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IEEE MIT Undergraduate Research Technology Conference (URTC)

作者： Evan Buckley Joshua Geyster Benjamin Larkin Ulkuhan Guler Department of Electrical and Computer Engineering Worcester Polytechnic Institute Worcester MA

This article presents the design of a miniaturized, minimally-invasive blood pH sensing device for medical point-of-care applications. Blood pH is a vital marker for determining respiratory and metabolic health. Abnormal blood pH levels indicate that the body’s natural buffer system is out of balance, which may imply serious health issues, such as kidney damage, pneumonia, or heart failure. This work utilizes an Ion Sensitive Field Effect Transistor (ISFET) to measure the pH of a liquid solution with a future focus on blood pH measurements. We designed a readout circuit to convert the changing threshold voltage of the ISFET, based on the solution’s pH level, to a pH measurement displayed on the device’s screen in real-time. With its portable size and its ability to accurately measure with a minimal amount of solution, this device may be a more convenient alternative for home use when compared to the current state-of-the-art blood pH measurement techniques.

关键词： Electrodes Pulmonary diseases Point of care Size measurement Threshold voltage Real-time systems Sensors

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STREAM: Software Tool for Routing Efficiently Advanced Macrofluidics

arXiv

引用

arXiv 2023年

作者： Wang, Lehong Kendre, Savita V. Liu, Haotian Nemitz, Markus P. Robotic Materials Group Department of Robotics Engineering Worcester Polytechnic Institute MA01609 United States Department of Computer Science Worcester Polytechnic Institute MA01609 United States Department of Electrical and Computer Engineering Worcester Polytechnic Institute MA01609 United States

The current fabrication and assembly of fluidic circuits for soft robots relies heavily on manual processes;as the complexity of fluidic circuits increases, manual assembly becomes increasingly arduous, error-prone, and time-consuming. We introduce a software tool that generates printable fluidic networks automatically. We provide a library of fluidic logic elements that are easily 3D printed from thermoplastic polyurethanes using Fused Deposition Modeling only. Our software tool and component library allow the development of arbitrary soft digital circuits. We demonstrate a variable frequency ring oscillator and a full adder. The simplicity of our approach using FDM printers only, democratizes fluidic circuit implementation beyond specialized laboratories. Our software is available on GitHub (https://***/roboticmaterialsgroup/FluidLogic) Copyright © 2023, The Authors. All rights reserved.

关键词： computer software

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Soft Fluidic Closed-Loop Controller for Untethered Underwater Gliders

arXiv

引用

arXiv 2023年

作者： Bonofiglio, Kalina Whiteside, Lauryn Angeles, Maya Haahr, Matthew Simpson, Brandon Palmer, Josh Wu, Yijia Nemitz, Markus P. Department of Robotics Engineering Worcester Polytechnic Institute 100 Institute Road WorcesterMA01609 United States Department of Mechanical Engineering Worcester Polytechnic Institute 100 Institute Road WorcesterMA01609 United States Department of Electrical Engineering Worcester Polytechnic Institute 100 Institute Road WorcesterMA01609 United States

Soft underwater robots typically explore bioinspired designs at the expense of power efficiency when compared to traditional underwater robots, which limits their practical use in real-world applications. We leverage a fluidic closed-loop controller to actuate a passive underwater glider. A soft hydrostatic pressure sensor is configured as a bang-bang controller actuating a swim bladder made from silicone balloons. Our underwater glider oscillates between the water surface and 4 m depth while traveling 15 m translationally. The fluidic underwater glider demonstrates a power efficiency of 28 mW/m. This work demonstrates a low-cost and power-efficient underwater glider and non-electronic controller. Due to its simple design, low cost, and ease of fabrication using FDM printing and soft lithography, it serves as a starting point for the exploration of non-electronic underwater soft robots. © 2023, CC BY.

关键词： 3D printing

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Liver Necrotic Mapping Using Diffusing Illumination Photoacoustic Imaging

Liver Necrotic Mapping Using Diffusing Illumination Photoaco...

