Martin Gjoreski |

A foundation model for electrodermal activity data

Thu, 01 Jan 2026 00:00:00 +0000

ERIS: Enhancing Privacy and Communication Efficiency in Serverless Federated Learning

Thu, 01 Jan 2026 00:00:00 +0000

Federated Concept-Based Models: Interpretable models with distributed supervision

Thu, 01 Jan 2026 00:00:00 +0000

Federated Learning with Energy-Based Structured Probabilistic Inference

Thu, 01 Jan 2026 00:00:00 +0000

Federated Learning with Profile Mapping under Distribution Shifts and Drifts

Thu, 01 Jan 2026 00:00:00 +0000

Multi-modal physiological markers of arousal induced by CO2 inhalation in Virtual Reality

Thu, 01 Jan 2026 00:00:00 +0000

Platform and Study Design for Adaptive EMA Using Smart Glasses to Monitor Affective States

Thu, 01 Jan 2026 00:00:00 +0000

LIFETRACE

Tue, 02 Dec 2025 00:00:00 +0000

Digital technologies and wearable devices have emerged as critical tools for developing personalized well-being/health interventions, closely supported by psychological experience sampling instruments. While existing public datasets often focus on clinical populations and controlled environments, this paper presents a comprehensive dataset integrating anthropometric and health measurements, in-the-wild longitudinal physical activity monitoring, environmental context data, socio-demographic factors, and standardized psychological well-being metrics. By monitoring relevant behavioral and health data, this dataset investigates those relevant determinants to form long-term physical activity habits. Over a 14-week period, we collected data from 88 participants. Of these, 59 followed a daily walking protocol during the first 7 weeks, with the remaining 29 participants serving as a control group, with no physical activity required. This paper presents an exploratory data analysis (EDA) to test the reliability and completeness of the collected data, together with a baseline machine learning model to assess the predictive power of included variables, relative to the well-being metrics. Moreover, by using the feature importance method, a preliminary explainability approach is applied to evaluate the soundness of predictions regarding feature contributions. Several applications are envisioned, including the impact of environmental conditions on behavior, exploring the contribution of each observed variable to habit formation, and providing a benchmark for future machine learning models in this field.

Data from \"LIFETRACE: A Longitudinal Multimodal Dataset on Daily Physical Activity, Well-Being, and Habits\" (Version 1)

Mon, 01 Dec 2025 00:00:00 +0000

Data from \"LIFETRACE: A Longitudinal Multimodal Dataset on Daily Physical Activity, Well-Being, and Habits\" (Version 1)

Mon, 01 Dec 2025 00:00:00 +0000

A Review of Methods for Unobtrusive Measurement of Work-Related Well-Being

Wed, 01 Jan 2025 00:00:00 +0000

A Survey on Federated Learning in Human Sensing

Wed, 01 Jan 2025 00:00:00 +0000

Causal Concept Graph Models: Beyond Causal Opacity in Deep Learning

Wed, 01 Jan 2025 00:00:00 +0000

Counterfactual Concept Bottleneck Models

Wed, 01 Jan 2025 00:00:00 +0000

Deep Learning for Facial Expression and Human Activity Recognition Using Smart Glasses

Wed, 01 Jan 2025 00:00:00 +0000

Evaluating the Quality of Counterfactual Explanations in Multivariate Time-Series

Wed, 01 Jan 2025 00:00:00 +0000

FLUX: Efficient Descriptor-Driven Clustered Federated Learning under Arbitrary Distribution Shifts

Wed, 01 Jan 2025 00:00:00 +0000

Generating Explanations for Models Predicting Student Exam Performance

Wed, 01 Jan 2025 00:00:00 +0000

LIFETRACE: A Longitudinal Multimodal Dataset on Daily Physical Activity, Well-Being, and Habits

Wed, 01 Jan 2025 00:00:00 +0000

Multi-Frequency Federated Learning for Human Activity Recognition Using Head-Worn Sensors

Wed, 01 Jan 2025 00:00:00 +0000

Towards Transparent Reasoning: What Drives Faithfulness in Large Language Models?

Wed, 01 Jan 2025 00:00:00 +0000

XAI for U 2025: 2$nd$ International Workshop on Explainable AI for Ubiquitous, Pervasive and Wearable Computing

Wed, 01 Jan 2025 00:00:00 +0000

A Feature-Based Approach for Subtle Emotion Recognition in Realistic Scenarios