Hassan Ghasemzadeh
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View article: AI-Powered Wearable Sensors for Health Monitoring and Clinical Decision Making
AI-Powered Wearable Sensors for Health Monitoring and Clinical Decision Making Open
AI-powered wearable sensors are transforming remote health monitoring by enabling real-time diagnostics, personalized interventions and proactive disease management. This review synthesizes recent advances in AI-integrated biosensors acros…
View article: CLAD-Net: Continual Activity Recognition in Multi-Sensor Wearable Systems
CLAD-Net: Continual Activity Recognition in Multi-Sensor Wearable Systems Open
The rise of deep learning has greatly advanced human behavior monitoring using wearable sensors, particularly human activity recognition (HAR). While deep models have been widely studied, most assume stationary data distributions - an assu…
View article: LLM-Powered Prediction of Hyperglycemia and Discovery of Behavioral Treatment Pathways from Wearables and Diet
LLM-Powered Prediction of Hyperglycemia and Discovery of Behavioral Treatment Pathways from Wearables and Diet Open
Postprandial hyperglycemia, marked by the blood glucose level exceeding the normal range after consuming a meal, is a critical indicator of progression toward type 2 diabetes in people with prediabetes and in healthy individuals. A key met…
View article: AI-Powered Wearable Sensors for Health Monitoring and Clinical Decision Making
AI-Powered Wearable Sensors for Health Monitoring and Clinical Decision Making Open
AI-powered wearable sensors represent a transformative frontier in remote health monitoring, offering unprecedented opportunities for real-time diagnostics, personalized interventions, and proactive disease management. This review synthesi…
View article: Trustworthy AI in Digital Health: A Comprehensive Review of Robustness and Explainability
Trustworthy AI in Digital Health: A Comprehensive Review of Robustness and Explainability Open
Ensuring trust in AI systems is essential for the safe and ethical integration of machine learning systems into high-stakes domains such as digital health. Key dimensions, including robustness, explainability, fairness, accountability, and…
View article: SenseCF: LLM-Prompted Counterfactuals for Intervention and Sensor Data Augmentation
SenseCF: LLM-Prompted Counterfactuals for Intervention and Sensor Data Augmentation Open
Counterfactual explanations (CFs) offer human-centric insights into machine learning predictions by highlighting minimal changes required to alter an outcome. Therefore, CFs can be used as (i) interventions for abnormality prevention and (…
View article: Detection and Severity Assessment of Parkinson’s Disease Through Analyzing Wearable Sensor Data Using Gramian Angular Fields and Deep Convolutional Neural Networks
Detection and Severity Assessment of Parkinson’s Disease Through Analyzing Wearable Sensor Data Using Gramian Angular Fields and Deep Convolutional Neural Networks Open
Parkinson’s disease (PD) is the second-most common neurodegenerative disease. With more than 20,000 new diagnosed cases each year, PD affects millions of individuals worldwide and is most prevalent in the elderly population. The current cl…
View article: AZT1D: A Real-World Dataset for Type 1 Diabetes
AZT1D: A Real-World Dataset for Type 1 Diabetes Open
High-quality real-world datasets are essential for advancing data-driven approaches in type 1 diabetes (T1D) management, including personalized therapy design, digital twin systems, and glucose prediction models. However, progress in this …
View article: GlyTwin: Digital Twin for Glucose Control in Type 1 Diabetes Through Optimal Behavioral Modifications Using Patient-Centric Counterfactuals
GlyTwin: Digital Twin for Glucose Control in Type 1 Diabetes Through Optimal Behavioral Modifications Using Patient-Centric Counterfactuals Open
Frequent and long-term exposure to hyperglycemia (i.e., high blood glucose) increases the risk of chronic complications such as neuropathy, nephropathy, and cardiovascular disease. Current technologies like continuous subcutaneous insulin …
View article: Domain-Informed Label Fusion Surpasses LLMs in Free-Living Activity Classification (Student Abstract)
Domain-Informed Label Fusion Surpasses LLMs in Free-Living Activity Classification (Student Abstract) Open
