107 research outputs found

    Unsupervised Motion Artifact Detection in Wrist-Measured Electrodermal Activity Data

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    One of the main benefits of a wrist-worn computer is its ability to collect a variety of physiological data in a minimally intrusive manner. Among these data, electrodermal activity (EDA) is readily collected and provides a window into a person's emotional and sympathetic responses. EDA data collected using a wearable wristband are easily influenced by motion artifacts (MAs) that may significantly distort the data and degrade the quality of analyses performed on the data if not identified and removed. Prior work has demonstrated that MAs can be successfully detected using supervised machine learning algorithms on a small data set collected in a lab setting. In this paper, we demonstrate that unsupervised learning algorithms perform competitively with supervised algorithms for detecting MAs on EDA data collected in both a lab-based setting and a real-world setting comprising about 23 hours of data. We also find, somewhat surprisingly, that incorporating accelerometer data as well as EDA improves detection accuracy only slightly for supervised algorithms and significantly degrades the accuracy of unsupervised algorithms.Comment: To appear at International Symposium on Wearable Computers (ISWC) 201

    Driver Drowsiness Detection: A Machine Learning Approach on Skin Conductance

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    The majority of car accidents worldwide are caused by drowsy drivers. Therefore, it is important to be able to detect when a driver is starting to feel drowsy in order to warn them before a serious accident occurs. Sometimes, drivers are not aware of their own drowsiness, but changes in their body signals can indicate that they are getting tired. Previous studies have used large and intrusive sensor systems that can be worn by the driver or placed in the vehicle to collect information about the driver’s physical status from a variety of signals that are either physiological or vehicle-related. This study focuses on the use of a single wrist device that is comfortable for the driver to wear and appropriate signal processing to detect drowsiness by analyzing only the physiological skin conductance (SC) signal. To determine whether the driver is drowsy, the study tests three ensemble algorithms and finds that the Boosting algorithm is the most effective in detecting drowsiness with an accuracy of 89.4%. The results of this study show that it is possible to identify when a driver is drowsy using only signals from the skin on the wrist, and this encourages further research to develop a real-time warning system for early detection of drowsiness

    A usability study of physiological measurement in school using wearable sensors

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    Measuring psychophysiological signals of adolescents using unobtrusive wearable sensors may contribute to understanding the development of emotional disorders. This study investigated the feasibility of measuring high quality physiological data and examined the validity of signal processing in a school setting. Among 86 adolescents, a total of more than 410 h of electrodermal activity (EDA) data were recorded using a wrist-worn sensor with gelled electrodes and over 370 h of heart rate data were recorded using a chest-strap sensor. The results support the feasibility of monitoring physiological signals at school. We describe specific challenges and provide recommendations for signal analysis, including dealing with invalid signals due to loose sensors, and quantization noise that can be caused by limitations in analog-to-digital conversion in wearable devices and be mistaken as physiological responses. Importantly, our results show that using toolboxes for automatic signal preprocessing, decomposition, and artifact detection with default parameters while neglecting differences between devices and measurement contexts yield misleading results. Time courses of students' physiological signals throughout the course of a class were found to be clearer after applying our proposed preprocessing steps

