Mitja Lustrek

Jožef Stefan Institute | IJS · Department of Intelligent Systems

Human Sensing, a field that leverages technology to monitor human activities, psycho-physiological states, and interactions with the environment, enhances our understanding of human behavior and drives the development of advanced services that improve overall quality of life. However, its reliance on detailed and often privacy-sensitive data as the...

Congestive heart failure (CHF) is an incurable disease where a key objective of the treatment is to maintain the patient’s quality of life (QoL) as much as possible. A model that predicts health-related QoL (HRQoL) based on physiological and ambient parameters can be used to monitor these parameters for the patient’s benefit. Since it is difficult...

Introduction/Background: Cardiovascular symptoms appear in a high proportion of patients in the few months following a severe SARS-CoV-2 infection. Non-invasive methods to predict disease severity could help personalizing healthcare and reducing the occurrence of these symptoms. Research Questions/Hypothesis: We hypothesized that blood long noncodi...

In our study aimed at improving the healthcare system for the aging population, we compared healthcare quality evaluations between 96 older individuals and 30 healthcare providers in Split-Dalmatia County (Croatia). Using nonparametric analyses such as the Mann-Whitney and Wilcoxon tests on Likert scale questionnaire scores, we found most participa...

Virtual reality (VR) technology is often referred to as the ‘ultimate empathy machine’ due to its capability to immerse users in alternate perspectives and environments beyond their immediate physical reality. In this study, participants will be immersed in 3-dimensional 360° VR videos where actors express different emotions (sadness, happiness, an...

Adequate hydration is important for one’s health, but many people do not consume sufficient fluids. By constantly monitoring fluid intake, we gain information that can be extremely useful in dealing with unhealthy drinking habits. This paper deals with the problem of developing a machine learning method for drinking detection, intended for use on a...

Introduction Existing literature indicates that academic staff experience increasing levels of work stress. This study investigated associations between day-to-day threat and challenge appraisal and day-to-day problem-focused coping, emotion-focused coping, and seeking social support among academic office workers. Methods This study is based on an...

Emotions are an essential constituent of well-being. They can be recognized using contact-free sensors such as cameras, based on facial expressions and physiological parameters, such as changes in temperature. We conducted an early evaluation of emotion recognition from RGB cameras using two datasets and high-lighted challenges such as subject-spec...

Tools for predicting COVID-19 outcomes enable personalized healthcare, potentially easing the disease burden. This collaborative study by 15 institutions across Europe aimed to develop a machine learning model for predicting the risk of in-hospital mortality post-SARS-CoV-2 infection. Blood samples and clinical data from 1286 COVID-19 patients coll...

Single-site multi-wavelength (MW) pulse transit time (PTT) measurement was recently proposed using contact sensors with sequential illumination. It leverages different penetration depths of light to measure the traversal of a cardiac pulse between skin layers. This enabled continuous single-site MW blood pressure (BP) monitoring, but faces challeng...

Sensor-based human activity recognition is becoming ever more prevalent. The increasing importance of distinguishing human movements, particularly in healthcare, coincides with the advent of increasingly compact sensors. A complex sequence of individual steps currently characterizes the activity recognition pipeline. It involves separate data colle...

Accurate and comprehensive nursing documentation is essential to ensure quality patient care. To streamline this process, we present SONAR, a publicly available dataset of nursing activities recorded using inertial sensors in a nursing home. The dataset includes 14 sensor streams, such as acceleration and angular velocity, and 23 activities recorde...

Existing literature indicates that academic staff experience increasing levels of work stress. This study investigated associations between day‐to‐day threat and challenge appraisal and day‐to‐day problem‐focused coping, emotion‐focused coping, and seeking social support among academic office workers. This study is based on an Ecological Momentary...

Contact-free sensing gained much traction in the past decade. While remote monitoring of some parameters (heart rate) is approaching clinical levels of precision, others remain challenging (blood pressure). We investigated the feasibility of estimating blood pressure (BP) via pulse transit time (PTT) in a novel remote single-site manner, using a mo...