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Photons Plus Ultrasound: Imaging and Sensing 2025

作者： Gao, Shang Hua, Yanbo Bhagavatula, Sharath K. Liu, Guigen Jonas, Oliver Zhang, Haichong K. Department of Robotics Engineering Worcester Polytechnic Institute MA United States Department of Biomedical Engineering Worcester Polytechnic Institute MA United States Department of Computer Science Worcester Polytechnic Institute MA United States Department of Radiology Brigham and Women’s Hospital Harvard Medical School MA United States

ISBN: (纸本)9781510683860

Liver cancer treatments often face challenges, particularly when tumors are considered surgically unresectable. Percutaneous radiofrequency ablation (RFA) offers a minimally invasive alternative but can have higher recurrence rates due to incomplete ablations. Standard non-contrast ultrasound imaging cannot distinguish ablation-induced necrosis, limiting ability to monitor treatment and ensure complete tumor coverage during the ablation procedure. Photoacoustic (PA) imaging, which uses light to generate ultrasound signals, has shown promise in distinguishing between ablated and normal tissues through spectroscopic PA (sPA) imaging. However, conventional light delivery methods are unsuitable for minimally invasive procedures due to bulky fiber bundles. Optical fibers, though suitable for less invasive insertion, have limited illumination volume because of the confined light delivery only at the fiber tip. This study introduces a diffusing fiber approach for effective light delivery within the liver, enhancing the PA excitation area compared to conventional tip illumination. The diffusing fiber was tested in both phantom and ex vivo liver tissue studies. Results demonstrated that the diffusing fiber provided a wider PA excitation area and more homogeneous illumination. In an ex vivo porcine liver, the diffusing fiber accurately mapped the ablated region with minimal deviation from gross pathological measurements. Our findings suggest that this diffusing fiber approach could significantly improve the accuracy of PA imaging for liver ablation, potentially reducing recurrence rates by ensuring complete tumor ablation and minimizing complications by enabling real time monitoring. This innovation paves the way for more reliable and effective minimally invasive treatments for liver cancer. © 2025 SPIE.

关键词： Photoacoustic microscopy

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Virtual-Coordinate Based Sampling and Embedding for Machine Learning with Graph Data

Virtual-Coordinate Based Sampling and Embedding for Machine ...

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International Conference on Machine Learning and Applications (ICMLA)

作者： Zheyi Qin Anura P. Jayasumana Randy Paffenroth Electrical and Computer Engineering Colorado State University Fort Collins USA Mathematical Sciences Computer Science and Data Science Worcester Polytechnic Institute Worcester MA USA

ISBN: (数字)9798350374889

ISBN: (纸本)9798350374896

Graph embedding techniques remains the only alternative for Graph Neural Networks (GNNs) in machine learning for applications for which data in the form of graphs. GNNs, however, require substantial computational resources and are not able to provide certain advantages of conventional neural networks, such as transfer learning. The existing embedding based techniques, however, rely on random walk based strategies, which are not efficient in capturing the topology information, or use more structured strategies followed by feature engineering to achieve the required embedding. We propose VCNN (VirtualCoordinate based Neural Networks) technique that directly uses distances to an extremely small set (< 1 %) of random nodes (anchors) for network sampling and embedding, thus eliminating the feature engineering step. Experiments with two benchmark datasets show that a simple shallow neural network configuration using VC can achieve comparable performance to existing embedding-based techniques requiring deeper neural networks, thus also eliminating the need to experiment with multiple neural network configurations. This finding is particularly relevant for applications where computational efficiency is crucial. This research extends the machine learning toolbox for networks and data in the form of networks, highlighting the importance of strategic parameter use and model simplicity.

关键词： Network topology Social networking (online) Neural networks Transfer learning Machine learning Benchmark testing Predictive models Data models Topology Tuning

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Optimizing Magnetic Induction Sensors for Non-Obtrusive Vital Signs Monitoring: Impact of Current Control on Operational Quality

Optimizing Magnetic Induction Sensors for Non-Obtrusive Vita...

