Alireza Rahimi

The transportation phase in off-site construction subjects prefabricated modules to road-induced vibrations, shocks, and handling loads that can degrade structural integrity. Existing monitoring approaches often rely on local data loggers, which not only lack real-time visibility but also fail to link structural responses to their precise spatial and temporal context. To address this gap, this study proposes a GPS-integrated Internet of Things (IoT) framework for real-time monitoring of prefabricated modules during transit. The system comprises distributed inertial sensing nodes wirelessly connected to a central gateway, which aggregates and transmits synchronized sensor and GPS data to a cloud platform for analysis and visualization. Field validation demonstrated stable multi-node data acquisition with sufficient battery life to support extended monitoring under LTE connectivity. The framework supports dual-stream analytics: (i) time- and frequency-domain assessment of structural exposure using peak acceleration, RMS, and FFT metrics, and (ii) causal inference of road events (e.g., potholes, bumps, sharp turns). Vertical acceleration emerged as the most responsive diagnostic channel for capturing road-induced excitations, while gyroscope-derived motion profiles distinguish between driver maneuvers and road irregularities. Through seamless integration of structural and geospatial data in a scalable, low-cost system, this framework enables actionable insights for route planning, condition-based inspection, and improved logistics management in modular construction.

The aging population faces increased health risks, with falls being a major concern for individuals over 65, leading to healthcare strain and distress. We propose a semi-supervised federated learning-based fall detection (SF2D) method that leverages edge devices to maintain user privacy while ensuring accurate detection. Our approach first trains an unsupervised autoencoder with federated learning, then uses its encoder to train a cloud-based classifier with benchmark datasets. Our proposed SF2D improves accuracy by 1% and recall by 4% over state-of-the-art systems, offering a practical, accurate solution for fall detection and elderly care.

Psychiatric disorders affect millions globally, yet their diagnosis faces significant challenges in clinical practice due to subjective assessments and accessibility concerns, leading to potential delays in treatment. To help address this issue, we present Heart2Mind, a human-centered contestable psychiatric disorder diagnosis system using wearable electrocardiogram (ECG) monitors. Our approach leverages cardiac biomarkers, particularly heart rate variability (HRV) and R-R intervals (RRI) time series, as objective indicators of autonomic dysfunction in psychiatric conditions. The system comprises three key components: (1) a Cardiac Monitoring Interface (CMI) for real-time data acquisition from Polar H9/H10 devices; (2) a Multi-Scale Temporal-Frequency Transformer (MSTFT) that processes RRI time series through integrated time-frequency domain analysis; (3) a Contestable Diagnosis Interface (CDI) combining Self-Adversarial Explanations (SAEs) with contestable Large Language Models (LLMs). Our MSTFT achieves 91.7% accuracy on the HRV-ACC dataset using leave-one-out cross-validation, outperforming state-of-the-art methods. SAEs successfully detect inconsistencies in model predictions by comparing attention-based and gradient-based explanations, while LLMs enable clinicians to validate correct predictions and contest erroneous ones. This work demonstrates the feasibility of combining wearable technology with Explainable Artificial Intelligence (XAI) and contestable LLMs to create a transparent, contestable system for psychiatric diagnosis that maintains clinical oversight while leveraging advanced AI capabilities. Our implementation is publicly available at: https://github.com/Analytics-Everywhere-Lab/heart2mind.