    Methods and techniques for analyzing human factors facets on drivers

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    Mención Internacional en el título de doctorWith millions of cars moving daily, driving is the most performed activity worldwide. Unfortunately, according to the World Health Organization (WHO), every year, around 1.35 million people worldwide die from road traffic accidents and, in addition, between 20 and 50 million people are injured, placing road traffic accidents as the second leading cause of death among people between the ages of 5 and 29. According to WHO, human errors, such as speeding, driving under the influence of drugs, fatigue, or distractions at the wheel, are the underlying cause of most road accidents. Global reports on road safety such as "Road safety in the European Union. Trends, statistics, and main challenges" prepared by the European Commission in 2018 presented a statistical analysis that related road accident mortality rates and periods segmented by hours and days of the week. This report revealed that the highest incidence of mortality occurs regularly in the afternoons during working days, coinciding with the period when the volume of traffic increases and when any human error is much more likely to cause a traffic accident. Accordingly, mitigating human errors in driving is a challenge, and there is currently a growing trend in the proposal for technological solutions intended to integrate driver information into advanced driving systems to improve driver performance and ergonomics. The study of human factors in the field of driving is a multidisciplinary field in which several areas of knowledge converge, among which stand out psychology, physiology, instrumentation, signal treatment, machine learning, the integration of information and communication technologies (ICTs), and the design of human-machine communication interfaces. The main objective of this thesis is to exploit knowledge related to the different facets of human factors in the field of driving. Specific objectives include identifying tasks related to driving, the detection of unfavorable cognitive states in the driver, such as stress, and, transversely, the proposal for an architecture for the integration and coordination of driver monitoring systems with other active safety systems. It should be noted that the specific objectives address the critical aspects in each of the issues to be addressed. Identifying driving-related tasks is one of the primary aspects of the conceptual framework of driver modeling. Identifying maneuvers that a driver performs requires training beforehand a model with examples of each maneuver to be identified. To this end, a methodology was established to form a data set in which a relationship is established between the handling of the driving controls (steering wheel, pedals, gear lever, and turn indicators) and a series of adequately identified maneuvers. This methodology consisted of designing different driving scenarios in a realistic driving simulator for each type of maneuver, including stop, overtaking, turns, and specific maneuvers such as U-turn and three-point turn. From the perspective of detecting unfavorable cognitive states in the driver, stress can damage cognitive faculties, causing failures in the decision-making process. Physiological signals such as measurements derived from the heart rhythm or the change of electrical properties of the skin are reliable indicators when assessing whether a person is going through an episode of acute stress. However, the detection of stress patterns is still an open problem. Despite advances in sensor design for the non-invasive collection of physiological signals, certain factors prevent reaching models capable of detecting stress patterns in any subject. This thesis addresses two aspects of stress detection: the collection of physiological values during stress elicitation through laboratory techniques such as the Stroop effect and driving tests; and the detection of stress by designing a process flow based on unsupervised learning techniques, delving into the problems associated with the variability of intra- and inter-individual physiological measures that prevent the achievement of generalist models. Finally, in addition to developing models that address the different aspects of monitoring, the orchestration of monitoring systems and active safety systems is a transversal and essential aspect in improving safety, ergonomics, and driving experience. Both from the perspective of integration into test platforms and integration into final systems, the problem of deploying multiple active safety systems lies in the adoption of monolithic models where the system-specific functionality is run in isolation, without considering aspects such as cooperation and interoperability with other safety systems. This thesis addresses the problem of the development of more complex systems where monitoring systems condition the operability of multiple active safety systems. To this end, a mediation architecture is proposed to coordinate the reception and delivery of data flows generated by the various systems involved, including external sensors (lasers, external cameras), cabin sensors (cameras, smartwatches), detection models, deliberative models, delivery systems and machine-human communication interfaces. Ontology-based data modeling plays a crucial role in structuring all this information and consolidating the semantic representation of the driving scene, thus allowing the development of models based on data fusion.I would like to thank the Ministry of Economy and Competitiveness for granting me the predoctoral fellowship BES-2016-078143 corresponding to the project TRA2015-63708-R, which provided me the opportunity of conducting all my Ph. D activities, including completing an international internship.Programa de Doctorado en Ciencia y Tecnología Informática por la Universidad Carlos III de MadridPresidente: José María Armingol Moreno.- Secretario: Felipe Jiménez Alonso.- Vocal: Luis Mart

    Physiological signal-based emotion recognition from wearable devices

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    The interest in computers recognizing human emotions has been increasing recently. Many studies have been done about recognizing emotions from physical signals such as facial expressions or from written text with good results. However, recognizing emotions from physiological signals such as heart rate, from wearable devices without physical signals have been challenging. Some studies have given good, or at least promising results. The challenge for emotion recognition is to understand how human body actually reacts to different emotional triggers and to find a common factors among people. The aim of this study is to find out whether it is possible to accurately recognize human emotions and stress from physiological signals using supervised machine learning. Further, we consider the question what type of biosignals are most informative for making such predictions. The performance of Support Vector Machines and Random Forest classifiers are experimentally evaluated on the task of separating stress and no-stress signals from three different biosignals: ECG, PPG and EDA. The challenges with these biosginals from acquiring them to pre-processing the signals are addressed and their connection to emotional experience is discussed. In addition, the challenges and problems on experimental setups used in previous studies are addressed and especially the usability problems of the dataset. The models implemented in this thesis were not able to accurately classify emotions using supervised machine learning from the dataset used. The models did not perform remarkably better than just randomly choosing labels. PPG signal however performed slightly better than ECG or EDA for stress detection

