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Luis Eduardo Velez QuinteroDoktorand

Om mig

Doctoral researcher exploring behavioral user modeling with machine learning and adaptive virtual reality enviroments in the Data Science Group.

My research aims to create more personalized immersive virtual reality environments for healthcare, education, or professional training. The implications of this work are a step beyond personalized digital services like social networks and streaming platforms, towards personalized 3D scenarios such as the so-called 'metaverse'.

Most of my work refers to real-time analysis of physiological signals, motion trajectories, and gameplay interactions to classify subjective user characteristics like skill level, emotional states, or mental workload. My research interesects fields from machine learning, human-computer interaction (HCI), physiological computing, and immersive technologies (XR).

From the technical side, I spend most of my time designing and evaluating VR systems developed in the Unity software, hacking physiological sensors, and running ML algorithms in Python.

More information on my personal website.



I urval från Stockholms universitets publikationsdatabas

  • A Psychophysiological Model of Firearms Training in Police Officers

    2020. John E. Muñoz (et al.). Frontiers in Psychology 11


    Crucial elements for police firearms training include mastering very specific psychophysiological responses associated with controlled breathing while shooting. Under high-stress situations, the shooter is affected by responses of the sympathetic nervous system that can impact respiration. This research focuses on how frontal oscillatory brainwaves and cardiovascular responses of trained police officers (N = 10) are affected during a virtual reality (VR) firearms training routine. We present data from an experimental study wherein shooters were interacting in a VR-based training simulator designed to elicit psychophysiological changes under easy, moderate and frustrating difficulties. Outcome measures in this experiment include electroencephalographic and heart rate variability (HRV) parameters, as well as performance metrics from the VR simulator. Results revealed that specific frontal areas of the brain elicited different responses during resting states when compared with active shooting in the VR simulator. Moreover, sympathetic signatures were found in the HRV parameters (both time and frequency) reflecting similar differences. Based on the experimental findings, we propose a psychophysiological model to aid the design of a biocybernetic adaptation layer that creates real-time modulations in simulation difficulty based on targeted physiological responses.

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  • Implementation of Mobile-Based Real-Time Heart Rate Variability Detection for Personalized Healthcare

    2019. Luis Quintero (et al.). 2019 International Conference on Data Mining Workshops (ICDMW)


    The ubiquity of wearable devices together with areas like internet of things, big data and machine learning have promoted the development of solutions for personalized healthcare that use digital sensors. However, there is a lack of an implemented framework that is technically feasible, easily scalable and that provides meaningful variables to be used in applications for translational medicine. This paper describes the implementation and early evaluation of a physiological sensing tool that collects and processes photoplethysmography data from a wearable smartwatch to calculate heart rate variability in real-time. A technical open-source framework is outlined, involving mobile devices for collection of heart rate data, feature extraction and execution of data mining or machine learning algorithms that ultimately deliver mobile health interventions tailored to the users. Eleven volunteers participated in the empirical evaluation that was carried out using an existing mobile virtual reality application for mental health and under controlled slow-paced breathing exercises. The results validated the feasibility of implementation of the proposed framework in the stages of signal acquisition and real-time calculation of heart rate variability (HRV). The analysis of data regarding packet loss, peak detection and overall system performance provided considerations to enhance the real-time calculation of HRV features. Further studies are planned to validate all the stages of the proposed framework.

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  • Open-Source Physiological Computing Framework using Heart Rate Variability in Mobile Virtual Reality Applications

    2019. Luis Quintero, Panagiotis Papapetrou, John E. Muñoz. 2019 IEEE International Conference on Artificial Intelligence and Virtual Reality (AIVR)


    Electronic and mobile health technologies are posed as a tool that can promote self-care and extend coverage to bridge the gap in accessibility to mental care services between low-and high-income communities. However, the current technology-based mental health interventions use systems that are either cumbersome, expensive or require specialized knowledge to be operated. This paper describes the open-source framework PARE-VR, which provides heart rate variability (HRV) analysis to mobile virtual reality (VR) applications. It further outlines the advantages of the presented architecture as an initial step to provide more scalable mental health therapies in comparison to current technical setups; and as an approach with the capability to merge physiological data and artificial intelligence agents to provide computing systems with user understanding and adaptive functionalities. Furthermore, PARE-VR is evaluated with a feasibility study using a specific relaxation exercise with slow-paced breathing. The aim of the study is to get insights of the system performance, its capability to detect HRV metrics in real-time, as well as to identify changes between normal and slow-paced breathing using the HRV data. Preliminary results of the study, with the participation of eleven volunteers, showed high engagement of users towards the VR activity, and demonstrated technical potentialities of the framework to create physiological computing systems using mobile VR and wearable smartwatches for scalable health interventions. Several insights and recommendations were concluded from the study for enhancing the HRV analysis in real-time and conducting future similar studies.

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  • Understanding research methodologies when combining virtual reality technology with machine learning techniques

    2020. Luis Quintero. PETRA '20, 1-4


    Virtual Reality (VR) technology represents a new medium to provide immersive solutions in different fields. The analysis of a user while interacting in VR, through data science and machine learning (ML) techniques, might provide insights to deliver customized functionalities that enhance productivity and efficiency in learning tasks in education or rehabilitation processes in healthcare. However, empirical research involving VR often borrows methods from human-computer interaction intending to evaluate human behavior through technology, whereas ML intend to create mathematical models, usually with non-empirical approach. Their opposite nature might cause confusion for early-stage researchers wanting to understand and follow the methodological approaches and communicative practices in empirical studies that merge both VR and ML. This paper presents a scoping review of methodological strategies undertaken in 21 peer-reviewed research articles that involve both VR and ML. Results show and appraise different methodological approaches in research projects, and outline a set of recommendations to combine metrics from inferential statistics and evaluation of ML models to increase validity, reliability and trustworthiness in future research projects that intersect VR and ML.

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