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    Definition and comparison of a new vascular index between young healthy and aged subjects

    , Article IECBES 2014, Conference Proceedings - 2014 IEEE Conference on Biomedical Engineering and Sciences: "Miri, Where Engineering in Medicine and Biology and Humanity Meet", 8 December 2014 through 10 December 2014 ; 2015 , Pages 911-915 ; 9781479940844 (ISBN) Jaafar, R ; Zahedi, E ; Mohd Ali, M. A ; Sharif University of Technology
    Institute of Electrical and Electronics Engineers Inc  2015
    In an effort to identify a useful noninvasive screening tool for cardiovascular diseases (CVD) screening, photoplethysmograph (PPG) signals were acquired and analyzed. These PPG signals were recorded during reactive hyperemia experiments consisting of a 4-minute blood flow blockage of the right arm (RA) brachial artery (BA) using a blood pressure cuff inflator. This procedure is usually done for the assessment of endothelial dysfunction which is a risk factor for developing CVD. In this study, signals of the infrared (IR) and red (R) LED's of the PPG sensor were analyzed. These signals were preprocessed, normalized and slow varying component of the signal (DC values) and the pulsatile... 

    Using a motion sensor to categorize nonspecific low back pain patients: A machine learning approach

    , Article Sensors (Switzerland) ; Volume 20, Issue 12 , 2020 , Pages 1-16 Abdollahi, M ; Ashouri, S ; Abedi, M ; Azadeh Fard, N ; Parnianpour, M ; Khalaf, K ; Rashedi, E ; Sharif University of Technology
    MDPI AG  2020
    Nonspecific low back pain (NSLBP) constitutes a critical health challenge that impacts millions of people worldwide with devastating health and socioeconomic consequences. In today’s clinical settings, practitioners continue to follow conventional guidelines to categorize NSLBP patients based on subjective approaches, such as the STarT Back Screening Tool (SBST). This study aimed to develop a sensor-based machine learning model to classify NSLBP patients into different subgroups according to quantitative kinematic data, i.e., trunk motion and balance-related measures, in conjunction with STarT output. Specifically, inertial measurement units (IMU) were attached to the trunks of ninety-four... 

    A practical sensor-based methodology for the quantitative assessment and classification of chronic non specific low back patients (NSLBP) in clinical settings

    , Article Sensors (Switzerland) ; Volume 20, Issue 10 , 2020 Davoudi, M ; Shokouhyan, S. M ; Abedi, M ; Meftahi, N ; Rahimi, A ; Rashedi, E ; Hoviattalab, M ; Narimani, R ; Parnianpour, M ; Khalaf, K ; Sharif University of Technology
    MDPI AG  2020
    The successful clinical application of patient-specific personalized medicine for the management of low back patients remains elusive. This study aimed to classify chronic nonspecific low back pain (NSLBP) patients using our previously developed and validated wearable inertial sensor (SHARIF-HMIS) for the assessment of trunk kinematic parameters. One hundred NSLBP patients consented to perform repetitive flexural movements in five different planes of motion (PLM): 0◦ in the sagittal plane, as well as 15◦ and 30◦ lateral rotation to the right and left, respectively. They were divided into three subgroups based on the STarT Back Screening Tool. The sensor was placed on the trunk of each...