Refine
Has Fulltext
- no (184)
Year of publication
Document Type
- Article (184) (remove)
Language
- English (184) (remove)
Is part of the Bibliography
- yes (184)
Keywords
- football (15)
- balance (10)
- youth (10)
- adolescents (9)
- resistance training (9)
- stretch-shortening cycle (9)
- athletic performance (8)
- exercise (8)
- training (8)
- strength training (7)
Institute
- Department Sport- und Gesundheitswissenschaften (102)
- Strukturbereich Kognitionswissenschaften (53)
- Extern (16)
- Department Psychologie (6)
- Humanwissenschaftliche Fakultät (6)
- Hasso-Plattner-Institut für Digital Engineering GmbH (3)
- Fakultät für Gesundheitswissenschaften (2)
- Fachgruppe Soziologie (1)
- Hasso-Plattner-Institut für Digital Engineering gGmbH (1)
TRIPOD
(2021)
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.
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.
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.
DUO-GAIT
(2023)
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.