@article{5bca1ff885fc4508bfd02315a0ba7d72,
title = "Validation of a sensor-based gait analysis system with a gold-standard motion capture system in patients with parkinson{\textquoteright}s disease",
abstract = "Digital technologies provide the opportunity to analyze gait patterns in patients with Par-kinson{\textquoteright}s Disease using wearable sensors in clinical settings and a home environment. Confirming the technical validity of inertial sensors with a 3D motion capture system is a necessary step for the clinical application of sensor-based gait analysis. Therefore, the objective of this study was to com-pare gait parameters measured by a mobile sensor-based gait analysis system and a motion capture system as the gold standard. Gait parameters of 37 patients were compared between both systems after performing a standardized 5 × 10 m walking test by reliability analysis using intra-class correlation and Bland–Altman plots. Additionally, gait parameters of an age-matched healthy control group (n = 14) were compared to the Parkinson cohort. Gait parameters representing bradykinesia and short steps showed excellent reliability (ICC > 0.96). Shuffling gait parameters reached ICC > 0.82. In a stridewise synchronization, no differences were observed for gait speed, stride length, stride time, relative stance and swing time (p > 0.05). In contrast, heel strike, toe off and toe clearance significantly differed between both systems (p < 0.01). Both gait analysis systems distinguish Parkinson patients from controls. Our results indicate that wearable sensors generate valid gait parameters compared to the motion capture system and can consequently be used for clinically relevant gait recordings in flexible environments.",
keywords = "Inertial sensors, Machine learning algorithm, Parkinson{\textquoteright}s disease, Spatiotemporal gait parameters, Three-dimensional gait analysis, Wearables",
author = "Verena Jakob and Arne K{\"u}derle and Felix Kluge and Jochen Klucken and Eskofier, {Bjoern M.} and J{\"u}rgen Winkler and Martin Winterholler and Heiko Gassner",
note = "Funding Information: The study was funded by institutional research grants from Bavarian Ministry of Economic Affairs and Media, Energy and Technology, Germany (?Mobile GAITLab?, 2017). This work was supported by the Fraunhofer Internal Programs under Grant No. Attract 044-602140 and 044-602150. Further, this work was funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) ? ?Mobility_APP?, grant number 438496663. B.M.E., J.W. and J.K. were (partly) funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation)? SFB 1483?Project-ID 442419336, EmpkinS. H.G., B.M.E., J.W., F.K., A.K. and J.K. are supported by Mobilise-D from the Innovative Medicines Initiative 2 Joint Undertaking under grant agreement no. 820820. Furthermore, B.M.E. gratefully acknowledges the support of the German Research Foundation (DFG) within the framework of the Heisenberg professorship programme (grant number ES 434/8-1). Funding Information: Funding: The study was funded by institutional research grants from Bavarian Ministry of Economic ?ffairs and Media, Energy and Technology, Germany (“Mobile G?ITLab”, 2017). This work was supported by the Fraunhofer Internal Programs under Grant No. Attract 044-602140 and 044-602150. Further, this work was funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) – “Mobility_?PP”, grant number 438496663. B.M.E., J.W. and J.K. were (partly) funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation)— SFB 1483—Project-ID 442419336, EmpkinS. H.G., B.M.E., J.W., F.K., A.K. and J.K. are supported by Mobilise-D from the Innovative Medicines Initiative 2 Joint Undertaking under grant agreement no. 820820. Furthermore, B.M.E. gratefully acknowledges the support of the German Research Foundation (DFG) within the framework of the Heisenberg professorship programme (grant number ES 434/8-1). Publisher Copyright: {\textcopyright} 2021 by the authors. Li-censee MDPI, Basel, Switzerland.",
year = "2021",
month = nov,
day = "1",
doi = "10.3390/s21227680",
language = "English",
volume = "21",
journal = "Sensors",
issn = "1424-8220",
publisher = "Multidisciplinary Digital Publishing Institute (MDPI)",
number = "22",
}