20.500.12556/RUL-132338 Hidden Markov model-based smart annotation for benchmark cyclic activity recognition database using wearables Activity monitoring using wearables is becoming ubiquitous, although accurate cycle level analysis, such as step-counting and gait analysis, are limited by a lack of realistic and labeled datasets. The effort required to obtain and annotate such datasets is massive, therefore we propose a smart annotation pipeline which reduces the number of events needing manual adjustment to 14%. For scenarios dominated by walking, this annotation effort is as low as 8%. The pipeline consists of three smart annotation approaches, namely edge detection of the pressure data, local cyclicity estimation, and iteratively trained hierarchical hidden Markov models. Using this pipeline, we have collected and labeled a dataset with over 150,000 labeled cycles, each with 2 phases, from 80 subjects, which we have made publicly available. The dataset consists of 12 different task-driven activities, 10 of which are cyclic. These activities include not only straight and steady-state motions, but also transitions, different ranges of bouts, and changing directions. Each participant wore 5 synchronized inertial measurement units (IMUs) on the wrists, shoes, and in a pocket, as well as pressure insoles and video. We believe that this dataset and smart annotation pipeline are a good basis for creating a benchmark dataset for validation of other semi- and unsupervised algorithms. activity recognition benchmark database gait analysis inertial measurement unit gait phases cyclic activities home monitoring smart annotation semi-supervised learning prepoznavanje podatkovne baze true false true Angleški jezik Slovenski jezik Članek v reviji 2021-10-22 07:59:34 2021-10-22 07:59:36 2022-09-12 04:11:58 0000-00-00 00:00:00 2019 0 0 21 str. iss. 8, art. 1820 Vol. 19 2019 0000-00-00 Zaloznikova Objavljeno NiDoloceno 0000-00-00 0000-00-00 0000-00-00 004.5 1424-8220 10.3390/s19081820 31415043 RAZ_Martindale_Christine_F._2019.pdf RAZ_Martindale_Christine_F._2019.pdf 1 5060A675F2B14102F73DC144A5FB802E 5364564b00cd584a15d2321c7be18f52e770df7569ade8ec81f7709e10865dff cf97d33a-32fd-11ec-abdb-00155dcfd717 https://repozitorij.uni-lj.si/Dokument.php?lang=slv&id=149951 https://www.mdpi.com/1424-8220/19/8/1820 0 c225ed53-32fd-11ec-abdb-00155dcfd717 https://repozitorij.uni-lj.si/Dokument.php?lang=slv&id=149950 Fakulteta za računalništvo in informatiko 0 0 0