Objectives This study aimed to investigate the associations between day-to-day work-related stress exposures (i.e., job demands and lack of job control), job strain, and next-day work engagement among office workers in academic settings. Additionally, we assessed the influence of psychological detachment and relaxation on next-day work engagement a...

Understanding the growth pattern is important in view of child and adolescent development. Due to different tempo of growth and timing of adolescent growth spurt, individuals reach their adult height at different ages. Accurate models to assess the growth involve intrusive radiological methods whereas the predictive models based solely on height da...

One key task in the early fight against the COVID-19 pandemic was to plan non-pharmaceutical interventions to reduce the spread of the infection while limiting the burden on the society and economy. With more data on the pandemic being generated, it became possible to model both the infection trends and intervention costs, transforming the creation...

One major challenge during the COVID-19 pandemic was the limited accessibility to healthcare facilities, especially for the older population. The aim of the current study was the exploration of the extent to which the healthcare systems responded to the healthcare needs of the older people with or without cognitive impairment and their caregivers i...

Introduction: The preferences of people with profound intellectual and multiple disabilities (PIMD) often remain unfulfilled since it stays challenging to decode their idiosyncratic behaviour resulting in a negative impact on their quality of life (QoL). Physiological data (i.e., heart rate (variability) and motion data) might be the missing piece...

Virtual reality (VR) technology is often characterized as "the ultimate empathy machine" as it enables users to experience how it is to be someone else or be somewhere other than where they are in the real physical world. Here, we conducted a narrative review of studies focused on using VR to elicit empathy. Considering the synthesized literature,...

Congestive heart failure (CHF) is an incurable disease where a key objective of the treatment is to maintain the patient's quality of life (QoL) as much as possible. A model that predicts health-related QoL (HRQoL) based on physiological and ambient parameters can be used to modify these parameters for the patient's benefit. Since it is difficult t...

As weather conditions become more complex and unpredictable as a consequence of global warming and air pollution, humans find it increasingly difficult to predict the amount of precipitation in the coming period, thus predicting the inflow into hydroelectric basins. Different types of hydropower plants (HPP), soil composition , how dry the soil is...

Fondazione Bruno Kessler is developing a mobile app prototype for empowering citizens to improve their health conditions through different lifestyle interventions that will be incorporated into a mobile application for lifestyle promotion of the Province of Trento in the context of the Trentino Salute 4.0 Competence Center. The envisioned intervent...

Purpose We investigated relations between day-to-day job demands, job control, job strain, social support at work, and day-to-day work–life interference among office workers in academia. Methods This study is based on a 15-working day data collection period using an Ecological Momentary Assessment (EMA) implemented in our self-developed STRAW smar...

Non-pharmaceutical interventions against COVID-19 and other infectious diseases seek good trade-offs between reducing the number of infections and their socioeconomic costs. We propose a framework that establishes these costs from data on interventions implemented in real life for each country taking into consideration its culture and economy. The...

From 2018 to 2021, the Sussex-Huawei Locomotion-Transportation Recognition Challenge presented different scenarios in which participants were tasked with recognizing eight different modes of locomotion and transportation using sensor data from smartphones. In 2019, the main challenge was using sensor data from one location to recognize activities w...

Background While chronic workplace stress is known to be associated with health-related outcomes like mental and cardiovascular diseases, research about day-to-day occupational stress is limited. This systematic review includes studies assessing stress exposures as work environment risk factors and stress outcomes, measured via self-perceived quest...

Workplace stress remains a major interest of occupational health research, usually based on theoretical models and quantitative large-scale studies. Office workers are especially exposed to stressors such as high workload and time pressure. The aim of this qualitative research was to follow a phenomenological approach to identify work stressors as...

Congestive heart failure is a chronic medical condition that affects about 2 % of the adult population. Even though it cannot be cured, it can be relieved by a proper, long-term, complex and personalized disease management. In this paper we present the HeartMan Decision Support System (DSS), aimed at supporting individual patients in their uptake o...

