@misc{KutznerTraviesoGonzalezBoenningeretal., author = {Kutzner, Tobias and Travieso-Gonz{\´a}lez, Carlos M. and B{\"o}nninger, Ingrid and Pazmi{\~n}o-Zapatier, Carlos F. and Plath, Wolf-Dietrich and Gebhard, Matthias}, title = {Writer Identification Using Handwritten Cursive Texts and Single Character Words}, series = {Electronics}, volume = {8}, journal = {Electronics}, number = {4}, issn = {2079-9292}, doi = {10.3390/electronics8040391}, pages = {25}, abstract = {One of the biometric methods in authentication systems is the writer verification/identification using password handwriting. The main objective of this paper is to present a robust writer verification system by using cursive texts as well as block letter words. To evaluate the system, two datasets have been used. One of them is called Secure Password DB 150, which is composed of 150 users with 18 samples of single character words per user. Another dataset is public and called IAM online handwriting database, and it is composed of 220 users of cursive text samples. Each sample has been defined by a set of features, composed of 67 geometrical, statistical, and temporal features. In order to get more discriminative information, two feature reduction methods have been applied, Fisher Score and Info Gain Attribute Evaluation. Finally, the classification system has been implemented by hold-out cross validation and k-folds cross validation strategies for three different classifiers, K-NN, Na{\"i}ve Bayes and Bayes Net classifiers. Besides, it has been applied for verification and identification approaches. The best results of 95.38\% correct classification are achieved by using the k-nearest neighbor classifier for single character DB. A feature reduction by Info Gain Attribute Evaluation improves the results for Na{\"i}ve Bayes Classifier to 98.34\% for IAM online handwriting DB. It is concluded that the set of features and its reduction are a strong selection for the based-password handwritten writer identification in comparison with the state-of-the-art.}, 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} }