@inproceedings{KutznerTraviesoGonzalezBoenningeretal., author = {Kutzner, Tobias and Travieso-Gonz{\´a}lez, Carlos M. and B{\"o}nninger, Ingrid and Alonso, Jes{\´u}s B. and V{\´a}squez, Jose Luis}, title = {Writer identification on mobile device based on handwritten}, series = {47th International Carnahan Conference on Security Technology (ICCST), 2013, 8 - 11 Oct. 2013, Medellin}, booktitle = {47th International Carnahan Conference on Security Technology (ICCST), 2013, 8 - 11 Oct. 2013, Medellin}, publisher = {IEEE}, address = {Piscataway, NJ}, isbn = {978-1-4799-0889-9}, doi = {10.1109/CCST.2013.6922063}, pages = {1 -- 5}, language = {en} } @inproceedings{KutznerDietzeBoenningeretal., author = {Kutzner, Tobias and Dietze, Mario and B{\"o}nninger, Ingrid and Travieso-Gonz{\´a}lez, Carlos M. and Singh, Anushikha and Dutta, Malay Kishore}, title = {Online Handwriting Verification with Safe Password and Increasing Number of Features}, series = {2016 Signal Processing and Integrated Networks (SPIN), 11-12 Feb. 2016}, booktitle = {2016 Signal Processing and Integrated Networks (SPIN), 11-12 Feb. 2016}, publisher = {IEEE}, address = {Piscataway, NJ}, isbn = {978-1-4673-9197-9}, doi = {10.1109/SPIN.2016.7566777}, pages = {650 -- 655}, language = {en} } @inproceedings{MachadoMolinaBoenningerDuttaetal., author = {Machado-Molina, Miguel and B{\"o}nninger, Ingrid and Dutta, Malay Kishore and Kutzner, Tobias and Travieso-Gonz{\´a}lez, Carlos M.}, title = {Gait-based recognition of humans using Kinect camera}, series = {Recent Advances in Computer Engineering, Communications and Information Technology, 8th International Conference on Communications and Information Technology (CIT '14), Tenerife, 2014}, booktitle = {Recent Advances in Computer Engineering, Communications and Information Technology, 8th International Conference on Communications and Information Technology (CIT '14), Tenerife, 2014}, publisher = {WSEAS Press}, address = {Athens [u.a.]}, isbn = {978-960-474-361-2}, pages = {63 -- 71}, language = {en} } @inproceedings{KutznerBoenningerTraviesoGonzalez, author = {Kutzner, Tobias and B{\"o}nninger, Ingrid and Travieso-Gonz{\´a}lez, Carlos M.}, title = {Nutzer-Authentifizierung mittels handschriftlichen Passworten auf Mobil-Ger{\"a}ten}, series = {12. Wissenschaftstage der Hochschule Lausitz (FH) - University of Applied Sciences, 2012}, booktitle = {12. Wissenschaftstage der Hochschule Lausitz (FH) - University of Applied Sciences, 2012}, abstract = {Ziel des Beitrages ist es, das Potential von handschriftlichen Passworten f{\"u}r die Erh{\"o}- hung der Sicherheit beim Zugang zu Systemen zu untersuchen. Wir schlagen eine Mo- bilger{\"a}t-Server-L{\"o}sung mit verteilter Vorverarbeitung vor. Untersucht werden die Erfolgsraten der korrekten Schreibererkennung bei der Verwen- dung nur eines Wortes, des Passwortes, der Reduktion der Anzahl der Passworte, die zum Trainieren des Klassifikationsalgorithmus verwendet werden, und der Reduktion der Parameter. Der implementierte Prototyp f{\"u}hrt die Segmentierung auf dem Mobilger{\"a}t aus. Merkmalsextraktion und Klassifikation laufen als Server-Applikationen. Das Tool WEKA wird f{\"u}r die Ermittlung des geeignetsten Algorithmus f{\"u}r die Erkennung des Schreibers des Passwortes verwendet. Die Analysen werden mit 280 handschriftlichen Passworten durchgef{\"u}hrt. Davon sind 176 Originale und 104 F{\"a}lschungen. Als Mobilge- r{\"a}te wurden ein HTC Desire mit Android 2.2 und ein Samsung Galaxy Ace mit Android 2.3.3 verwendet. Das vorgeschlagene System erkennt mit der Wahrscheinlichkeit von 96,87\% den Schreiber des Passwortes, die False Acceptance Rate betr{\"a}gt 12,5\%, wenn acht Passworte jedes Schreibers f{\"u}r das Klassifikationsmodell verwendet werden.}, language = {de} } @inproceedings{KutznerBoenningerTraviesoGonzalezetal., author = {Kutzner, Tobias and B{\"o}nninger, Ingrid and Travieso-Gonz{\´a}lez, Carlos M. and Dutta, Malay Kishore and Singh, Anushikha}, title = {Study of Long-Term Quality of Online Signature Verification Systems}, series = {IEEE 2nd International Conference on Communication, Control and Intelligent Systems (CCIS), 18-20 Nov. 2016}, booktitle = {IEEE 2nd International Conference on Communication, Control and Intelligent Systems (CCIS), 18-20 Nov. 2016}, publisher = {IEEE}, address = {Piscataway, NJ}, isbn = {978-1-5090-3210-5}, doi = {10.1109/CCIntelS.2016.7878206}, pages = {85 -- 89}, language = {en} } @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{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} }