FuSE-MET addresses critical challenges in deploying human activity recognition (HAR) systems in uncontrolled environments by effectively managing noisy labels, sparse data, and undefined activity vocabularies. By integrating BERT-based wor…
View article: Enhancing Metabolic Syndrome Prediction with Hybrid Data Balancing and Counterfactuals
Enhancing Metabolic Syndrome Prediction with Hybrid Data Balancing and Counterfactuals Open
Metabolic Syndrome (MetS) is a cluster of interrelated risk factors that significantly increases the risk of cardiovascular diseases and type 2 diabetes. Despite its global prevalence, accurate prediction of MetS remains challenging due to…
View article: CAN-STRESS: A Real-World Multimodal Dataset for Understanding Cannabis Use, Stress, and Physiological Responses
CAN-STRESS: A Real-World Multimodal Dataset for Understanding Cannabis Use, Stress, and Physiological Responses Open
Coping with stress is one of the most frequently cited reasons for chronic cannabis use. Therefore, it is hypothesized that cannabis users exhibit distinct physiological stress responses compared to non-users, and these differences would b…
View article: AIMI: Leveraging Future Knowledge and Personalization in Sparse Event Forecasting for Treatment Adherence
AIMI: Leveraging Future Knowledge and Personalization in Sparse Event Forecasting for Treatment Adherence Open
Adherence to prescribed treatments is crucial for individuals with chronic conditions to avoid costly or adverse health outcomes. For certain patient groups, intensive lifestyle interventions are vital for enhancing medication adherence. A…
View article: Identifying Common Patterns in the Time of Day of Mindfulness Meditation Associated with Long-Term Maintenance
Identifying Common Patterns in the Time of Day of Mindfulness Meditation Associated with Long-Term Maintenance Open
Forming a habit of practicing mindfulness meditation around the same time of day is one strategy that may support long-term maintenance and in turn improve physical and mental health. The purpose of this study was to identify common patter…
View article: Cost-Effective Multitask Active Learning in Wearable Sensor Systems
Cost-Effective Multitask Active Learning in Wearable Sensor Systems Open
Multitask learning models provide benefits by reducing model complexity and improving accuracy by concurrently learning multiple tasks with shared representations. Leveraging inductive knowledge transfer, these models mitigate the risk of …
View article: Type 1 Diabetes Management using GLIMMER: Glucose Level Indicator Model with Modified Error Rate
Type 1 Diabetes Management using GLIMMER: Glucose Level Indicator Model with Modified Error Rate Open
Managing Type 1 Diabetes (T1D) demands constant vigilance as individuals strive to regulate their blood glucose levels to avoid the harmful effects of dysglycemia, including both hyperglycemia and hypoglycemia. Despite the development of a…
View article: AttenGluco: Multimodal Transformer-Based Blood Glucose Forecasting on AI-READI Dataset
AttenGluco: Multimodal Transformer-Based Blood Glucose Forecasting on AI-READI Dataset Open
Diabetes is a chronic metabolic disorder characterized by persistently high blood glucose levels (BGLs), leading to severe complications such as cardiovascular disease, neuropathy, and retinopathy. Predicting BGLs enables patients to maint…
View article: Freezing of Gait Detection Using Gramian Angular Fields and Federated Learning from Wearable Sensors
Freezing of Gait Detection Using Gramian Angular Fields and Federated Learning from Wearable Sensors Open
Freezing of gait (FOG) is a debilitating symptom of Parkinson's disease that impairs mobility and safety by increasing the risk of falls. An effective FOG detection system must be accurate, real-time, and deployable in free-living environm…
View article: Hybrid Attention Model Using Feature Decomposition and Knowledge Distillation for Glucose Forecasting
Hybrid Attention Model Using Feature Decomposition and Knowledge Distillation for Glucose Forecasting Open
The availability of continuous glucose monitors as over-the-counter commodities have created a unique opportunity to monitor a person's blood glucose levels, forecast blood glucose trajectories and provide automated interventions to preven…
View article: Self-Supervised Learning and Opportunistic Inference for Continuous Monitoring of Freezing of Gait in Parkinson's Disease
