@book{KubanRottaNolteetal.2023, author = {Kuban, Robert and Rotta, Randolf and Nolte, J{\"o}rg and Chromik, Jonas and Beilharz, Jossekin Jakob and Pirl, Lukas and Friedrich, Tobias and Lenzner, Pascal and Weyand, Christopher and Juiz, Carlos and Bermejo, Belen and Sauer, Joao and Coelh, Leandro dos Santos and Najafi, Pejman and P{\"u}nter, Wenzel and Cheng, Feng and Meinel, Christoph and Sidorova, Julia and Lundberg, Lars and Vogel, Thomas and Tran, Chinh and Moser, Irene and Grunske, Lars and Elsaid, Mohamed Esameldin Mohamed and Abbas, Hazem M. and Rula, Anisa and Sejdiu, Gezim and Maurino, Andrea and Schmidt, Christopher and H{\"u}gle, Johannes and Uflacker, Matthias and Nozza, Debora and Messina, Enza and Hoorn, Andr{\´e} van and Frank, Markus and Schulz, Henning and Alhosseini Almodarresi Yasin, Seyed Ali and Nowicki, Marek and Muite, Benson K. and Boysan, Mehmet Can and Bianchi, Federico and Cremaschi, Marco and Moussa, Rim and Abdel-Karim, Benjamin M. and Pfeuffer, Nicolas and Hinz, Oliver and Plauth, Max and Polze, Andreas and Huo, Da and Melo, Gerard de and Mendes Soares, F{\´a}bio and Oliveira, Roberto C{\´e}lio Lim{\~a}o de and Benson, Lawrence and Paul, Fabian and Werling, Christian and Windheuser, Fabian and Stojanovic, Dragan and Djordjevic, Igor and Stojanovic, Natalija and Stojnev Ilic, Aleksandra and Weidmann, Vera and Lowitzki, Leon and Wagner, Markus and Ifa, Abdessatar Ben and Arlos, Patrik and Megia, Ana and Vendrell, Joan and Pfitzner, Bjarne and Redondo, Alberto and R{\´i}os Insua, David and Albert, Justin Amadeus and Zhou, Lin and Arnrich, Bert and Szab{\´o}, Ildik{\´o} and Fodor, Szabina and Ternai, Katalin and Bhowmik, Rajarshi and Campero Durand, Gabriel and Shevchenko, Pavlo and Malysheva, Milena and Prymak, Ivan and Saake, Gunter}, title = {HPI Future SOC Lab - Proceedings 2019}, number = {158}, editor = {Meinel, Christoph and Polze, Andreas and Beins, Karsten and Strotmann, Rolf and Seibold, Ulrich and R{\"o}dszus, Kurt and M{\"u}ller, J{\"u}rgen}, publisher = {Universit{\"a}tsverlag Potsdam}, address = {Potsdam}, isbn = {978-3-86956-564-4}, issn = {1613-5652}, doi = {10.25932/publishup-59791}, url = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-597915}, publisher = {Universit{\"a}t Potsdam}, pages = {xi, 301}, year = {2023}, abstract = {The "HPI Future SOC Lab" is a cooperation of the Hasso Plattner Institute (HPI) and industry partners. Its mission is to enable and promote exchange and interaction between the research community and the industry partners. The HPI Future SOC Lab provides researchers with free of charge access to a complete infrastructure of state of the art hard and software. This infrastructure includes components, which might be too expensive for an ordinary research environment, such as servers with up to 64 cores and 2 TB main memory. The offerings address researchers particularly from but not limited to the areas of computer science and business information systems. Main areas of research include cloud computing, parallelization, and In-Memory technologies. This technical report presents results of research projects executed in 2019. Selected projects have presented their results on April 9th and November 12th 2019 at the Future SOC Lab Day events.}, language = {en} } @misc{AlbertOwolabiGebeletal.2020, author = {Albert, Justin Amadeus and Owolabi, Victor and Gebel, Arnd and Brahms, Clemens Markus and Granacher, Urs and Arnrich, Bert}, title = {Evaluation of the Pose Tracking Performance of the Azure Kinect and Kinect v2 for Gait Analysis in Comparison with a Gold Standard}, series = {Postprints der Universit{\"a}t Potsdam : Reihe der Digital Engineering Fakult{\"a}t}, journal = {Postprints der Universit{\"a}t Potsdam : Reihe der Digital Engineering Fakult{\"a}t}, number = {3}, doi = {10.25932/publishup-48413}, url = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-484130}, pages = {24}, year = {2020}, abstract = {Gait analysis is an important tool for the early detection of neurological