20.500.12556/RUL-131859 Robust stride segmentation of inertial signals based on local cyclicity estimation A novel approach for stride segmentation, gait sequence extraction, and gait event detection for inertial signals is presented. The approach operates by combining different local cyclicity estimators and sensor channels, and can additionally employ a priori knowledge on the fiducial points of gait events. The approach is universal as it can work on signals acquired by different inertial measurement unit (IMU) sensor types, is template-free, and operates unsupervised. A thorough evaluation was performed with two datasets: our own collected FRIgait dataset available for open use, containing long-term inertial measurements collected from 57 subjects using smartphones within the span of more than one year, and an FAU eGait dataset containing inertial data from shoe-mounted sensors collected from three cohorts of subjects: healthy, geriatric, and Parkinson’s disease patients. The evaluation was performed in controlled and uncontrolled conditions. When compared to the ground truth of the labelled FRIgait and eGait datasets, the results of our evaluation revealed the high robustness, efficiency (F-measure of about 98%), and accuracy (mean absolute error MAE in about the range of one sample) of the proposed approach. Based on these results, we conclude that the proposed approach shows great potential for its applicability in procedures and algorithms for movement analysis. inertial sensors stride segmentation gait assessment inertial signals biomedical signal processing inercialni senzorji segmentacija korakov ocenjevanje hoje inercijski signali obdelava biomedicinskih signalov true false true Angleški jezik Slovenski jezik Članek v reviji 2021-10-05 08:19:17 2021-10-05 08:19:18 2022-09-10 04:14:13 0000-00-00 00:00:00 2018 0 0 24 str. iss. 4, art. 1091 Vol. 18 Apr. 2018 0000-00-00 Zaloznikova Objavljeno NiDoloceno 0000-00-00 0000-00-00 0000-00-00 004.93:621.391 1424-8220 10.3390/s18041091 1537764547 RAZ_Sprager_Sebastijan_2018.pdf RAZ_Sprager_Sebastijan_2018.pdf 1 2F057048A652EBFB2CF98DF53E0F632E 609714dcfcf0a4afda0b975d880f784dac10667597cc2ddbdcc998696521fe60 39decbb2-25a4-11ec-a523-00155dcfd717 https://repozitorij.uni-lj.si/Dokument.php?lang=slv&id=149282 http://www.mdpi.com/1424-8220/18/4/1091 1 e1a54f59-25a3-11ec-a523-00155dcfd717 https://repozitorij.uni-lj.si/Dokument.php?lang=slv&id=149281 Fakulteta za računalništvo in informatiko 0 0 0