Martin Gjoreski

Univesita della svizzera italiana

Background Continuous assessment of affective behaviors could improve the diagnosis, assessment and monitoring of chronic mental health and neurological conditions such as depression. However, there are no technologies well suited to this, limiting potential clinical applications. Aim To test if we could replicate previous evidence of hypo reactiv...

The estimation of human mobility patterns is essential for many components of developed societies, including the planning and management of urbanization, pollution, and disease spread. One important type of mobility estimator is the next-place predictors, which use previous mobility observations to anticipate an individual’s subsequent location. So...

Continuous and automatic monitoring of an individual's physical activity using wearable devices provides valuable insights into their daily habits and patterns. This information can be used to promote healthier lifestyles, prevent chronic diseases, and improve overall well-being. Smart glasses are an emerging technology that can be worn comfortably...

This study aimed to evaluate the use of novel optomyography (OMG) based smart glasses, OCOsense™, for the monitoring and recognition of facial expressions. Experiments were conducted on data gathered from 27 young adult participants, who performed facial expressions varying in intensity, duration, and head movement. The facial expressions included...

Human mobility modeling is crucial for many facets of our society, including disease transmission modeling and urban planning. The explosion of mobility data prompted the application of deep learning to human mobility. Along with the growth of research interest, there is also increasing privacy concern. This study first examines the cutting-edge ap...

Assessing pain levels in real-world conditions, such as during active surgery, can be challenging. Self-reports, often considered globally as ‘ground-truth’ can be unreliable, episodic and ill-suited to routine monitoring or use with non-verbal patients. Lately, physiological measurements have been explored as an objective method for assessing the...

Parkinson’s disease (PD) is one of the most common neurodegenerative disorders of the central nervous system, which predominantly affects patients’ motor functions, movement, and stability. Monitoring movement in patients with PD is crucial for inferring motor state fluctuations throughout daily life activities, which aids in disease progression an...

Sensor-based sleep monitoring systems can be used to track sleep behavior on a daily basis and provide feedback to their users to promote health and well-being. Such systems can provide data visualizations to enable self-reflection on sleep habits or a sleep coaching service to improve sleep quality. To provide useful feedback, sleep monitoring sys...

Using a novel wearable surface electromyography (sEMG), we investigated induced affective states by measuring the activation of facial muscles traditionally associated with positive (left/right orbicularis and left/right zygomaticus) and negative expressions (the corrugator muscle). In a sample of 38 participants that watched 25 affective videos in...

Generating counterfactuals to discover hypothetical predictive scenarios is the de facto standard for explaining machine learning models and their predictions. However, building a counterfactual explainer that is time-efficient, scalable, and model-agnostic, in addition to being compatible with continuous and categorical attributes, remains an open...

Location-based Behavioral Analytics (LBA) holds a great potential for improving the services available in smart cities. Naively implemented, such an approach would track the movements of every citizen and share their location traces with the various smart service providers—similar to today's Web analytics systems that track visitors across the web...

Multi-attribute decision analysis is an approach to decision support in which decision alternatives are evaluated by multi-criteria models. An advanced feature of decision support models is the possibility to search for new alternatives that satisfy certain conditions. This task is important for practical decision support; however, the related work...

Human mobility modeling is a complex yet essential subject of study related to modeling important spatiotemporal events, including traffic, disease spreading, and customized directions and recommendations. While spatiotemporal data can be collected easily via smartphones, current state-of-the-art deep learning methods require vast amounts of such p...

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...

Assessing pain levels in real-world conditions, such as during active surgery, can be challenging. Self-reports, often considered globally as 'ground-truth' can be unreliable, episodic and ill-suited to routine monitoring or use with non-verbal patients. Lately, physiological measurements have been explored as an objective method for assessing the...

Breathing rate is considered one of the fundamental vital signs and a highly informative indicator of physiological state. Given that the monitoring of heart activity is less complex than the monitoring of breathing, a variety of algorithms have been developed to estimate breathing activity from heart activity. However, estimating breathing rate fr...

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...

Artificial intelligence (AI) and its sister ambient intelligence (AmI) have in recent years become one of the main contributors to the progress of digital society and human civilization [...]

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...

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...

This paper describes the machine learning (ML) method Head-AR, which achieved the highest performance in a competition with 11 other algorithms and won the Emteq Activity Recognition challenge. The goal of the challenge was to recognize eight activities of daily life from a device mounted on the head, which provided data from a 3-axis IMU: accelero...

To further extend the applicability of wearable sensors in various domains such as mobile health systems and the automotive industry, new methods for accurately extracting subtle physiological information from these wearable sensors are required. However, the extraction of valuable information from physiological signals is still challenging-smartph...

Commercially available smartphones, smart glasses, smartwatches, and smart rings are just a few examples of sensor-packed devices that are enabling the technological revolution currently underway. To further extend the successful applicability of wearable devices in sectors such as mobile health, methods for accurate measurements of psycho-physiolo...

One of the main tasks of a course is that it should motivate students to work continuously during the semester. Using the same course curriculum for students with different cultural backgrounds can have different impacts on student behavior and motivation. In this paper is conducted a case study at Technical University of Kosice and University of L...

