Researcher:
Tokmak, Fadime

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Fadime

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Tokmak

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Tokmak, Fadime

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    Unveiling the relationships between seismocardiogram signals, physical activity types and metabolic equivalent of task scores
    (Institute of Electrical and Electronics Engineers (IEEE), 2023) Department of Electrical and Electronics Engineering; Department of Computer Engineering; Gürsoy, Beren Semiz; Tokmak, Fadime; Faculty Member; Undergraduate Student; Department of Electrical and Electronics Engineering; Department of Computer Engineering; College of Engineering; College of Engineering; 332403; N/A
    Objective: The diagnosis of metabolic syndrome and cardiovascular disorders can highly benefit from physical activity and energy expenditure assessment. In this study, we investigated the relationship between metabolic equivalent of task (MET) scores and seismocardiogram (SCG)-derived parameters. Methods: We worked with the PAMAP2 dataset and focused on the 3-axial chest acceleration data. We first segmented the 3-axial SCG signals into respiration (0-1 Hz), cardiac vibrations (1-20 Hz) and heart sounds (20-40 Hz) components. Additionally, we investigated their combinations: 0-20 Hz, 1-40 Hz and 0-40 Hz. We then windowed each signal, and extracted time and frequency domain features from each window. Using the MET scores and activity types, we trained linear regression and random forest classification models first using 80-20% split, then with leave-one-subject-out cross-validation (LOSO-CV). Additionally, we investigated the significance of each feature and axis. Results: For the 80-20% task, the best performing frequency bands were 0-1 Hz, 0-20 Hz, and 0-40 Hz, which yielded a (MET mean-squared-error, classification accuracy) pair of (0.354, 0.952), (0.367, 0.904), and (0.377, 0.914), respectively. When LOSO-CV was applied, we obtained (1.059, 0.865), (0.681, 0.868), and (0.804, 0.875) for each band, respectively. Additionally, our results revealed that the lateral axis provides the most critical information about cardiorespiratory effect of performed activities. Conclusion: Different SCG components can provide unique and substantial contributions to activity and energy expenditure assessment. Significance: This framework can be leveraged in the design of wearable systems for monitoring the activity and energy expenditure levels, and understanding their relationship with underlying cardiorespiratory parameters.
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    Detection of stride time and stance phase ratio from accelerometer data for gait analysis
    (Institute of Electrical and Electronics Engineers Inc., 2022) N/A; Department of Computer Engineering; N/A; Department of Computer Engineering; Department of Computer Engineering; Department of Electrical and Electronics Engineering; Vural, Atay; Erzin, Engin; Akar, Kardelen; Tokmak, Fadime; Köprücü, Nursena; Emirdağı, Ahmet Rasim; Faculty Member; Faculty Member; Master Student; Student; Student; Student; Department of Computer Engineering; Department of Electrical and Electronics Engineering; Koç University Research Center for Translational Medicine (KUTTAM) / Koç Üniversitesi Translasyonel Tıp Araştırma Merkezi (KUTTAM); N/A; N/A; N/A; N/A; N/A; School of Medicine; College of Engineering; Graduate School of Sciences and Engineering; College of Engineering; College of Engineering; College of Engineering; 182369; 34503; N/A; N/A; N/A; N/A
    Stride time and stance phase ratio are supportive biomarkers used in the diagnosis and treatment of gait disorders and are currently frequently used in research studies. In this study, the 3-axis accelerometer signal, taken from the foot, was denoised by a low-pass FIR (finite impulse response) filter. By using the fundamental frequency analysis the dominant frequency was found and with that frequency an optimal length for a window to be shifted across the whole signal for further purposes. And the turning region was extracted by using the Pearson correlation coefficient with the segments that overlapped by shifting the selected window over the whole signal, after getting the walking segments the stride time parameter is calculated by using a simple peak-picking algorithm. The stance and swing periods of the pseudo-steps, which emerged as a result of the double step time calculation algorithm, were found with the dynamic time warping method, and the ratio of the stance phase in a step to the whole step was calculated as a percentage. The results found were compared with the results of the APDM system, and the mean absolute error rate was calculated as 0.029 s for the stride time and 0.0084 for the stance phase ratio.