A public dataset of overground walking kinetics in healthy adult individuals on different sessions within one day

The dataset comprises raw kinetic data of 42 healthy subjects (22 female, 20 male; M age: 25.6 years, SD 6.1; M body height: 1.72 m, SD 0.09; M body mass: 66.9 kg, SD 10.7) during overground walking. All subjects were without gait pathology and free of lower extremity pain or injuries. The file 'GRF_META_DATA.csv' contains additional meta information for each subject and session, including: "SUBJECT_ID" [number] "SESSION_ID" [number] "GENDER" [female ; male] "AGE" [years] "BODY_SIZE" [m] "BODY_MASS" [kg] The six ground reaction force data files are organized according to the following naming convention: “GRF-type-processing-side.csv”. The type denotes, whether the file holds the data of the vertical (“F_V"), anterior-posterior (“F_AP"), medio-lateral (“F_ML") ground reaction force time-series. Each of the “GRF-type-processing-side.csv” files is structured as a matrix with N rows and M columns. Each row holds the data of one trial. The first column identifies the subject (“SUBJECT_ID”), the second column the number of the recording session (“SESSION_ID”), and the third column the gait velocity of the trial (“VELOCITY”). Note that due to the non-time-normalized nature of the data and the resulting different vector lengths in the “RAW” files, non-available numbers have been replaced by “NaN” to maintain a constant matrix dimension. When using (any part) of this dataset, please cite this dataset and the original article: Burdack, J., Horst, F., Giesselbach, S., Hassan, I., Daffner, S., & Schöllhorn, W. I. (2020). A public dataset of overground walking kinetics in healthy adult individuals on different sessions within one day. Mendeley Data, v2. http://dx.doi.org/10.17632/y55wfcsrhz.1 Burdack, J., Horst, F., Giesselbach, S., Hassan, I., Daffner, S., & Schöllhorn, W. I. (2020). Systematic comparison of the influence of different data preprocessing methods on the performance of gait classifications using machine learning. Frontiers in Bioengineering and Biotechnology, 8, 260. https://doi.org/10.3389/fbioe.2020.00260 Please feel free to send us your technical questions, requests and bug reports by email: horst@uni-mainz.de