This paper describes the design for a personal mental health virtual assistant with novel ambient intelligence integration-PerMEASS. It is specifically designed to provide help for three mental health issues: stress, anxiety, and depression. Its assistance in these issues is based on two very closely related and trending multidisciplinary computer...

Recent wearable devices enable continuous and unobtrusive monitoring of human's physiological parameters, like e.g., electrodermal activity and heart rate, over long periods of time in everyday life settings. Continuous monitoring of these parameters enables the creation of systems able to predict affective states and stress with the goal of provid...

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...

Chronic heart failure (CHF) affects over 26 million of people worldwide, and its incidence is increasing by 2% annually. Despite the significant burden that CHF poses and despite the ubiquity of sensors in our lives, methods for automatically detecting CHF are surprisingly scarce, even in the research community. We present a method for CHF detectio...

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 being recognized. The goal of the challenge was to recognize eight locomotion activities (Still, Walk,...

The Sussex-Huawei Locomotion Challenge 2019 was an open competition in activity recognition where the participants were tasked with recognizing eight different modes of locomotion and transportation. The main difficulty of the challenge is that the training data was recorded with a smartphone that was placed in a different body location than the te...

Convolution Neural Network (CNN) filters learned on one domain can be used as feature extractors on another similar domain. Transferring filters allow reusing datasets across domains and reducing labelling costs. In this paper, four activity recognition datasets were analyzed to study the effects of transferring filters across the datasets. A spect...

Chronic heart failure (CHF) affects over 26 million of people worldwide and represents a significant societal, logistic and financial burden both for the patients and for the healthcare system, necessitating novel management approaches of this patient population. In this paper, we explore the possibilities of detecting heart failure worsening based...

In recent years, activity recognition (AR) has become prominent in ubiquitous systems. Following this trend, the Sussex-Huawei Locomotion-Transportation (SHL) recognition challenge provides a unique opportunity for researchers to test their AR methods against a common, real-life and large-scale benchmark. The goal of the challenge is to recognize e...

The Sussex-Huawei Locomotion-Transportation recognition challenge presents a unique opportunity to the activity-recognition community - providing a large, real-life dataset with activities different from those typically being recognized. This paper describes our submission (team JSI Classic) to the competition that was organized by the dataset auth...

To prevent undesirable effects of attention grabbing at times when a user is occupied with a difficult task, ubiquitous computing devices should be aware of the user's cognitive load. However, inferring cognitive load is extremely challenging, especially when performed without obtrusive, expensive, and purpose-built equipment. In this study we exam...

As activity recognition becomes an integral part of many mobile applications, its requirement for lightweight and accurate techniques leads to development of new tools and algorithms. This paper has three main contributions: (1) to design an architecture for automatic data collection, thus reducing the time and cost and making the process of develo...

Blood pressure is one of the most valuable vital signs. Recently, the use of bio-sensors has expanded, however, the blood pressure estimation still requires additional devices. We proposed a method based on complexity analysis and machine learning techniques for blood pressure estimation using only ECG signals. Using ECG recordings from 51 differen...

Background: Blood pressure (BP) measurements have been used widely in clinical and private environments. Recently, the use of ECG monitors has proliferated; however, they are not enabled with BP estimation. We have developed a method for BP estimation using only electrocardiogram (ECG) signals. Methods: Raw ECG data are filtered and segmented, a...

Arousal recognition from physiological signals is a task with many challenge remaining, especially when performed in several different domains. However, the need for emotional intelligent machines increases day by day, starting with timely detection and improved management of mental disorders in mobile health, all the way to enhancing user experien...

Modern lifestyle is largely sedentary and often stressful, giving rise to extensive research and development of solutions for the management of these two lifestyle aspects. Physical activity monitoring is a mature area of ubiquitous computing, with many devices and mobile applications available on the market. Mental stress monitoring is still a hot...

Affect recognition is an important task in ubiquitous computing, in particular in health and human-computer interaction. In the former, it contributes to the timely detection and treatment of emotional and mental disorders, and in the latter, it enables indigenous interaction and enhanced user experience. We present an inter-domain study for affect...

The recent advances in machine learning (ML) and sound processing have considerably expanded the possibilities provided by such techniques. The next step are open-access tools in existing ML toolkits for general sound ML tasks. We present JSI Sound, one of the first tools of this kind, developed within the Orange data mining software. The input for...

Being able to detect stress as it occurs can greatly contribute to dealing with its negative health and economic consequences. However, detecting stress in real life with an unobtrusive wrist device is a challenging task. The objective of this study is to develop a method for stress detection that can accurately, continuously and unobtrusively moni...

Continuous exposure to stress is harmful for mental and physical health, but to combat stress, one should first detect it. In this paper we propose a method for continuous detection of stressful events using data provided from a commercial wrist device. The method consists of three machine-learning components: a laboratory stress detector that dete...

Stress is a process triggered by a demanding physical and/or psychological event. It is not necessarily a negative process, but when present continuously, the stress process results in chronical stress. The chronical stress has negative health consequences such as raised blood pressure, bad sleep, increased vulnerability to infections, slower body...