Monitoring

Wearable Loop Sensors for Knee Flexion Monitoring: Dynamic Measurements on Human Subjects
Wearable Loop Sensors for Knee Flexion Monitoring: Dynamic Measurements on Human Subjects 150 150 IEEE Open Journal of Engineering in Medicine and Biology (OJEMB)
Goals: We have recently introduced a new class of wearable loop sensors for joint flexion monitoring that overcomes limitations in the state-of-the-art. Our previous studies reported a proof-of-concept on a… read more

Guest Editorial Introduction to the Special Section on Invisible Sensing: Radar-Based Biomonitoring
Guest Editorial Introduction to the Special Section on Invisible Sensing: Radar-Based Biomonitoring 150 150 IEEE Open Journal of Engineering in Medicine and Biology (OJEMB)

Author(s): Bjoern M. Eskofier, Martin Vossiek Abstract: Published in: IEEE Open Journal of Engineering in Medicine and Biology Page(s): 678 – 679 Year of Publication: 2024 Electronic ISSN: 2644-1276 DOI:…

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Overview of Radar-Based Gait Parameter Estimation Techniques for Fall Risk Assessment
Overview of Radar-Based Gait Parameter Estimation Techniques for Fall Risk Assessment 150 150 IEEE Open Journal of Engineering in Medicine and Biology (OJEMB)
Current methods for fall risk assessment rely on Quantitative Gait Analysis (QGA) using costly optical tracking systems, which are often only available at specialized laboratories that may not be easily… read more

Usability Assessment of Technologies for Remote Monitoring of Knee Osteoarthritis
Usability Assessment of Technologies for Remote Monitoring of Knee Osteoarthritis 150 150 IEEE Open Journal of Engineering in Medicine and Biology (OJEMB)
Goal: To evaluate the usability of different technologies designed for a remote assessment of knee osteoarthritis. Methods: We recruited eleven patients affected by mild or moderate knee osteoarthritis, eleven caregivers,… read more

Pulse2AI: An Adaptive Framework to Standardize and Process Pulsatile Wearable Sensor Data for Clinical Applications
Pulse2AI: An Adaptive Framework to Standardize and Process Pulsatile Wearable Sensor Data for Clinical Applications 150 150 IEEE Open Journal of Engineering in Medicine and Biology (OJEMB)
Goal: To establish Pulse2AI as a reproducible data preprocessing framework for pulsatile signals that generate high-quality machine-learning-ready datasets from raw wearable recordings. Methods: We proposed an end-to-end data preprocessing framework… read more

On-Demand Gait-Synchronous Electrical Cueing in Parkinson’s Disease Using Machine Learning and Edge Computing: A Pilot Study
On-Demand Gait-Synchronous Electrical Cueing in Parkinson’s Disease Using Machine Learning and Edge Computing: A Pilot Study 150 150 IEEE Open Journal of Engineering in Medicine and Biology (OJEMB)
Goal: Parkinson’s disease (PD) can lead to gait impairment and Freezing of Gait (FoG). Recent advances in cueing technologies have enhanced mobility in PD patients. While sensor technology and machine… read more

Quantifying the Suitability of Biosignals Acquired During Surgery for Multimodal Analysis
Quantifying the Suitability of Biosignals Acquired During Surgery for Multimodal Analysis 150 150 IEEE Open Journal of Engineering in Medicine and Biology (OJEMB)
Goal: Recently, large datasets of biosignals acquired during surgery became available. As they offer multiple physiological signals measured in parallel, multimodal analysis – which involves their joint analysis – can… read more

COVID-19 Artificial Intelligence Diagnosis using only Cough Recordings
Author(s)3: Jordi Laguarta, Ferran Hueto, Brian Subirana
COVID-19 Artificial Intelligence Diagnosis using only Cough Recordings 200 200 IEEE Open Journal of Engineering in Medicine and Biology (OJEMB)
We hypothesized that COVID-19 subjects, especially including asymptomatics, could be accurately discriminated only from a forced-cough cell phone recording using Artificial Intelligence. To train our MIT Open Voice model we built a data collection pipeline of COVID-19 cough recordings through our website (opensigma.mit.edu) between April and May 2020 and created the largest audio COVID-19 cough balanced dataset reported to date with 5,320 subjects. read more
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