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Annual International Conference of the IEEE engineering in Medicine and Biology Society (EMBC)

作者： Mahdi Momeni Adrian Radomski Ulkuhan Guler Daniel Teichmann SDU Health Informatics and Technology The Maersk Mc-Kinney Moller Institute University of Southern Denmark Denmark Department of Electrical and Computer Engineering Worcester Polytechnic Institute Worcester MA USA

ISBN: (数字)9798350371499

ISBN: (纸本)9798350371505

This paper investigates the advancement of magnetic induction-based heart and respiration rate sensing by actively controlling the coil current. This is realized through the implementation of a current-starved inverter mechanism. Experiments show a notable level of accuracy of the proposed circuit in measuring heart and respiration activity when compared to a reference sensor. The direct manipulation of current levels was found to have a direct impact on the signal strength. Incrementing the overall current within the proposed circuit from 60 mA to 100 mA resulted in an augmentation of the output amplitude of the heart rate signal from 8.5 mV to 27 mV, accompanied by a marginal enhancement in beat-to-beat interval accuracy. Moreover, the proposed sensor demonstrates noteworthy precision in monitoring the respiratory rate when compared with the reference sensor under different current values, exhibiting the same trend in signal strength. This finding offers valuable insight for the development of future power-optimized magnetic induction sensors with enhanced robustness.

关键词： Heart rate Current control Accuracy Power demand Inverters Robustness Sensors Biomedical monitoring Monitoring Magnetic circuits

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DeePaGait: Motor Assessment of Parkinson’s Disease Using a Multi-Layer 1D Convolutional Neural Network on Smartphone Gait Data

DeePaGait: Motor Assessment of Parkinson’s Disease Using a ...

引用

IEEE International Conference on Big Data

作者： Hamza Abujrida Emmanuel Agu Kaveh Pahlavan Department of Electrical and Computer Engineering Worcester Polytechnic Institute Worcester MA USA

ISBN: (纸本)9781665480468

Nearly 1 million people in the USA and 10 million people worldwide are living with Parkinson’s disease (PD) [1]. The progression of the disease can be inferred from the changes in the patients’ gait to inform early intervention. Due to their near-ubiquitous ownership, smartphone sensing is a very effective, highly available way of assessing gait. Objectives: In this work, we explore the inference of PD gait, by analyzing the patient’s walk data collected from a smartphone in the patients’ homes, using Deep Learning (DL) methods. Specifically, we investigate DL models to classify the severity of the motor part of the UPDRS (MDS-UPDRS 2.10-2.13). Methodology: Using data from the mPower study [2], we selected 152 PD patients who recorded at least 3 walks before taking their medication. We extracted gait cycles from the accelerometer and gyroscope sensor records and fed them to DeePaGait; our DL multi-layer Conventional Neural Network (CNN). DeePaGait operates on 1Dimensional convolution filters to classify 30 seconds of walking data into one of five severity levels (Normal, Slight, Mild, Moderate, Severe). Results: Our DeePaGait DL network was able to Classify the PD Walking-Balance, Shaking/Tremor, and Freeze of Gait (FoG) symptoms, with an accuracy of: 99.1%,98.4%, and 98.2% respectively. Conclusion: Despite the challenges of working with a patient-labeled, crowdsourced dataset, we were able to demonstrate that gait classification based on smartphone sensor data is a viable approach to infer the severity of PD gait anomalies.

关键词： Legged locomotion Neural networks Predictive models Big Data Data models Sensors Planning

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Realistic Data Generation for 6D Pose Estimation of Surgical Instruments

Realistic Data Generation for 6D Pose Estimation of Surgical...

