IEEE Transactions on Biomedical Engineering

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Electro-optical classification of pollen grains via microfluidics and machine learning
This interdisciplinary work involves sensor science, microfluidics, machine learning, and palynology. Palynology - i.e., the study of pollen and fungal spores - finds applications in high-impact fields like air quality control, allergology, and agriculture. Traditionally, the study of pollen takes place through microscopic analysis performed by specialized operators, after staining of the sample. The procedure requires long times and is prone to human errors. Therefore, there is an unmet need for accurate, label-free, and automated systems for the analysis of pollen, ideally within a field-portable and cost-effective platform. In this framework, we propose an original multimodal approach... Read more
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Semi-Automatic Planning and Three-Dimensional Electrospinning of Patient-Specific Grafts for Fontan Surgery
This work aims to develop a semi-automatic tissue engineered vascular graft (TEVG) planning method for designing and 3D-printing hemodynamically optimized Fontan TEVGs. We present a computation framework by parameterizing Fontan grafts to explore patient-specific vascular graft design space and search for optimal designs. We employed nonlinear constrained optimization technique to minimize indexed power loss of Fontan grafts while keeping hepatic flow distribution (HFD) and percentage of abnormal wall shear stress (%WSS) within clinically acceptable thresholds. Our work significantly reduces the collaborative effort and turnaround time between clinicians and engineering teams for designing patient-specific hemodynamically optimized TEVGs... Read more
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Mechanical Imaging of Soft Tissues with Miniature Climbing robots
We propose a method that uses our previously developed skin-crawling robots to noninvasively test the mechanical properties of soft tissue. We explore the use of two miniature sensors: an indenter and a cutometer. We evaluate the sensor's performance from data collected on simulated tissue, classifying the depth and size of a simulated lump with over 98.8% accuracy using convolutional neural nets. Finally, we do limited on-body testing to map dry skin on the forearm with a cutometer. We hope to improve the ability to test tissues noninvasively, providing better sensitivity and systematic data collection... Read more
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A Framework for Efficient N-Way Interaction Testing in Case/Control Studies With Categorical Data
Most common diseases are influenced by multiple gene interactions and interactions with the environment. Performing an exhaustive search to identify such interactions is computationally expensive and needs to address the multiple testing problem. A four-step framework is proposed for the... Read more
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FENet: A Frequency Extraction Network for Obstructive Sleep Apnea Detection
Obstructive Sleep Apnea (OSA) is a highly prevalent but inconspicuous disease that seriously jeopardizes the health of human beings. Polysomnography (PSG), the gold standard of detecting OSA, requires multiple specialized sensors for signal collection, hence patients have to physically visit... Read more
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Continuous Gait Phase Estimation using LSTM for Robotic Transfemoral Prosthesis Across Walking Speeds
User gait phase estimation plays a key role for the seamless control of the lower-limb robotic assistive devices (e.g., exoskeletons or prostheses) during ambulation. To achieve this, several studies have attempted to estimate the gait phase using a thigh or... Read more
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Characterization of a Benchmark Database for Myoelectric Movement Classification
In this paper, we characterize the Ninapro database and its use as a benchmark for hand prosthesis evaluation. The database is a publicly available resource that aims to support research on advanced myoelectric hand prostheses. The database is obtained by... Read more
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A Novel Wearable Device for Continuous Temperature Monitoring & Fever Detection
Continuous temperature monitoring in high-risk patients can enable healthcare providers to remotely track patients’ temperatures, promptly detect fevers and timely intervene to improve clinical outcomes. We evaluated if a novel wearable, continuous temperature monitor (Verily Patch) can reliably estimate body... Read more
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Using Wearables and Machine Learning to Enable Personalized Lifestyle Recommendations to Improve Blood Pressure
Background: Blood pressure (BP) is an essential indicator for human health and is known to be greatly influenced by lifestyle factors, like activity and sleep factors. However, the degree of impact of each lifestyle factor on BP is unknown and... Read more
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Quantification of Motor Function Post-stroke using Novel Combination of Wearable Inertial and Mechanomyographic Sensors
Subjective clinical rating scales represent the gold-standard diagnosis of motor function following stroke, however in practice they suffer from well-recognized limitations including assessor variance, low inter-rater reliability and low resolution. Automated systems have been proposed for empirical quantification but have... Read more