IEEE Open Journal of
Engineering in Medicine and Biology

The IEEE Open Journal of Engineering in Medicine and Biology covers the development and application of engineering concepts and methods to biology, medicine and health sciences to provide effective solutions to biological, medical and healthcare problems.
Why
OJEMB
Information
for Authors
Recent
Manuscripts

Highlights

Paolo Bonato, Ph.D.
Editor-in-chief
Editor-in-chief

Paolo Bonato, Ph.D., serves as Director of the Motion Analysis Laboratory at Spaulding Rehabilitation Hospital, Boston MA. He is an Associate Professor in the Department of Physical Medicine and Rehabilitation at Harvard Medical School, an Adjunct Professor of Biomedical Engineering at the MGH Institute of Health Professions, an Associate Faculty Member at the Wyss Institute for Biologically Inspired Engineering, and an Adjunct Associate Professor at Boston University College of Health & Rehabilitation Sciences. He has held Adjunct Faculty positions at MIT, the University of Ireland Galway, and the University of Melbourne. His research work is focused on the development of rehabilitation technologies with special emphasis on wearable technology and robotics. Dr. Bonato served as the Founding Editor-in-Chief of Journal on NeuroEngineering and Rehabilitation, which is now ranked #4 by impact factor out 132 journals with focus on rehabilitation technology assessed... Read more

Paolo Bonato, Ph.D., serves as Director of the Motion Analysis Laboratory at Spaulding Rehabilitation Hospital, Boston MA. He is an Associate Professor in the Department of Physical Medicine and Rehabilitation at Harvard Medical School, an Adjunct Professor of Biomedical Engineering at the MGH Institute of Health Professions, an Associate Faculty Member at the Wyss Institute for Biologically Inspired Engineering, and an Adjunct Associate Professor at Boston University College of Health & Rehabilitation Sciences. He has held Adjunct Faculty positions at MIT, the University of Ireland Galway, and the University of Melbourne. His research work is focused on the development of rehabilitation technologies with special emphasis on wearable technology and robotics. Dr. Bonato served as the Founding Editor-in-Chief of Journal on NeuroEngineering and Rehabilitation, which is now ranked #4 by impact factor out 132 journals with focus on rehabilitation technology assessed by Thomson Reuters. He serves as a Member of the Advisory Board of the IEEE Journal of Biomedical and Health Informatics and as Associate Editor of the IEEE Journal of Translational Engineering in Health and Medicine. Dr. Bonato served as an Elected Member of the IEEE Engineering in Medicine and Biology Society (EMBS) AdCom (2007-2010) and as IEEE EMBS Vice President for Publications (2013-2016). He also served as President of the International Society of Electrophysiology and Kinesiology (2008-2010). He received the M.S. degree in electrical engineering from Politecnico di Torino, Turin, Italy in 1989 and the Ph.D. degree in biomedical engineering from Universita` di Roma “La Sapienza” in 1995.

Read less

Optical Thermography Infrastructure to Assess Thermal Distribution in Critically Ill Children

Authors: Monisha Shcherbakova, Rita Noumeir, Michael Levy, Armelle Bridier, Victor Lestrade, Philippe Jouvet

The temperature distribution at the skin surface could be a useful tool to monitor changes in cardiac output. Goal: The aim of this study was to explore infrared thermography as a method to analyze temperature profiles of critically ill children. Methods: Patients admitted to the pediatric intensive care unit (PICU) were included in this study. An infrared sensor was used to take images in clinical conditions. The infrared core and limb temperatures (θ c & θ l ) were extracted, as well as temperatures along a line drawn between these two regions. Results: The median [interquartile range] θ c extracted from the images was 33.88°C [32.74-34.19] and the median θ l was 30.21°C [28.89-33.13]. There was a good correlation between the θ c and the clinical axillary temperature (rho = 0.39, p-value = 0.016). There was also a good correlation between the θ c and θ l (rho = 0.66, p-value = 1.2 e −05 ). Conclusion: Thermography was found to be effective to estimate the body temperature. Correlation with specific clinical conditions needs further study.

Date of Publication: 17 December 2021

Enhancement of Closed-Loop Cognitive Stress Regulation using Supervised Control Architectures

Authors: Hamid Fekri Azgomi, Rose T. Faghih

Goal: We propose novel supervised control architectures to regulate the cognitive stress state and close the loop. Methods: We take information present in underlying neural impulses of skin conductance signals and employ model-based control techniques to close the loop in a state-space framework. For performance enhancement, we establish a supervised knowledge-based layer to update control system in real time. In the supervised architecture, the controller parameters are being updated in real-time. Results: Statistical analyses demonstrate the efficiency of supervised control architectures in improving the closed-loop results while maintaining stress levels within a desired range with more optimized control efforts. The model-based approaches would guarantee the control system-perspective criteria such as stability and optimality, and the proposed supervised knowledge-based layer would further enhance their efficiency. Conclusion: Outcomes in this in silico study verify the proficiency of the proposed supervised architectures to be implemented in the real world.