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IEEE International Conference on Robotics and Automation (ICRA)

作者： Juan Antonio Barragan Jintan Zhang Haoying Zhou Adnan Munawar Peter Kazanzides Department of Computer Science Johns Hopkins University Baltimore MD USA Department of Robotics Engineering Worcester Polytechnic Institute Worcester MA USA

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

Automation in surgical robotics has the potential to improve patient safety and surgical efficiency, but it is difficult to achieve due to the need for robust perception algorithms. In particular, 6D pose estimation of surgical instruments is critical to enable the automatic execution of surgical maneuvers based on visual feedback. In recent years, supervised deep learning algorithms have shown increasingly better performance at 6D pose estimation tasks; yet, their success depends on the availability of large amounts of annotated data. In household and industrial settings, synthetic data, generated with 3D computer graphics software, has been shown as an alternative to minimize annotation costs of 6D pose datasets. However, this strategy does not translate well to surgical domains as commercial graphics software have limited tools to generate images depicting realistic instrument-tissue interactions. To address these limitations, we propose an improved simulation environment for surgical robotics that enables the automatic generation of large and diverse datasets for 6D pose estimation of surgical instruments. Among the improvements, we developed an automated data generation pipeline and an improved surgical scene. To show the applicability of our system, we generated a dataset of 7.5k images with pose annotations of a surgical needle that was used to evaluate a state-of-the-art pose estimation network. The trained model obtained a mean translational error of 2.59mm on a challenging dataset that presented varying levels of occlusion. These results highlight our pipeline’s success in training and evaluating novel vision algorithms for surgical robotics applications.

关键词： Training Visualization Medical robotics Three-dimensional displays Annotations Instruments Pose estimation

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Hands-On Learning: Teaching Parallel and Distributed Computing through Unplugged Activities in Undergraduate CS Courses

Hands-On Learning: Teaching Parallel and Distributed Computi...

引用

2024 Workshops of the International Conference for High Performance Computing, Networking, Storage and Analysis, SC Workshops 2024

作者： Dasgupta, Anurag Margapuri, Venkat Shamoun, Simon Taneja, Shubbhi Toups, Matthew Valdosta State University Computer Science and Engineering Technology ValdostaGA31698 United States Villanova University Department of Computing Sciences VillanovaPA19085 United States Hofstra University Department of Computer Science HempsteadNY11549 United States Worcester Polytechnic Institute Department of Computer Science WorcesterMA01609 United States Tulane University Computer Science Department New OrleansLA70118 United States

ISBN: (纸本)9798350355543

The authors present and evaluate an unplugged activity to introduce parallel computing concepts to undergraduate students. Students in five CS classrooms used a deck of playing cards in small groups to consider how parallelization can improve performance and how improvement decreases with increased parallelization. Before and after the activity, students took a short survey about their solution and their ideas about parallelism. The authors carried out this activity in seven courses at five institutions in the 2023-2024 academic year. The results showed that students had an increased appreciation for parallelization and this type of activity. © 2024 IEEE.

关键词： computer science CS CS education hands-on learning parallel distributed computing unplugged

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Autonomous Search of Semantic Objects in Unknown Environments

arXiv

引用

arXiv 2023年

作者： Qian, Zhentian Fu, Jie Xiao, Jing Robotics Engineering Department Worcester Polytechnic Institute WorcesterMA United States Department of Electrical and Computer Engineering University of Florida GainesvilleFL United States

This paper addresses the problem of enabling a robot to search for a semantic object, i.e., an object with a semantic label, in an unknown and GPS-denied environment. For the robot in the unknown environment to detect and find the target semantic object, it must perform simultaneous localization and mapping (SLAM) at both geometric and semantic levels using its onboard sensors while planning and executing its motion based on the ever-updated SLAM results. In other words, the robot must be able to conduct simultaneous localization, semantic mapping, motion planning, and execution in real-time in the presence of sensing and motion uncertainty. This is an open problem as it combines semantic SLAM based on perception and real-time motion planning and execution under uncertainty. Moreover, the goals of the robot motion change on the fly depending on whether and how the robot can detect the target object. We propose a novel approach to tackle the problem, leveraging semantic SLAM, Bayesian Networks, Markov Decision Process, and Real-Time Dynamic Programming. The results in simulation and real experiments demonstrate the effectiveness and efficiency of our approach. © 2023, CC BY.

关键词： Semantics

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