This session represents peer-reviewed contributed papers presented as posters.
Authors will be available from 3:00-6:00 pm for the interactive session.
The papers will be published in the 2026 Proceedings of the Design of Medical Devices Conference in the ASME Digital Collection.
Click the drop downs below for additional information.
Presentation Details
Bio-Inspired Robotic Capillary Mimetic Core
Full Title: Bio-Inspired Robotic Capillary Mimetic Core: Vibrational Signatures of Red Blood Cells Unveil Mechanosensory Disruption in Hypoxic Tumor Microenvironments
Contrast-Enhanced Micro-Computed Tomography Imaging of Fiber Remodeling in Human Hearts with a History of a Myocardial Infarction
Patient-Specific Coronary Flow Field Prediction using Physics-Informed Neural Operators
In Silico Analysis of Helical Fixation Geometries for Cardiac Lead Implantation and Extraction
A Digital Atlas and Innovative Virtual Reality Application
Full Title: A Digital Atlas and Innovative Virtual Reality Application - “CHD 360”: for the Enhanced Understanding of Congenital Heart Defects
Deriving Dynamic Fractional Flow Reserve From 4D Coronary CT Synthesized by a Diffusion Model
The Dark Side of Organoids
Toward Rapid Assessment for Personalized Inhaler Design via Machine Learning-based Reduced Ordered Modeling (ROM)
Identifying Key Design Parameters for Improving MRI Safety of Embolization Coils
Efficiency Over Complexity: Optimal Neural Architectures for Near-Term Forecasting of Parkinsonian Freezing of Gait
Hemodynamic Simulation of Brain Arteriovenous Malformation (AVM)
Artificial Intelligence Energy-Regulation Modeling System for Fatigue Prediction in People with Multiple Sclerosis
Applying Design Thinking to Elevate the Value Proposition of Medical Devices in LMICs
Full Title: Applying Design Thinking to Elevate the Value Proposition of Medical Devices in LMICs: A Case of an Improved Design of a High Flow Nasal Oxygen Device
A Novel AI Powered Objective Pain Quantifying Software as a Medical Device
Demonstration of Force Plate Capability for Wheelchair Seating Assessment
Qhalikiosk: Design and Implementation of a Multilingual Health Kiosk for Automated Triage
Towards Anticipating Panic Attacks using a Smartwatch: A Pilot Study
Corazón Saludable
Full Title: Corazón Saludable: Cardiovascular Risk of a Colombian Hospital's Personnel and mHealth tools in Primary Prevention of Cardiovascular Disease
A Novel You Only Listen Once (YOLO) Deep Learning Model
Full Title: A Novel You Only Listen Once (YOLO) Deep Learning Model for Interstitial Lung Disease (ILD) Detection using Digital Lung Sound Auscultation
Designing an AI-Powered Voice Assistant Device for Healthcare Workers in Low-Resource Settings
Technology using artificial intelligence (AI) has emerged with the potential to transform healthcare systems of low- and middle-income countries. Nonetheless, there is a lack of implementation of context-aware medical education devices. Many barriers exist in the implementation of these devices, often revolving around lack of internet access and electricity. We introduce AISHA, an offline voice-activated device which leverages the conversational abilities of AI to provide access to healthcare knowledge in rural areas. In this article, we explore AISHA’s design, capabilities, and limitations. We demonstrate the need for devices like AISHA and discuss their potential to transform the way healthcare workers interface with information. This research may assist other ventures in building similar devices in the future. By describing the design of the AISHA device, we will evaluate the considerations of introducing AI-based healthcare solutions in underserved areas.
Hannes V3.0 – Enhancing Fluoroscopic Realism in a Physical Endovascular Training Model
Development and Testing of 3D-Printed Distal Stroke Models for Mechanical Thrombectomy Training
Low-Cost Colonoscopy Manikin Design to Enhance Colonoscopy Training
Leveraging AI for MC/DC Unit Test Case Generation in Safety-Critical Systems
Facial Symmetry Quantification: Landmark-Based Analysis Through Computer Vision Modeling
Four Data-Driven Design Choices for IMU-Based Fetal Movement Detection
Full Title: Four Data-Driven Design Choices for IMU-Based Fetal Movement Detection: Sensitivity, Thresholding, Windowing, and Filtering
Prenatal care requires objective, continuous monitoring, yet current fetal movement (FM) detection methods rely on subjective maternal perception or intermittent clinical testing. Laboratory-based FM detection systems lack data-driven standards for signal preparation and algorithm design. This study presents a methodological approach for characterizing inertial measurement unit (IMU) signals and applies the approach to a unique, clinician-validated dataset of eighteen pregnant participants to provide data-driven insights for objective FM monitoring under stationary conditions. Specifically, suggested guidance for four design choices was presented: (1) The minimum hardware sensitivity required to reliably capture weak FM, (2) the signal magnitude threshold to reliably capture strong FM while rejecting artifacts from maternal movement, (3) the maximum window size for data analysis based on observed FM durations, and (4) the cutoff frequencies for filter design to reject signal noise and artifacts. Linear Mixed-Effect Models (LMEMs) were applied to 1373 FM events across four analytical classes (No FM, Weak FM, Strong FM, Artifact) and the estimated marginal means (EMMs) of the groups were compared to quantify the presented design choices. The methodology presented acts as a foundational step toward standardizing the signal preparation and algorithm design of future fetal movement detection systems.
A Clinical Perspective on Wheelchair Seat Cushion Attributes to Inform Design
Evaluation of the Muse 2 and Tobii Pro Nano to Inform Design of Cognitive State Monitoring Wearables
At-Home Assessment of Usability and Data Collection From a Wearable Bioelectronic Monitoring Device
The Power of a Ping: Leveraging Alerts for Improved Health Outcomes in Low-Resource Settings
A Multimodal Wearable System for Real-Time Detection
Full Title: A Multimodal Wearable System for Real-Time Detection of Exercise-Induced Asthma using Bioimpedance and ECG-Derived Biomarkers
A Wearable Monitor for Exercise-Induced Laryngeal Obstruction (EILO): A Feasibility Case Study
A Universal Joint-adaptive Orthosis with Velocity Modulated Resistance
Protection In Numbers
Full Title: Protection In Numbers: Incorporating Secure by Design Development Practices to Provide Passive Protections to Medical Devices using BINDIV-MD
Emerging Trends and Advances in Point of Care Technologies for Early Detection of Cervical Cancer
Full Title: Emerging Trends and Advances in Point of Care Technologies for Early Detection of Cervical Cancer: A Bibliometric Study