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 - 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 - Zhou, Lin A1 - Fischer, Eric A1 - Tunca, Can A1 - Brahms, Clemens Markus A1 - Ersoy, Cem A1 - Granacher, Urs A1 - Arnrich, Bert T1 - How We Found Our IMU BT - Guidelines to IMU Selection and a Comparison of Seven IMUs for Pervasive Healthcare Applications T2 - Postprints der Universität Potsdam : Reihe der Digital Engineering Fakultät N2 - Inertial measurement units (IMUs) are commonly used for localization or movement tracking in pervasive healthcare-related studies, and gait analysis is one of the most often studied topics using IMUs. The increasing variety of commercially available IMU devices offers convenience by combining the sensor modalities and simplifies the data collection procedures. However, selecting the most suitable IMU device for a certain use case is increasingly challenging. In this study, guidelines for IMU selection are proposed. In particular, seven IMUs were compared in terms of their specifications, data collection procedures, and raw data quality. Data collected from the IMUs were then analyzed by a gait analysis algorithm. The difference in accuracy of the calculated gait parameters between the IMUs could be used to retrace the issues in raw data, such as acceleration range or sensor calibration. Based on our algorithm, we were able to identify the best-suited IMUs for our needs. This study provides an overview of how to select the IMUs based on the area of study with concrete examples, and gives insights into the features of seven commercial IMUs using real data. T3 - Zweitveröffentlichungen der Universität Potsdam : Reihe der Digital Engineering Fakultät - 2 KW - inertial measurement unit KW - pervasive healthcare KW - gait analysis KW - comparison of devices Y1 - 2020 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:517-opus4-481628 IS - 2 ER - TY - JOUR A1 - Zhou, Lin A1 - Fischer, Eric A1 - Tunca, Can A1 - Brahms, Clemens Markus A1 - Ersoy, Cem A1 - Granacher, Urs A1 - Arnrich, Bert T1 - How We Found Our IMU BT - Guidelines to IMU Selection and a Comparison of Seven IMUs for Pervasive Healthcare Applications JF - Sensors N2 - Inertial measurement units (IMUs) are commonly used for localization or movement tracking in pervasive healthcare-related studies, and gait analysis is one of the most often studied topics using IMUs. The increasing variety of commercially available IMU devices offers convenience by combining the sensor modalities and simplifies the data collection procedures. However, selecting the most suitable IMU device for a certain use case is increasingly challenging. In this study, guidelines for IMU selection are proposed. In particular, seven IMUs were compared in terms of their specifications, data collection procedures, and raw data quality. Data collected from the IMUs were then analyzed by a gait analysis algorithm. The difference in accuracy of the calculated gait parameters between the IMUs could be used to retrace the issues in raw data, such as acceleration range or sensor calibration. Based on our algorithm, we were able to identify the best-suited IMUs for our needs. This study provides an overview of how to select the IMUs based on the area of study with concrete examples, and gives insights into the features of seven commercial IMUs using real data. KW - inertial measurement unit KW - pervasive healthcare KW - gait analysis KW - comparison of devices Y1 - 2020 U6 - https://doi.org/10.3390/s20154090 SN - 1424-8220 VL - 20 IS - 15 PB - MDPI CY - Basel ER -