Date of Publication: 20 January 2022

Artificial Intelligence for Detecting COVID-19 with the Aid of Human Cough, Breathing and Speech Signals: Scoping Review

Authors: Mouzzam Husain, Andrew Simpkin, Claire Gibbons, Tanya Talkar, Daniel M. Low, Paolo Bonato, Satrajit Ghosh, Thomas Quatieri, Derek T. OKeeffe

Background: Official tests for COVID-19 are time consuming, costly, can produce high false negatives, use up vital chemicals and may violate social distancing laws. Therefore, a fast and reliable additional solution using recordings of cough, breathing and speech data for preliminary screening may help alleviate these issues. Objective: This scoping review explores how Artificial Intelligence (AI) technology aims to detect COVID-19 disease by using cough, breathing and speech recordings, as reported in the literature. Here, we describe and summarize attributes of the identified AI techniques and datasets used for their implementation. Methods: A scoping review was conducted following the guidelines of PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews). Electronic databases (Google Scholar, Science Direct, and IEEE Xplore) were searched between 1st April 2020 and 1st August 2021. Terms were selected based on the target intervention (i.e. AI), the target disease (i.e., COVID-19) and acoustic correlates of the disease (i.e. speech, breathing and cough). A narrative approach was used to summarize the extracted data. Results: 21 studies and 8 Apps out of the 83 retrieved studies met the inclusion criteria. Half of the publications and Apps were from the USA. The most prominent AI architecture used was a convolutional neural network, followed by a recurrent neural network. AI models were mainly trained, tested and run on websites and personal computers, rather than on phone apps. More than half of the included studies reported area-under-the-curve performance of greater than 0.90 on symptomatic and negative datasets while one study achieved 100% sensitivity in predicting asymptomatic COVID-19 for cough-, breathing- or speech-based acoustic features. Conclusions: The included studies show that AI has the potential to help detect COVID-19 using cough, breathing and speech samples. However, the proposed methods have not been tested clinically, understood neurophysiologicallly, nor validated with broad training and testing datasets.

Date of Publication: 14 February 2022

Heart-Lung Interactions During Mechanical Ventilation: Analysis via a Cardiopulmonary Simulation Model

Authors: Nikolaos Karamolegkos, Antonio Albanese, Nicolas W. Chbat

Heart-lung interaction mechanisms are generally not well understood. Mechanical ventilation, for example, accentuates such interactions and could compromise cardiac activity. Thereby, assessment of ventilation-induced changes in cardiac function is considered an unmet clinical need. We believe that mathematical models of the human cardiopulmonary system can provide invaluable insights into such cardiorespiratory interactions. In this article, we aim to use a mathematical model to explain heart-lung interaction phenomena and provide physiologic hypotheses to certain contradictory experimental observations during mechanical ventilation. To accomplish this task, we highlight three model components that play a crucial role in heart-lung interactions: 1) pericardial membrane, 2) interventricular septum, and 3) pulmonary circulation that enables pulmonary capillary compression due to lung inflation. Evaluation of the model’s response under simulated ventilation scenarios shows good agreement with experimental data from the literature. A sensitivity analysis is also presented to evaluate the relative impact of the model’s highlighted components on the cyclic ventilation-induced changes in cardiac function.

Date of Publication: 17 November 2021

Ankle Exoskeleton Assistance Increases Six-Minute Walk Test Performance in Cerebral Palsy

Authors: Benjamin C. Conner, Greg Orekhov, Zachary F. Lerner

Objective: To determine the effects of providing battery-powered ankle dorsiflexor and plantar flexor exoskeleton assistance on six-minute walk test performance and efficiency in children and young adults with cerebral palsy by comparing distance walked under exoskeleton assisted (Assisted) and no device (Shod) walking conditions, and explore the acclimation rate to maximal walking with ankle exoskeleton assistance. Results: Six-minute walk test performance significantly improved under the final Assisted condition test compared to the Shod condition (42 ± 27 m, p = 0.02), surpassing the minimum clinically important difference range for children and young adults with CP. There was no difference in walking efficiency (-0.06 ± 0.1, p = 0.3). Participants had an average acclimation rate of 19.6 m per session. Conclusions: Powered ankle assistance can significantly improve six-minute walk test performance in individuals with mild-to-moderate gait impairment from CP, supporting the use of this intervention to improve functional mobility and walking capacity in this patient population.

Date of Publication: 15 December 2021

Feasibility Validation on Healthy Adults of a Novel Active Vibrational Sensing Based Ankle Band for Ankle Flexion Angle Estimation

Authors: Peiqi Kang, Shuo Jiang, Peter B. Shull, Benny Lo

Goal: In this paper, we introduced a novel ankle band with a vibrational sensor that can achieve low-cost ankle flexion angle estimation, which can be potentially used for automated ankle flexion angle estimation in home-based foot drop rehabilitation scenarios. Methods: Previous ankle flexion angle estimation methods require either professional knowledge or specific equipment and lab environment, which is not feasible for foot drop patients to achieve accurate measurement by themselves in a home-based scenario. To solve the above problems, a prototype was developed based on the assumption that the echo of a vibration signal on the tibialis anterior had different acoustic impedance distribution. By analyzing the frequency spectrum of the echo, the ankle flexion angle can be estimated. Therefore, a surface transducer was utilized to generate frequency-varying active vibration, and a contact microphone was utilized to capture the echo. A portable analog signal processing hub drove the transducer, and was used for echo signal collection from the microphone. Finally, a Random Forest regression model was applied to estimate the ankle flexion angle based on the spectrum amplitude of the echo. Results: Five healthy subjects were recruited in the experiment. The regression estimation error is 4.16 degrees, and the R 2 is 0.81. Conclusions: These results demonstrate the feasibility of the proposed ankle band for accurate ankle flexion angle estimation.

Date of Publication: 23 November 2021