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  • 1.
    Rahman, Hamidur
    et al.
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Ahmed, Mobyen Uddin
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Begum, Shahina
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Non-Contact Physiological Parameters Extraction Using Facial Video Considering Illumination, Motion, Movement and Vibration2020In: IEEE Transactions on Biomedical Engineering, ISSN 0018-9294, E-ISSN 1558-2531, Vol. 67, no 1, p. 88-98, article id 8715455Article in journal (Refereed)
    Abstract [en]

    Objective: In this paper, four physiological parameters, i.e., heart rate (HR), inter-beat-interval (IBI), heart rate variability (HRV), and oxygen saturation (SpO2), are extracted from facial video recordings. Methods: Facial videos were recorded for 10 min each in 30 test subjects while driving a simulator. Four regions of interest (ROIs) are automatically selected in each facial image frame based on 66 facial landmarks. Red-green-blue color signals are extracted from the ROIs and four physiological parameters are extracted from the color signals. For the evaluation, physiological parameters are also recorded simultaneously using a traditional sensor 'cStress,' which is attached to hands and fingers of test subjects. Results: The Bland Altman plots show 95% agreement between the camera system and 'cStress' with the highest correlation coefficient R = 0.96 for both HR and SpO2. The quality index is estimated for IBI considering 100 ms R-peak error; the accumulated percentage achieved is 97.5%. HRV features in both time and frequency domains are compared and the highest correlation coefficient achieved is 0.93. One-way analysis of variance test shows that there are no statistically significant differences between the measurements by camera and reference sensors. Conclusion: These results present high degrees of accuracy of HR, IBI, HRV, and SpO2 extraction from facial image sequences. Significance: The proposed non-contact approach could broaden the dimensionality of physiological parameters extraction using cameras. This proposed method could be applied for driver monitoring application under realistic conditions, i.e., illumination, motion, movement, and vibration.

  • 2.
    Tomasic, Ivan
    et al.
    Jozef Stefan Institute, Ljubljana, Slovenia.
    Frljak, Sabina
    University Medical Center, Ljubljana, Slovenia.
    Trobec, Roman
    Jozef Stefan Institute, Ljubljana, Slovenia.
    Estimating the universal positions of wireless body electrodes for measuring cardiac electrical activity2013In: IEEE Transactions on Biomedical Engineering, ISSN 0018-9294, E-ISSN 1558-2531, Vol. 60, no 12, p. 3368-3374, article id 6574211Article in journal (Refereed)
    Abstract [en]

    A methodology is presented for estimating the wireless body electrode (WE) positions and for calculating the linear transformations that enable the synthesis of a 12-lead ECG or a multichannel ECG from three WEs, which in turn simplifies and improves the acquisition of ECGs. We present, compare, and evaluate three approaches to the synthesis: fully personalized, fully universal, and combined with universal leads and personalized transformations. The evaluation results show that WEs are an acceptable alternative to the standard 12-lead ECG device for patients with chronic myocardial ischemia, if either the fully personalized or combined approach is used. The median correlation coefficients are all higher than 0.94 and 0.92 for the fully personalized and combined approaches, respectively. The corresponding kappa and percentual diagnostic agreements between the synthesized and target 12-lead ECGs are 0.88 (95%) and 0.83 (92%), respectively. The evaluation additionally shows that the personalization of the transformations has more impact on the quality of the synthesized ECGs than the personalization of the WEs' positions.

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