    Biomedical Signal Analysis of the Brain and Systemic Physiology

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    Near-infrared spectroscopy (NIRS) is a non-invasive and easy-to-use diagnostic technique that enables real-time tissue oxygenation measurements applied in various contexts and for different purposes. Continuous monitoring with NIRS of brain oxygenation, for example, in neonatal intensive care units (NICUs), is essential to prevent lifelong disabilities in newborns. Moreover, NIRS can be applied to observe brain activity associated with hemodynamic changes in blood flow due to neurovascular coupling. In the latter case, NIRS contributes to studying cognitive processes allowing to conduct experiments in natural and socially interactive contexts of everyday life. However, it is essential to measure systemic physiology and NIRS signals concurrently. The combination of brain and body signals enables to build sophisticated systems that, for example, reduce the false alarms that occur in NICUs. Furthermore, since fNIRS signals are influenced by systemic physiology, it is essential to understand how the latter impacts brain signals in functional studies. There is an interesting brain body coupling that has rarely been investigated yet. To take full advantage of these brain and body data, the aim of this thesis was to develop novel approaches to analyze these biosignals to extract the information and identify new patterns, to solve different research or clinical questions. For this the development of new methodological approaches and sophisticated data analysis is necessary, because often the identification of these patterns is challenging or not possible with traditional methods. In such cases, automatic machine learning (ML) techniques are beneficial. The first contribution of this work was to assess the known systemic physiology augmented (f)NIRS approach for clinical use and in everyday life. Based on physiological and NIRS signals of preterm infants, an ML-based classification system has been realized, able to reduce the false alarms in NICUs by providing a high sensitivity rate. In addition, the SPA-fNIRS approach was further applied in adults during a breathing task. The second contribution of this work was the advancement of the classical fNIRS hyperscanning method by adding systemic physiology measures. For this, new biosignal analyses in the time-frequency domain have been developed and tested in a simple nonverbal synchrony task between pairs of subjects. Furthermore, based on SPA-fNIRS hyperscanning data, another ML-based system was created, which is able distinguish familiar and unfamiliar pairs with high accuracy. This approach enables to determine the strength of social bonds in a wide range of social interaction contexts. In conclusion, we were the first group to perform a SPA-fNIRS hyperscanning study capturing changes in cerebral oxygenation and hemodynamics as well as systemic physiology in two subjects simultaneously. We applied new biosignals analysis methods enabling new insights into the study of social interactions. This work opens the door to many future inter-subjects fNIRS studies with the benefit of assessing the brain-to-brain, the brain-to-body, and body-to-body coupling between pairs of subjects

    Seizure Detection, Seizure Prediction, and Closed-Loop Warning Systems in Epilepsy

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    Nearly one-third of patients with epilepsy continue to have seizures despite optimal medication management. Systems employed to detect seizures may have the potential to improve outcomes in these patients by allowing more tailored therapies and might, additionally, have a role in accident and SUDEP prevention. Automated seizure detection and prediction require algorithms which employ feature computation and subsequent classification. Over the last few decades, methods have been developed to detect seizures utilizing scalp and intracranial EEG, electrocardiography, accelerometry and motion sensors, electrodermal activity, and audio/video captures. To date, it is unclear which combination of detection technologies yields the best results, and approaches may ultimately need to be individualized. This review presents an overview of seizure detection and related prediction methods and discusses their potential uses in closed-loop warning systems in epilepsy

    Automatic Detection of Electrodermal Activity Events during Sleep

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    Publisher Copyright: © 2023 by the authors.Currently, there is significant interest in developing algorithms for processing electrodermal activity (EDA) signals recorded during sleep. The interest is driven by the growing popularity and increased accuracy of wearable devices capable of recording EDA signals. If properly processed and analysed, they can be used for various purposes, such as identifying sleep stages and sleep-disordered breathing, while being minimally intrusive. Due to the tedious nature of manually scoring EDA sleep signals, the development of an algorithm to automate scoring is necessary. In this paper, we present a novel scoring algorithm for the detection of EDA events and EDA storms using signal processing techniques. We apply the algorithm to EDA recordings from two different and unrelated studies that have also been manually scored and evaluate its performances in terms of precision, recall, and (Formula presented.) score. We obtain (Formula presented.) scores of about 69% for EDA events and of about 56% for EDA storms. In comparison to the literature values for scoring agreement between experts, we observe a strong agreement between automatic and manual scoring of EDA events and a moderate agreement between automatic and manual scoring of EDA storms. EDA events and EDA storms detected with the algorithm can be further processed and used as training variables in machine learning algorithms to classify sleep health.Peer reviewe
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