiological Signals: The Seven Principles

Learning effective models from emotion-elicited physiological responses for the classification and description of emotions is increasingly required to derive accurate analysis from affective interactions. Despite the relevance of this task, there is still lacking an integrative view of existing contributions. Additionally, there is no agreement on how to deal with the differences of physiological responses across individuals, and on how to learn from flexible sequential behavior and subtle but meaningful spontaneous variations of the signals. In this work, we rely on empirical evidence to define seven principles for a robust mining physiological signals to recognize and characterize affective states. These principles compose a coherent and complete roadmap for the development of new methods for the analysis of physiological signals. In particular, these principles address the current over-emphasis on feature-based models by including critical generative views derived from different streams of research, including multivariate data analysis and temporal data mining. Additionally, we explore how to use background knowledge related with the experimental setting and psychophysiological profiles from users to shape the learning of emotion-centered models. A methodology that integrates these principles is proposed and validated using signals collected during human-to-human and human-to-robot affective interactions.
Rui Henriques, Ana Paiva

A Generic Effort-Based Behavior Description for User Engagement Analysis

Human interaction is to a large extent based on implicit, unconscious behavior and the related body language. In this article, we propose ‘Directed Effort’ a generic description of human behavior suitable as user engagement and interest input for higher level human-computer interaction applications. Research from behavioral and psychological sciences is consulted for the creation of an attention model which is designed to represent the engagement of people towards generic objects in public spaces. The functionality of this behavior analysis approach is demonstrated in a prototypical implementation to present the potential of the presented meta-level description of behavior.
Benedikt Gollan, Alois Ferscha


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