TY  - GEN
A1  - Blaesius, Thomas
A1  - Eube, Jan
A1  - Feldtkeller, Thomas
A1  - Friedrich, Tobias
A1  - Krejca, Martin Stefan
A1  - Lagodzinski, Gregor J. A.
A1  - Rothenberger, Ralf
A1  - Severin, Julius
A1  - Sommer, Fabian
A1  - Trautmann, Justin
T1  - Memory-restricted Routing With Tiled Map Data
T2  - 2018 IEEE International Conference on Systems, Man, and Cybernetics (SMC)
N2  - Modern routing algorithms reduce query time by depending heavily on preprocessed data. The recently developed Navigation Data Standard (NDS) enforces a separation between algorithms and map data, rendering preprocessing inapplicable. Furthermore, map data is partitioned into tiles with respect to their geographic coordinates. With the limited memory found in portable devices, the number of tiles loaded becomes the major factor for run time. We study routing under these restrictions and present new algorithms as well as empirical evaluations. Our results show that, on average, the most efficient algorithm presented uses more than 20 times fewer tile loads than a normal A*.
Y1  - 2018
SN  - 978-1-5386-6650-0
U6  - https://doi.org/10.1109/SMC.2018.00567
SN  - 1062-922X
SP  - 3347
EP  - 3354
PB  - IEEE
CY  - New York
ER  - 
TY  - JOUR
A1  - Trautmann, Justin
A1  - Zhou, Lin
A1  - Brahms, Clemens Markus
A1  - Tunca, Can
A1  - Ersoy, Cem
A1  - Granacher, Urs
A1  - Arnrich, Bert
T1  - TRIPOD
BT  - A treadmill walking dataset with IMU, pressure-distribution  and photoelectric data for gait analysis
JF  - Data : open access ʻData in scienceʼ journal
N2  - Inertial measurement units (IMUs) enable easy to operate and low-cost data recording for gait analysis. When combined with treadmill walking, a large number of steps can be collected in a controlled environment without the need of a dedicated gait analysis laboratory. In order to evaluate existing and novel IMU-based gait analysis algorithms for treadmill walking, a reference dataset that includes IMU data as well as reliable ground truth measurements for multiple participants and walking speeds is needed. This article provides a reference dataset consisting of 15 healthy young adults who walked on a treadmill at three different speeds. Data were acquired using seven IMUs placed on the lower body, two different reference systems (Zebris FDMT-HQ and OptoGait), and two RGB cameras. Additionally, in order to validate an existing IMU-based gait analysis algorithm using the dataset, an adaptable modular data analysis pipeline was built. Our results show agreement between the pressure-sensitive Zebris and the photoelectric OptoGait system (r = 0.99), demonstrating the quality of our reference data. As a use case, the performance of an algorithm originally designed for overground walking was tested on treadmill data using the data pipeline. The accuracy of stride length and stride time estimations was comparable to that reported in other studies with overground data, indicating that the algorithm is equally applicable to treadmill data. The Python source code of the data pipeline is publicly available, and the dataset will be provided by the authors upon request, enabling future evaluations of IMU gait analysis algorithms without the need of recording new data.
KW  - inertial measurement unit
KW  - gait analysis algorithm
KW  - OptoGait
KW  - Zebris
KW  - data pipeline
KW  - public dataset
Y1  - 2021
U6  - https://doi.org/10.3390/data6090095
SN  - 2306-5729
VL  - 6
IS  - 9
PB  - MDPI
CY  - Basel
ER  - 
TY  - GEN
A1  - Trautmann, Justin
A1  - Zhou, Lin
A1  - Brahms, Clemens Markus
A1  - Tunca, Can
A1  - Ersoy, Cem
A1  - Granacher, Urs
A1  - Arnrich, Bert
T1  - TRIPOD - A Treadmill Walking Dataset with IMU, Pressure-distribution  and Photoelectric Data for Gait Analysis
T2  - Postprints der Universität Potsdam : Reihe der Digital Engineering Fakultät
N2  - Inertial measurement units (IMUs) enable easy to operate and low-cost data recording for gait analysis. When combined with treadmill walking, a large number of steps can be collected in a controlled environment without the need of a dedicated gait analysis laboratory. In order to evaluate existing and novel IMU-based gait analysis algorithms for treadmill walking, a reference dataset that includes IMU data as well as reliable ground truth measurements for multiple participants and walking speeds is needed. This article provides a reference dataset consisting of 15 healthy young adults who walked on a treadmill at three different speeds. Data were acquired using seven IMUs placed on the lower body, two different reference systems (Zebris FDMT-HQ and OptoGait), and two RGB cameras. Additionally, in order to validate an existing IMU-based gait analysis algorithm using the dataset, an adaptable modular data analysis pipeline was built. Our results show agreement between the pressure-sensitive Zebris and the photoelectric OptoGait system (r = 0.99), demonstrating the quality of our reference data. As a use case, the performance of an algorithm originally designed for overground walking was tested on treadmill data using the data pipeline. The accuracy of stride length and stride time estimations was comparable to that reported in other studies with overground data, indicating that the algorithm is equally applicable to treadmill data. The Python source code of the data pipeline is publicly available, and the dataset will be provided by the authors upon request, enabling future evaluations of IMU gait analysis algorithms without the need of recording new data.
T3  - Zweitveröffentlichungen der Universität Potsdam : Reihe der Digital Engineering Fakultät - 6 
KW  - inertial measurement unit
KW  - gait analysis algorithm
KW  - OptoGait
KW  - Zebris
KW  - data pipeline
KW  - public dataset
Y1  - 2021
U6  - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:517-opus4-522027
IS  - 6
ER  -