Guillermo Hidalgo GadeaRuhr-Universität Bochum | RUB · Institut für Kognitive Neurowissenschaft (IKN)
Guillermo Hidalgo Gadea
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PhD student working on embodied cognition, agency and learning in pigeons. Interested in comparative psychology, numerical cognition, computational modelling and machine learning. My research has employed a variety of methods, including behavioral experiments in non-human primates and physiological signal processing in humans for fatigue and discomfort detection.
Fatigued driving is one of the main contributors to road traffic accidents. Poor sleep quality and lack of sleep negatively affect driving performance, and extreme states of fatigue can cause microsleep (i.e. short episodes of sleep with complete loss of awareness). Driver monitoring systems analyze biosignals (e.g. gaze, blinking, heart rate) and...
In this study, ways of accessing information about ambiguous words were tested. The non-selective access hypothesis states that all meanings of an ambiguous word are activated for retrieval. In contrast the selective access hypothesis predicates that only the meaning that fits the semantic context is accessed. To test this, 29 students (Age: 21....
This paper presents the idea of brute force feature extraction for Electrocardiography (ECG) signals applied to discomfort detection. To build an ECG Discomfort Corpus an experimental discomfort induction was conducted. 50 subjects underwent a 2 h (dis-)comfort condition in separate sessions in randomized order. ECG and subjective discomfort was re...
Social affective touch is an important aspect of close relationships in humans. It has been also observed in many non-human primate species. Despite the high relevance of behaviours like embraces for personal wellbeing and mental health, they remain vastly under-investigated in psychology. This may be because psychology often relies on a limited re...
This study aims to provide an interdisciplinary perspective on biosignal processing for driver monitoring systems. Machine learning models were trained with ECG, Pupil Diameter and Eyelid Opening data from overnight sleep deprived subjects experiencing microsleep in a supervised test track driving environment. Model performance of 0.85 accuracy and...
This study aims to provide an interdisciplinary perspective on biosignal processing for driver monitoring systems. Machine learning models were trained with ECG, Pupil Diameter and Eyelid Opening data from overnight sleep deprived subjects experiencing micros-leep in a supervised test track driving environment. Model performance of 0.85 accuracy an...
The purpose of this study was to provide an interdisciplinary perspective on biosignal processing for a driver monitoring system by developing a machine learning environment to detect microsleep. Hypotheses assumed improved classification performance for (1) biosignal multiplicity and (2) data processing multimodality. ECG, Pupil Diameter and Eyeli...
I am working on a subsequence clustering task to decompose time series of given behaviors (e.g. videos) to identify action units, action sequences and behavior classes. The question is on the expected structure of modelled behavior (e.g. hierarchical) and how to give each action unite, sub-system etc. an accurate interpretation (post-hoc).
Is the classification in subsystems, modes, modules and actions as in Tinbergen (1942) and Timberlake (1994 ) still up to date in Ethology? Any recommended literature?
I'm starting the literature review with Bowers (2018) and Bowers (2020):