One key task in the early fight against the COVID-19 pandemic was to plan non-pharmaceutical interventions to reduce the spread of the infection while limiting the burden on the society and economy. With more data on the pandemic being generated, it became possible to model both the infection trends and intervention costs, transforming the creation...

Covid-19 has so far affected every country in the world. The Non-Pharmaceutical Interventions (NPIs) by governments have proven themselves quite effective at stopping the spread of infections, but when applied in a very strict and long-lasting manner could have devastating consequences for the economic and social well-being of the population. XPRIZ...

You can find the slides on our website: https://www.insension.eu/conferences/

To further extend the applicability of wearable sensors, methods for accurately extracting subtle psychological information from the sensor data are required. However, accessing subjective information in everyday life, such as cognitive load, remains challenging. To bring consensus on methods for cognitive load monitoring, a machine learning challe...

The COVID-19 pandemic affected the whole world, but not all countries were impacted equally. This opens the question of what factors can explain the initial faster spread in some countries compared to others. Many such factors are overshadowed by the effect of the countermeasures, so we studied the early phases of the infection when countermeasures...

Chronic kidney disease (CKD) represents a heavy burden on the healthcare system because of the increasing number of patients, high risk of progression to end-stage renal disease, and poor prognosis of morbidity and mortality. The aim of this study is to develop a machine-learning model that uses the comorbidity and medication data obtained from Tai...

The coronavirus disease 2019 (COVID-19) pandemic has been as unprecedented as unexpected, affecting more than 105 million people worldwide as of February 8th, 2020 and causing more than 2.3 million deaths according the World Health Organization. Not only affecting the lungs and provoking acute respiratory distress, severe acute respiratory syndrome...

The coronavirus disease 2019 (COVID-19) pandemic has been as unprecedented as unexpected, affecting more than 105 million people worldwide as of February 8th, 2020 and causing more than 2.3 million deaths according the World Health Organization. Not only affecting the lungs and provoking acute respiratory distress, severe acute respiratory syndrome...

This study tested the effectiveness of HeartMan—a mobile personal health system offering decisional support for management of congestive heart failure (CHF)—on health-related quality of life (HRQoL), self-management, exercise capacity, illness perception, mental and sexual health. A randomized controlled proof-of-concept trial (1:2 ratio of control...

Understanding people’s eating habits plays a crucial role in interventions promoting a healthy lifestyle. This requires objective measurement of the time at which a meal takes place, the duration of the meal, and what the individual eats. Smartwatches and similar wrist-worn devices are an emerging technology that offers the possibility of practical...

Contact-free sensors offer important advantages compared to traditional wearables. Radio-frequency sensors (e.g., radars) offer the means to monitor cardiorespiratory activity of people without compromising their privacy, however, only limited information can be obtained via movement, traditionally related to heart or breathing rate. We investigate...

Finding the best classifiers according to different criteria is often performed by a multi-objective machine learning algorithm. This study considers two criteria that are usually treated as the most important when deciding which classifier to apply in practice: comprehensibility and accuracy. A model that offers a broad range of trade-offs between...

Context recognition using wearable devices is a mature research area, but one of the biggest issues it faces is the high energy consumption of the device that is sensing and processing the data. In this work we propose three different methods for optimizing its energy use. We also show how to combine all three methods to further increase the energy...

The Cooking Activity Recognition Challenge tasked the competitors with recognizing food preparation using motion capture and acceleration sensors. This paper summarizes our submission to this competition, describing how we reordered the training data, relabeled it and how we handcrafted features for this dataset. Our classification pipeline first d...

Food frequency questionnaires (FFQs) are the most commonly selected tools in nutrition monitoring, as they are inexpensive, easily implemented and provide useful information regarding dietary intake. They are usually carefully drafted by experts from nutritional and/or medical fields and can be validated by using other dietary monitoring techniques...

