TY - GEN A1 - Steinmetzer, Tobias A1 - Bönninger, Ingrid A1 - Priwitzer, Barbara A1 - Reinhardt, Fritjof A1 - Reckhardt, Markus Christoph A1 - Erk, Dorela A1 - Travieso-González, Carlos M. T1 - Clustering of Human Gait with Parkinson's Disease by Using Dynamic Time Warping T2 - IEEE International Work Conference on Bioinspired Intelligence (IWOBI) N2 - We present a new method for detecting gait disorders according to their stadium using cluster methods for sensor data. 21 healthy and 18 Parkinson subjects performed the Time Up and Go test. The time series were segmented into separate steps. For the analysis the horizontal acceleration measured by a mobile sensor system was considered. We used Dynamic Time Warping and Hierarchical Custering to distinguish the stadiums. A specificity of 92% was achieved. KW - DTW KW - clustering KW - parkinson disease KW - time series Y1 - 2018 U6 - https://doi.org/10.1109/IWOBI.2018.8464203 VL - 2018 SP - 1 EP - 6 ER - TY - GEN A1 - Reinhardt, Fritjof A1 - Reichmann, Heinz A1 - Altmann, Olaf A1 - Reckhardt, Markus Christoph A1 - Krönert, Tabea A1 - Suchfort, Maik A1 - Vogel, Steffen A1 - Nitzsche, Nico T1 - Klinische und paraklinische Schlaganfallverlaufscharakteristik zur Prozessoptimierung auf der Stroke Unit T2 - Aktuelle Neurologie Y1 - 2014 U6 - https://doi.org/10.1055/s-0033-1360086 SN - 1431-4886 SN - 0302-4350 VL - 41 IS - 2 SP - 90 EP - 99 ER - TY - GEN A1 - Steinmetzer, Tobias A1 - Reckhardt, Markus Christoph A1 - Reinhardt, Fritjof A1 - Erk, Dorela A1 - Travieso-González, Carlos M. T1 - Comparison of algorithms and classifiers for stride detection using wearables T2 - Neural Computing and Applications N2 - Sensor-based systems for diagnosis or therapy support of motor dysfunctions need methodologies of automatically stride detection from movement sequences. In this proposal, we developed a stride detection system for daily life use. We compared mostly used algorithms min–max patterns, dynamic time warping, convolutional neural networks (CNN), and automatic framing using two data sets of 32 healthy and 28 Parkinson’s disease (PD) persons. We developed an insole with force and IMU sensors to record the gait data. The PD patients carried out the standardized time up and go test, and the healthy persons a daily life activities test (walking, sitting, standing, ascending and descending stairs). As an automatically stride detection process for daily life use, we propose a first stride detection using automatic framing, and after normalization and resampling data a CNN is used. A F1-score of 0.938 (recall 0.968, precision 0.910) for time up and go test and of 0.944 (recall 0.992, precision 0.901) for daily life activities test were obtained for CNN. Compared to the other detection methods, up to 6% F-measure improvement was shown. KW - Stride detection KW - Gait analysis KW - Inertial sensors KW - Parkinson’s disease KW - Validation Dynamic time warping KW - Time up and go test KW - Convolutional neural networks Y1 - 2019 U6 - https://doi.org/10.1007/s00521-019-04384-6 SN - 1433-3058 VL - 32(2020) IS - 24 SP - 17857 EP - 17868 ER -