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Department: search.filters.department.Department of Computer EngineeringSubject: search.filters.subject.Neurosciences

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    PublicationOpen Access
    Computational modeling of organisational learning by self-modeling networks
    (Elsevier, 2022) Treur, Jan; Roelofsma, Peter H. M. P.; Department of Computer Engineering; Canbaloğlu, Gülay; Department of Computer Engineering; Graduate School of Sciences and Engineering
    Within organisational learning literature, mental models are considered a vehicle for both individual learning and organizational learning. By learning individual mental models (and making them explicit), a basis for formation of shared mental models for the level of the organization is created, which after its formation can then be adopted by individuals. This provides mechanisms for organizational learning. These mechanisms have been used as a basis for an adaptive computational network model. The model is illustrated by a not too complex but realistic case study.
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    PublicationMetadata only
    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.