Several studies have reported on increasing psychosocial stress in academia due to work environment risk factors like job insecurity, work-family conflict, research grant applications, and high workload. The STRAW project adds novel aspects to occupational stress research among academic staff by measuring day-to-day stress in their real-world work...

Context recognition (CR) systems infer the user’s context, such as their physical activity, from sensor data obtained, for example, with smartphone sensors. Designing an energy-efficient CR system, however, is a complex optimization problem involving conflicting objectives and several constraints arising from real-world limitations and designers’ p...

The WellCo project aims to provide a mobile application featuring a virtual coach for behaviour changes aiming to achieve for healthier lifestyle. The nutrition monitoring module consists of two main parts-qualitative (Food Frequency Questionnaire) and quantitative (eating detection and bite counting). In this paper we present the nutrition monitor...

Background Congestive heart failure (CHF) is a disease that requires complex management involving multiple medications, exercise, and lifestyle changes. It mainly affects older patients with depression and anxiety, who commonly find management difficult. Existing mobile apps supporting the self-management of CHF have limited features and are inadeq...

BACKGROUND Congestive heart failure (CHF) is a disease that requires complex management involving multiple medications, exercise, and lifestyle changes. It mainly affects older patients with depression and anxiety, who commonly find management difficult. Existing mobile apps supporting the self-management of CHF have limited features and are inadeq...

Human Energy Expenditure (EE) is a valuable tool for measuring physical activity and its impact on our body in an objective way. To accurately measure the EE, there are methods such as doubly labeled water and direct and indirect calorimetry, but their cost and practical limitations make them suitable only for research and professional sports. This...

Background and Objective: Chronic kidney disease (CKD) represent a heavy burden on the healthcare system because of the increasing number of patients, high risk of progression to end-stage renal disease, and poor prognosis of morbidity and mortality. The aim of this study is to develop a machine-learning model that uses the comorbidity and medicati...

Starting renal replacement therapy (RRT) for patients with chronic kidney disease (CKD) at an optimal time, either with hemodialysis or kidney transplantation, is crucial for patient’s well-being and for successful management of the condition. In this paper, we explore the possibilities of creating forecasting models to predict the onset of RRT 3,...

This study introduces two datasets for multimodal research on cognitive load inference and personality traits. Different to other datasets in Affective Computing, which disregard participants’ personality traits or focus only on emotions, stress, or cognitive load from one specific task, the participants in our experiments performed seven different...

From not disturbing a focused programmer to entertaining a restless commuter waiting for a train, personal ubiquitous computing devices could greatly enhance their interaction with humans, should these devices only be aware of their users’ cognitive engagement. Despite impressive advances in the inference of human movement, physical activity, routi...

It is only a matter of time until autonomous vehicles become ubiquitous; however, human driving supervision will remain a necessity for decades. To assess the driver’s ability to take control over the vehicle in critical scenarios, driver distractions can be monitored using wearable sensors or sensors that are embedded in the vehicle, such as video...

The Sussex-Huawei Locomotion-Transportation Recognition Challenge presented a unique opportunity to the activity-recognition community to test their approaches on a large, real-life benchmark dataset with activities different from those typically recognized. The goal of the challenge was to recognize, as accurately as possible, eight locomotion act...

Human falls are common source of injury among the elderly, because often the elderly person is injured and cannot call for help. In the literature this is addressed by various fall-detection systems, of which most common are the ones that use wearable sensors. This paper describes the winning method developed for the Challenge Up: Multimodal Fall D...

Patient-reported outcomes (PROs) have been previously considered “soft” end-points because of the lack of association of the reported outcome to measurable biological parameters. The present study aimed to assess whether electrocardiographic measures are associated to PROs changes. We evaluated the association between heart rate (HR), QRS and QT/QT...

Advances in ambient intelligence technologies achieved over recent years allow building ICT systems capable of supporting people in executing previously difficult tasks. This includes providing new opportunities to people with special needs, such as people with disabilities. In this paper we discuss our approach at designing and developing a smart...