443 citations · 584 across the 33 of their papers we have counts for
36 papers
Real-Time Multimodal Activity-Aware Error Detection in Robot-Assisted Surgery
Seyed Hamid Reza Roodabeh, Zongyu Li, Homa Alemzadeh
Robot-assisted minimally invasive surgery improves surgical precision but introduces complexity, making technical error detection essential for ensuring patient safety. Current exe…
SAMD: A Tool for Identifying False Data Injection Scenarios in AI/ML-enabled Medical Devices
Mohammadreza Hallajiyan, Xueren Ge, Athish Pranav Dharmalingam +4
The growing integration of artificial intelligence (AI) and machine learning (ML) in medical systems requires effective measures to address emerging security risks. One such risk i…
EMSDialog: Synthetic Multi-person Emergency Medical Service Dialogue Generation from Electronic Patient Care Reports via Multi-LLM Agents
Xueren Ge, Sahil Murtaza, Anthony Cortez +1
Conversational diagnosis prediction requires models to track evolving evidence in streaming clinical conversations and decide when to commit to a diagnosis. Existing medical dialog…
Digital Guardians: The Past and The Future of Cyber-Physical Resilience
Saurabh Bagchi, Hyunseung Kim, Tarek Abdelzaher +20
Resilience in cyber-physical systems (CPS) is the fundamental ability to maintain safety and critical functionality despite adverse "perturbations," which includes security attacks…
A Comprehensive Analysis of the Effects of Network Quality of Service on Robotic Telesurgery
Zhaomeng Zhang, Seyed Hamid Reza Roodabeh, Homa Alemzadeh
The viability of long-distance telesurgery hinges on reliable network Quality of Service (QoS), yet the impact of realistic network degradations on task performance is not sufficie…
MiDAS: A Multimodal Data Acquisition System and Dataset for Robot-Assisted Minimally Invasive Surgery
Keshara Weerasinghe, Seyed Hamid Reza Roodabeh, Andrew Hawkins +3
Background: Robot-assisted minimally invasive surgery (RMIS) research increasingly relies on multimodal data, yet access to proprietary robot telemetry remains a major barrier. We…
Expert-Guided Prompting and Retrieval-Augmented Generation for Emergency Medical Service Question Answering
Xueren Ge, Sahil Murtaza, Anthony Cortez +1
Large language models (LLMs) have shown promise in medical question answering, yet they often overlook the domain-specific expertise that professionals depend on, such as the clini…
EgoEMS: A High-Fidelity Multimodal Egocentric Dataset for Cognitive Assistance in Emergency Medical Services
Keshara Weerasinghe, Xueren Ge, Tessa Heick +5
Emergency Medical Services (EMS) are critical to patient survival in emergencies, but first responders often face intense cognitive demands in high-stakes situations. AI cognitive…
Systems-Theoretic and Data-Driven Security Analysis in ML-enabled Medical Devices
Gargi Mitra, Mohammadreza Hallajiyan, Inji Kim +5
The integration of AI/ML into medical devices is rapidly transforming healthcare by enhancing diagnostic and treatment facilities. However, this advancement also introduces serious…
Safety Interventions against Adversarial Patches in an Open-Source Driver Assistance System
Cheng Chen, Grant Xiao, Daehyun Lee +4
Drivers are becoming increasingly reliant on advanced driver assistance systems (ADAS) as autonomous driving technology becomes more popular and developed with advanced safety feat…
Real-Time Multimodal Cognitive Assistant for Emergency Medical Services
Keshara Weerasinghe, Saahith Janapati, Xueren Ge +4
Emergency Medical Services (EMS) responders often operate under time-sensitive conditions, facing cognitive overload and inherent risks, requiring essential skills in critical thin…
Multimodal Transformers for Real-Time Surgical Activity Prediction
Keshara Weerasinghe, Seyed Hamid Reza Roodabeh, Kay Hutchinson +1
