@misc{SteinmetzerBoenningerPriwitzeretal., author = {Steinmetzer, Tobias and B{\"o}nninger, Ingrid and Priwitzer, Barbara and Reinhardt, Fritjof and Reckhardt, Markus Christoph and Erk, Dorela and Travieso-Gonz{\´a}lez, Carlos M.}, title = {Clustering of Human Gait with Parkinson's Disease by Using Dynamic Time Warping}, series = {IEEE International Work Conference on Bioinspired Intelligence (IWOBI)}, volume = {2018}, journal = {IEEE International Work Conference on Bioinspired Intelligence (IWOBI)}, doi = {10.1109/IWOBI.2018.8464203}, pages = {1 -- 6}, abstract = {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.}, language = {en} } @misc{ReinhardtReichmannAltmannetal., author = {Reinhardt, Fritjof and Reichmann, Heinz and Altmann, Olaf and Reckhardt, Markus Christoph and Kr{\"o}nert, Tabea and Suchfort, Maik and Vogel, Steffen and Nitzsche, Nico}, title = {Klinische und paraklinische Schlaganfallverlaufscharakteristik zur Prozessoptimierung auf der Stroke Unit}, series = {Aktuelle Neurologie}, volume = {41}, journal = {Aktuelle Neurologie}, number = {2}, issn = {1431-4886}, doi = {10.1055/s-0033-1360086}, pages = {90 -- 99}, language = {de} } @misc{SteinmetzerReckhardtReinhardtetal., author = {Steinmetzer, Tobias and Reckhardt, Markus Christoph and Reinhardt, Fritjof and Erk, Dorela and Travieso-Gonz{\´a}lez, Carlos M.}, title = {Comparison of algorithms and classifiers for stride detection using wearables}, series = {Neural Computing and Applications}, volume = {32(2020)}, journal = {Neural Computing and Applications}, number = {24}, issn = {1433-3058}, doi = {10.1007/s00521-019-04384-6}, pages = {17857 -- 17868}, abstract = {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.}, language = {en} }