diseases and for the assessment of risk of falling in elderly people. The availability of low-cost camera hardware on the market today and recent advances in Machine Learning enable a wide range of clinical and health-related applications, such as patient monitoring or exercise recognition at home. In this study, we evaluated the motion tracking performance of the latest generation of the Microsoft Kinect camera, Azure Kinect, compared to its predecessor Kinect v2 in terms of treadmill walking using a gold standard Vicon multi-camera motion capturing system and the 39 marker Plug-in Gait model. Five young and healthy subjects walked on a treadmill at three different velocities while data were recorded simultaneously with all three camera systems. An easy-to-administer camera calibration method developed here was used to spatially align the 3D skeleton data from both Kinect cameras and the Vicon system. With this calibration, the spatial agreement of joint positions between the two Kinect cameras and the reference system was evaluated. In addition, we compared the accuracy of certain spatio-temporal gait parameters, i.e., step length, step time, step width, and stride time calculated from the Kinect data, with the gold standard system. Our results showed that the improved hardware and the motion tracking algorithm of the Azure Kinect camera led to a significantly higher accuracy of the spatial gait parameters than the predecessor Kinect v2, while no significant differences were found between the temporal parameters. Furthermore, we explain in detail how this experimental setup could be used to continuously monitor the progress during gait rehabilitation in older people.}, language = {en} } @article{AlbertOwolabiGebeletal.2020, author = {Albert, Justin Amadeus and Owolabi, Victor and Gebel, Arnd and Brahms, Clemens Markus and Granacher, Urs and Arnrich, Bert}, title = {Evaluation of the Pose Tracking Performance of the Azure Kinect and Kinect v2 for Gait Analysis in Comparison with a Gold Standard}, series = {Sensors}, volume = {20}, journal = {Sensors}, number = {18}, publisher = {MDPI}, address = {Basel}, issn = {1424-8220}, doi = {10.3390/s20185104}, pages = {22}, year = {2020}, abstract = {Gait analysis is an important tool for the early detection of neurological diseases and for the assessment of risk of falling in elderly people. The availability of low-cost camera hardware on the market today and recent advances in Machine Learning enable a wide range of clinical and health-related applications, such as patient monitoring or exercise recognition at home. In this study, we evaluated the motion tracking performance of the latest generation of the Microsoft Kinect camera, Azure Kinect, compared to its predecessor Kinect v2 in terms of treadmill walking using a gold standard Vicon multi-camera motion capturing system and the 39 marker Plug-in Gait model. Five young and healthy subjects walked on a treadmill at three different velocities while data were recorded simultaneously with all three camera systems. An easy-to-administer camera calibration method developed here was used to spatially align the 3D skeleton data from both Kinect cameras and the Vicon system. With this calibration, the spatial agreement of joint positions between the two Kinect cameras and the reference system was evaluated. In addition, we compared the accuracy of certain spatio-temporal gait parameters, i.e., step length, step time, step width, and stride time calculated from the Kinect data, with the gold standard system. Our results showed that the improved hardware and the motion tracking algorithm of the Azure Kinect camera led to a significantly higher accuracy of the spatial gait parameters than the predecessor Kinect v2, while no significant differences were found between the temporal parameters. Furthermore, we explain in detail how this experimental setup could be used to continuously monitor the progress during gait rehabilitation in older people.}, language = {en} }