Abdullah Al Mamun

Parker Seegmiller, Joseph Gatto, Abdullah Mamun et al.

Prescription medications often impose temporal constraints on regular health behaviors (RHBs) of patients, e.g., eating before taking medication. Violations of such medical temporal constraints (MTCs) can result in adverse effects. Detecting and predicting such violations before they occur can help alert the patient. We formulate the problem of modeling MTCs and develop a proof-of-concept solution, ActSafe, to predict violations of MTCs well ahead of time. ActSafe utilizes a context-free grammar based approach for extracting and mapping MTCs from patient education materials. It also addresses the challenges of accurately predicting RHBs central to MTCs (e.g., medication intake). Our novel behavior prediction model, HERBERT , utilizes a basis vectorization of time series that is generalizable across temporal scale and duration of behaviors, explicitly capturing the dependency between temporally collocated behaviors. Based on evaluation using a real-world RHB dataset collected from 28 patients in uncontrolled environments, HERBERT outperforms baseline models with an average of 51% reduction in root mean square error. Based on an evaluation involving patients with chronic conditions, ActSafe can predict MTC violations a day ahead of time with an average F1 score of 0.86.

Abdullah Mamun, Lawrence D. Devoe, Mark I. Evans et al.

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 decision-making. To fill this gap, we propose "Artificial Intelligence (AI) for Modeling and Explaining Neonatal Health" (AIMEN), a deep learning framework that not only predicts adverse labor outcomes from maternal, fetal, obstetrical, and intrapartum risk factors but also provides the model's reasoning behind the predictions made. The latter can provide insights into what modifications in the input variables of the model could have changed the predicted outcome. We address the challenges of imbalance and small datasets by synthesizing additional training data using Adaptive Synthetic Sampling (ADASYN) and Conditional Tabular Generative Adversarial Networks (CTGAN). AIMEN uses an ensemble of fully-connected neural networks as the backbone for its classification with the data augmentation supported by either ADASYN or CTGAN. AIMEN, supported by CTGAN, outperforms AIMEN supported by ADASYN in classification. AIMEN can predict a high risk for adverse labor outcomes with an average F1 score of 0.784. It also provides counterfactual explanations that can be achieved by changing 2 to 3 attributes on average. Resources available: https://github.com/ab9mamun/AIMEN.

Md Tasnin Tanvir, Dr Santanu Kumar Dash, Ishan Shahnan et al.

The challenge of separating AI-generated text from human-authored content is becoming more urgent as generative AI technologies like ChatGPT become more widely available. In this work, we address this issue by looking at both the detection of content that has been entirely generated by AI and the identification of human text that has been reworded by AI. In our work, a comprehensive methodology to detect AI- generated text using XLM-RoBERTa, a state-of-the-art multilingual transformer model. Our approach includes rigorous preprocessing, and feature extraction involving perplexity, semantic, and readability features. We fine-tuned the XLM-RoBERTa model on a balanced dataset of human and AI-generated texts and evaluated its performance. The model demonstrated high accuracy and robust performance across various text genres. Additionally, we conducted feature analysis to understand the model's decision-making process, revealing that perplexity and attention-based features are critical in differentiating between human and AI-generated texts. Our findings offer a valuable tool for maintaining academic integrity and contribute to the broader field of AI ethics by promoting transparency and accountability in AI systems. Future research directions include exploring other advanced models and expanding the dataset to enhance the model's generalizability.

Shovito Barua Soumma, S M Raihanul Alam, Rudmila Rahman et al.

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 environments to enable timely interventions. However, existing detection methods face challenges due to (1) intra- and inter-patient variability, (2) subject-specific training, (3) using multiple sensors in FOG dominant locations (e.g., ankles) leading to high failure points, (4) centralized, non-adaptive learning frameworks that sacrifice patient privacy and prevent collaborative model refinement across populations and disease progression, and (5) most systems are tested in controlled settings, limiting their real-world applicability for continuous in-home monitoring. Addressing these gaps, we present FOGSense, a real-world deployable FOG detection system designed for uncontrolled, free-living conditions using only a single sensor. FOGSense uses Gramian Angular Field (GAF) transformations and privacy-preserving federated deep learning to capture temporal and spatial gait patterns missed by traditional methods with a low false positive rate. We evaluated our system using a public Parkinson's dataset collected in a free-living environment. FOGSense improves accuracy by 10.4% over a single-axis accelerometer, reduces failure points compared to multi-sensor systems, and demonstrates robustness to missing values. The federated architecture allows personalized model adaptation and efficient smartphone synchronization during off-peak hours, making it effective for long-term monitoring as symptoms evolve. Overall, FOGSense achieved a 22.2% improvement in F1-score and a 74.53% reduction in false positive rate compared to state-of-the-art methods, along with enhanced sensitivity for FOG episode detection.

Abdullah Mamun, Krista S. Leonard, Megan E. Petrov et al.

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 conducted two clinical studies involving 58 prediabetic veterans and 60 patients with obstructive sleep apnea to gather multimodal behavioral data using wearable devices. We develop multimodal long short-term memory (LSTM) network models, which are capable of forecasting the number of step counts of a patient up to 24 hours in advance by examining data from activity and engagement modalities. Furthermore, we design goal-based forecasting models to predict whether a person's next-day steps will be over a certain threshold. Results: Multimodal LSTM with early fusion achieves 33% and 37% lower mean absolute errors than linear regression and ARIMA respectively on the prediabetes dataset. LSTM also outperforms linear regression and ARIMA with a margin of 13% and 32% on the sleep dataset. Multimodal forecasting models also perform with 72% and 79% accuracy on the prediabetes dataset and sleep dataset respectively on goal-based forecasting. Conclusion: Our experiments conclude that multimodal LSTM models with early fusion are better than multimodal LSTM with late fusion and unimodal LSTM models and also than ARIMA and linear regression models. Significance: We address an important and challenging task of time-series forecasting in uncontrolled environments. Effective forecasting of a person's physical activity can aid in designing adaptive behavioral interventions to keep the user engaged and adherent to a prescribed routine.

Syeda Mariam Ahmed, Yan Zhi Tan, Chee Meng Chew et al.

In this paper, we propose novel edge and corner detection algorithms for unorganized point clouds. Our edge detection method evaluates symmetry in a local neighborhood and uses an adaptive density based threshold to differentiate 3D edge points. We extend this algorithm to propose a novel corner detector that clusters curvature vectors and uses their geometrical statistics to classify a point as corner. We perform rigorous evaluation of the algorithms on RGB-D semantic segmentation and 3D washer models from the ShapeNet dataset and report higher precision and recall scores. Finally, we also demonstrate how our edge and corner detectors can be used as a novel approach towards automatic weld seam detection for robotic welding. We propose to generate weld seams directly from a point cloud as opposed to using 3D models for offline planning of welding paths. For this application, we show a comparison between Harris 3D and our proposed approach on a panel workpiece.