TY - GEN A1 - Albert, Justin Amadeus A1 - Owolabi, Victor A1 - Gebel, Arnd A1 - Brahms, Clemens Markus A1 - Granacher, Urs A1 - Arnrich, Bert T1 - Evaluation of the Pose Tracking Performance of the Azure Kinect and Kinect v2 for Gait Analysis in Comparison with a Gold Standard BT - A Pilot Study T2 - Postprints der Universität Potsdam : Reihe der Digital Engineering Fakultät N2 - 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. T3 - Zweitveröffentlichungen der Universität Potsdam : Reihe der Digital Engineering Fakultät - 3 KW - motion capture KW - evaluation KW - human motion KW - RGB-D cameras KW - digital health Y1 - 2020 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:517-opus4-484130 IS - 3 ER - TY - JOUR A1 - Albert, Justin Amadeus A1 - Owolabi, Victor A1 - Gebel, Arnd A1 - Brahms, Clemens Markus A1 - Granacher, Urs A1 - Arnrich, Bert T1 - Evaluation of the Pose Tracking Performance of the Azure Kinect and Kinect v2 for Gait Analysis in Comparison with a Gold Standard BT - A Pilot Study JF - Sensors N2 - 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. KW - motion capture KW - evaluation KW - human motion KW - RGB-D cameras KW - digital health Y1 - 2020 U6 - https://doi.org/10.3390/s20185104 SN - 1424-8220 VL - 20 IS - 18 PB - MDPI CY - Basel 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 - 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 - TY - JOUR A1 - Brahms, Markus A1 - Heinzel, Stephan A1 - Rapp, Michael A. A1 - Mückstein, Marie A1 - Hortobágyi, Tibor A1 - Stelzel, Christine A1 - Granacher, Urs T1 - The acute effects of mental fatigue on balance performance in healthy young and older adults – A systematic review and meta-analysis JF - Acta Psychologica N2 - Cognitive resources contribute to balance control. There is evidence that mental fatigue reduces cognitive resources and impairs balance performance, particularly in older adults and when balance tasks are complex, for example when trying to walk or stand while concurrently performing a secondary cognitive task. We conducted a systematic literature search in PubMed (MEDLINE), Web of Science and Google Scholar to identify eligible studies and performed a random effects meta-analysis to quantify the effects of experimentally induced mental fatigue on balance performance in healthy adults. Subgroup analyses were computed for age (healthy young vs. healthy older adults) and balance task complexity (balance tasks with high complexity vs. balance tasks with low complexity) to examine the moderating effects of these factors on fatigue-mediated balance performance. We identified 7 eligible studies with 9 study groups and 206 participants. Analysis revealed that performing a prolonged cognitive task had a small but significant effect (SMDwm = −0.38) on subsequent balance performance in healthy young and older adults. However, age- and task-related differences in balance responses to fatigue could not be confirmed statistically. Overall, aggregation of the available literature indicates that mental fatigue generally reduces balance in healthy adults. However, interactions between cognitive resource reduction, aging and balance task complexity remain elusive. KW - Cognitive fatigue KW - Exertion KW - Tiredness KW - Postural control KW - Gait KW - Sway Y1 - 2022 U6 - https://doi.org/10.1016/j.actpsy.2022.103540 SN - 1873-6297 VL - 225 SP - 1 EP - 13 PB - Elsevier CY - Amsterdam ER - TY - JOUR A1 - Sandau, Ingo A1 - Granacher, Urs T1 - Long-term monitoring of training load, force-velocity profile, and Performance in elite weightlifters: a case series with two male Olympic athletes JF - Journal of strength and conditioning research : the research journal of the NSCA N2 - The aim of this case series approach was to analyze weekly changes in force-velocity relationship (FvR) parameters ((v) over bar, (F) over bar (0), (P) over bar (max)) and theoretical snatch performance (snatchth) assessed through a specific snatch pull test in preparation of the European and World Championships in 2 male elite weightlifters. A second aim was to examine associations of training load (volume, volume load, average load), barbell -, and snatchth over a period of 2 macrocycles in preparation of the same competitions. FvR-parameters, snatchth, training load data, and body mass were assessed weekly over 40 weeks. Using the smallest real difference approach, significant (p <= 0.05) decreases in (v) over bar (0) and increases in (v) over bar, (F) over bar (0), (P) over bar (max), and snatchth were found within macrocycles. However, the large significant loss in body mass (approximate to 11%) in athlete 1 during macrocycle 2 represents most likely a main factor for diminished (P) over bar (max), and snatchth in macrocycle 2. Based on cross-correlation analyses, barbell FvR-parameters and snatchth were significantly (p <= 0.05) associated with maximal strength, muscle power, and speed training load variables. Moderate correlations (0.31-0.47) were found between training load and (P) over bar (max) and snatchth in athlete 2. It can be concluded that the applied training loads elicits improvements in <(P)(max) and snatchth because the athlete approached the main competitions. However, because of the large loss in body mass, the relations between training load and barbell FvR-parameters and snatchth were less clear in athlete 1. It seems that a loss in body mass as a result of a change in bodyweight category mitigates <(P)over bar>(max) development during the macrocycle and hindered to reach peak snatchth at the main competitions. KW - snatch KW - time series analysis KW - power KW - maximal strength KW - speed Y1 - 2022 U6 - https://doi.org/10.1519/JSC.0000000000004228 SN - 1064-8011 SN - 1533-4287 VL - 36 IS - 12 SP - 3446 EP - 3455 PB - Lippincott Williams & Wilkins CY - Philadelphia, Pa. ER - TY - JOUR A1 - Zhou, Lin A1 - Fischer, Eric A1 - Brahms, Clemens Markus A1 - Granacher, Urs A1 - Arnrich, Bert T1 - DUO-GAIT BT - a gait dataset for walking under dual-task and fatigue conditions with inertial measurement units JF - Scientific data N2 - In recent years, there has been a growing interest in developing and evaluating gait analysis algorithms based on inertial measurement unit (IMU) data, which has important implications, including sports, assessment of diseases, and rehabilitation. Multi-tasking and physical fatigue are two relevant aspects of daily life gait monitoring, but there is a lack of publicly available datasets to support the development and testing of methods using a mobile IMU setup. We present a dataset consisting of 6-minute walks under single- (only walking) and dual-task (walking while performing a cognitive task) conditions in unfatigued and fatigued states from sixteen healthy adults. Especially, nine IMUs were placed on the head, chest, lower back, wrists, legs, and feet to record under each of the above-mentioned conditions. The dataset also includes a rich set of spatio-temporal gait parameters that capture the aspects of pace, symmetry, and variability, as well as additional study-related information to support further analysis. This dataset can serve as a foundation for future research on gait monitoring in free-living environments. Y1 - 2023 U6 - https://doi.org/10.1038/s41597-023-02391-w SN - 2052-4463 VL - 10 IS - 1 PB - Nature Publ. Group CY - London ER -