Self-Supervised Learning and Opportunistic Inference for Continuous Monitoring of Freezing of Gait in Parkinson's Disease Open
Parkinson's disease (PD) is a progressive neurological disorder that impacts the quality of life significantly, making in-home monitoring of motor symptoms such as Freezing of Gait (FoG) critical. However, existing symptom monitoring techn…
View article: Multimodal Physical Activity Forecasting in Free-Living Clinical Settings: Hunting Opportunities for Just-in-Time Interventions
Multimodal Physical Activity Forecasting in Free-Living Clinical Settings: Hunting Opportunities for Just-in-Time Interventions Open
Objective: This research aims to develop a lifestyle intervention system, called MoveSense, that forecasts a patient's activity behavior to allow for early and personalized interventions in real-world clinical environments. Methods: We con…
View article: Use of What-if Scenarios to Help Explain Artificial Intelligence Models for Neonatal Health
Use of What-if Scenarios to Help Explain Artificial Intelligence Models for Neonatal Health Open
Early detection of intrapartum risk enables interventions to potentially prevent or mitigate adverse labor outcomes such as cerebral palsy. Currently, there is no accurate automated system to predict such events to assist with clinical dec…
View article: Wearable-Based Real-time Freezing of Gait Detection in Parkinson's Disease Using Self-Supervised Learning
Wearable-Based Real-time Freezing of Gait Detection in Parkinson's Disease Using Self-Supervised Learning Open
LIFT-PD is an innovative self-supervised learning framework developed for real-time detection of Freezing of Gait (FoG) in Parkinson's Disease (PD) patients, using a single triaxial accelerometer. It minimizes the reliance on large labeled…
View article: CUDLE: Learning Under Label Scarcity to Detect Cannabis Use in Uncontrolled Environments
CUDLE: Learning Under Label Scarcity to Detect Cannabis Use in Uncontrolled Environments Open
Wearable sensor systems have demonstrated a great potential for real-time, objective monitoring of physiological health to support behavioral interventions. However, obtaining accurate labels in free-living environments remains difficult d…
View article: Adversarial Transferability in Embedded Sensor Systems: An Activity Recognition Perspective
Adversarial Transferability in Embedded Sensor Systems: An Activity Recognition Perspective Open
Machine learning algorithms are increasingly used for inference and decision-making in embedded systems. Data from sensors are used to train machine learning models for various smart functions of embedded and cyber-physical systems ranging…
View article: Minimum-Cost Sensor Channel Selection For Wearable Computing
Minimum-Cost Sensor Channel Selection For Wearable Computing Open
Sensor systems are constrained by design and finding top sensor channel(s) for a given computational task is an important but hard problem. We define an optimization framework and mathematically formulate the minimum-cost channel selection…
View article: Inter-Beat Interval Estimation with Tiramisu Model: A Novel Approach with Reduced Error
Inter-Beat Interval Estimation with Tiramisu Model: A Novel Approach with Reduced Error Open
Inter-beat interval (IBI) measurement enables estimation of heart-tare variability (HRV) which, in turn, can provide early indication of potential cardiovascular diseases (CVDs). However, extracting IBIs from noisy signals is challenging s…
View article: Designing User-Centric Behavioral Interventions to Prevent Dysglycemia with Novel Counterfactual Explanations
Designing User-Centric Behavioral Interventions to Prevent Dysglycemia with Novel Counterfactual Explanations Open
Monitoring unexpected health events and taking actionable measures to avert them beforehand is central to maintaining health and preventing disease. Therefore, a tool capable of predicting adverse health events and offering users actionabl…
View article: Table of Contents
Table of Contents Open
View article: Pandemic Preparedness With Pervasive Computing
Pandemic Preparedness With Pervasive Computing Open
The COVID-19 pandemic has stimulated pervasive computing research and development resulting in new, impactful public health tools, including digital contact tracing, crowd dynamics analysis, and symptom tracking, which are broadly recogniz…