Real-time recognition and prediction of surgical activities are fundamental to advancing safety and autonomy in robot-assisted surgery. This paper presents a multimodal transformer…
Camera-Independent Single Image Depth Estimation from Defocus Blur
Lahiru Wijayasingha, Homa Alemzadeh, John A. Stankovic
Monocular depth estimation is an important step in many downstream tasks in machine vision. We address the topic of estimating monocular depth from defocus blur which can yield mor…
Towards Interpretable Motion-level Skill Assessment in Robotic Surgery
Kay Hutchinson, Katherina Chen, Homa Alemzadeh
Purpose: We study the relationship between surgical gestures and motion primitives in dry-lab surgical exercises towards a deeper understanding of surgical activity at fine-grained…
KnowSafe: Combined Knowledge and Data Driven Hazard Mitigation in Artificial Pancreas Systems
Xugui Zhou, Maxfield Kouzel, Chloe Smith +1
Significant progress has been made in anomaly detection and run-time monitoring to improve the safety and security of cyber-physical systems (CPS). However, less attention has been…
DKEC: Domain Knowledge Enhanced Multi-Label Classification for Diagnosis Prediction
Xueren Ge, Satpathy Abhishek, Ronald Dean Williams +2
Multi-label text classification (MLTC) tasks in the medical domain often face the long-tail label distribution problem. Prior works have explored hierarchical label structures to f…
Robotic Scene Segmentation with Memory Network for Runtime Surgical Context Inference
Zongyu Li, Ian Reyes, Homa Alemzadeh
Surgical context inference has recently garnered significant attention in robot-assisted surgery as it can facilitate workflow analysis, skill assessment, and error detection. Howe…
Runtime Stealthy Perception Attacks against DNN-based Adaptive Cruise Control Systems
Xugui Zhou, Anqi Chen, Maxfield Kouzel +4
Adaptive Cruise Control (ACC) is a widely used driver assistance technology for maintaining the desired speed and safe distance to the leading vehicle. This paper evaluates the sec…
Evaluating the Task Generalization of Temporal Convolutional Networks for Surgical Gesture and Motion Recognition using Kinematic Data
Kay Hutchinson, Ian Reyes, Zongyu Li +1
Fine-grained activity recognition enables explainable analysis of procedures for skill assessment, autonomy, and error detection in robot-assisted surgery. However, existing recogn…
Short: Basal-Adjust: Trend Prediction Alerts and Adjusted Basal Rates for Hyperglycemia Prevention
Chloe Smith, Maxfield Kouzel, Xugui Zhou +1
Significant advancements in type 1 diabetes treatment have been made in the development of state-of-the-art Artificial Pancreas Systems (APS). However, lapses currently exist in th…
Towards Surgical Context Inference and Translation to Gestures
Kay Hutchinson, Zongyu Li, Ian Reyes +1
Manual labeling of gestures in robot-assisted surgery is labor intensive, prone to errors, and requires expertise or training. We propose a method for automated and explainable gen…
Towards Developing Safety Assurance Cases for Learning-Enabled Medical Cyber-Physical Systems
Maryam Bagheri, Josephine Lamp, Xugui Zhou +2
Machine Learning (ML) technologies have been increasingly adopted in Medical Cyber-Physical Systems (MCPS) to enable smart healthcare. Assuring the safety and effectiveness of lear…
COMPASS: A Formal Framework and Aggregate Dataset for Generalized Surgical Procedure Modeling
Kay Hutchinson, Ian Reyes, Zongyu Li +1
Purpose: We propose a formal framework for the modeling and segmentation of minimally-invasive surgical tasks using a unified set of motion primitives (MPs) to enable more objectiv…
Design and Validation of an Open-Source Closed-Loop Testbed for Artificial Pancreas Systems
Xugui Zhou, Maxfield Kouzel, Haotian Ren +1
The development of a fully autonomous artificial pancreas system (APS) to independently regulate the glucose levels of a patient with Type 1 diabetes has been a long-standing goal…