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Parkinson's disease (PD) is one of the major public health problems in the world. It is a well-known fact that around one million people suffer from Parkinson's disease in the United States whereas the number of people suffering from…
Predicting the status of Major Depressive Disorder (MDD) from objective, non-invasive methods is an active research field. Yet, extracting automatically objective, interpretable features for a detailed analysis of the patient state remains…
Clinical in-bed video-based human motion analysis is a very relevant computer vision topic for several relevant biomedical applications. Nevertheless, the main public large datasets (e.g. ImageNet or 3DPW) used for deep learning approaches…
Atrial fibrillation (AF) is a leading cause of stroke and mortality, particularly in elderly patients. Wrist-worn photoplethysmography (PPG) enables non-invasive, continuous rhythm monitoring, yet suffers from significant vulnerability to…
The use of accurate and reliable open-source human activity recognition (HAR) models on passively collected wrist-accelerometer data is essential in large-scale epidemiological studies that investigate the association between physical…
Alzheimer's Disease (AD) is a progressive neurological disorder that can result in significant cognitive impairment and dementia. Accurate and timely diagnosis is essential for effective treatment and management of this disease. In this…
Detecting arousals in sleep is essential for diagnosing sleep disorders. However, using Machine Learning (ML) in clinical practice is impeded by fundamental issues, primarily due to mismatches between clinical protocols and ML methods.…
Given a road network and a set of trajectory data, the anomalous behavior detection (ABD) problem is to identify drivers that show significant directional deviations, hardbrakings, and accelerations in their trips. The ABD problem is…
Sensor-based human activity recognition has become a critical component of many emerging applications ranging from behavioral medicine to gaming. However, an unprecedented increase in the diversity of sensor devices in the…
Autism Spectrum Disorder (ASD) is a neurodevelopmental disorder characterized by deficits in social communication and behavioral patterns. Eye movement data offers a non-invasive diagnostic tool for ASD detection, as it is inherently…
Agitation is one of the most prevalent symptoms in people with dementia (PwD) that can place themselves and the caregiver's safety at risk. Developing objective agitation detection approaches is important to support health and safety of PwD…
Despite the revolutionary impact of AI and the development of locally trained algorithms, achieving widespread generalized learning from multi-modal data in medical AI remains a significant challenge. This gap hinders the practical…
Attention Deficit Hyperactivity Disorder (ADHD) and Autism Spectrum Disorder (ASD) are neurodevelopmental conditions which impact on a significant number of children and adults. Currently, the diagnosis of such disorders is done by experts…
Agitation is one of the most common responsive behaviors in people living with dementia, particularly among those residing in community settings without continuous clinical supervision. Timely prediction of agitation can enable early…
Resting-state functional Magnetic Resonance Imaging (R-fMRI) holds the promise to reveal functional biomarkers of neuropsychiatric disorders. However, extracting such biomarkers is challenging for complex multi-faceted neuropatholo-gies,…
Cognitive fatigue has been a common problem among workers which has become an increasing global problem since the emergence of COVID-19 as a global pandemic. While existing multi-modal wearable sensors-aided automatic cognitive fatigue…
Parkinson's disease (PD) is a neurodegenerative condition characterized by frequently changing motor symptoms, necessitating continuous symptom monitoring for more targeted treatment. Classical time series classification and deep learning…
Eye movements have been widely investigated to study the atypical visual attention in Autism Spectrum Disorder (ASD). The majority of these studies have been focused on limited eye movement features by statistical comparisons between ASD…
Time-series anomaly detection plays a vital role in monitoring complex operation conditions. However, the detection accuracy of existing approaches is heavily influenced by pattern distribution, existence of multiple normal patterns,…
Automated depression screening and diagnosis is a highly relevant problem today. There are a number of limitations of the traditional depression detection methods, namely, high dependence on clinicians and biased self-reporting. In recent…