Zobrazeno 1 - 10
of 23
pro vyhledávání: '"Markus Miezal"'
Publikováno v:
Open Research Europe, Vol 4 (2024)
In-field human motion capture (HMC) is drawing increasing attention due to the multitude of application areas. Plenty of research is currently invested in camera-based (markerless) HMC, with the advantage of no infrastructure being required on the bo
Externí odkaz:
https://doaj.org/article/d839aac90ff845ee9a3e6007ca8ab67d
Publikováno v:
PLoS ONE, Vol 14, Iss 2, p e0213064 (2019)
3D joint kinematics can provide important information about the quality of movements. Optical motion capture systems (OMC) are considered the gold standard in motion analysis. However, in recent years, inertial measurement units (IMU) have become a p
Externí odkaz:
https://doaj.org/article/77d54507adf5446b9b9d7d7e704239c3
Autor:
Wolfgang Teufl, Bertram Taetz, Markus Miezal, Michael Lorenz, Juliane Pietschmann, Thomas Jöllenbeck, Michael Fröhlich, Gabriele Bleser
Publikováno v:
Sensors, Vol 19, Iss 22, p 5006 (2019)
Patients after total hip arthroplasty (THA) suffer from lingering musculoskeletal restrictions. Three-dimensional (3D) gait analysis in combination with machine-learning approaches is used to detect these impairments. In this work, features from the
Externí odkaz:
https://doaj.org/article/063f5f23a8824d06a62cc573f5a087bb
Autor:
Wolfgang Teufl, Michael Lorenz, Markus Miezal, Bertram Taetz, Michael Fröhlich, Gabriele Bleser
Publikováno v:
Sensors, Vol 19, Iss 1, p 38 (2018)
The aim of this study was to assess the validity and test-retest reliability of an inertial measurement unit (IMU) system for gait analysis. Twenty-four healthy subjects conducted a 6-min walking test and were instrumented with seven IMUs and retrore
Externí odkaz:
https://doaj.org/article/47e15da9cd3b4d6fb290cfd46dd0bbcb
Publikováno v:
Sensors, Vol 18, Iss 7, p 1980 (2018)
The present study investigates an algorithm for the calculation of 3D joint angles based on inertial measurement units (IMUs), omitting magnetometer data. Validity, test-retest reliability, and long-term stability are evaluated in reference to an opt
Externí odkaz:
https://doaj.org/article/44018e1249064bceac7f8c66e10f8412
Autor:
Gabriele Bleser, Dima Damen, Ardhendu Behera, Gustaf Hendeby, Katharina Mura, Markus Miezal, Andrew Gee, Nils Petersen, Gustavo Maçães, Hugo Domingues, Dominic Gorecky, Luis Almeida, Walterio Mayol-Cuevas, Andrew Calway, Anthony G Cohn, David C Hogg, Didier Stricker
Publikováno v:
PLoS ONE, Vol 10, Iss 6, p e0127769 (2015)
Today, the workflows that are involved in industrial assembly and production activities are becoming increasingly complex. To efficiently and safely perform these workflows is demanding on the workers, in particular when it comes to infrequent or rep
Externí odkaz:
https://doaj.org/article/d02be13f2c3d47ccbd9b2ff6eab58e42
Publikováno v:
Sensors, Vol 16, Iss 7, p 1132 (2016)
In inertial body tracking, the human body is commonly represented as a biomechanical model consisting of rigid segments with known lengths and connecting joints. The model state is then estimated via sensor fusion methods based on data from attached
Externí odkaz:
https://doaj.org/article/7a3cd0021a08477194b304ce3f8983fe
Publikováno v:
Gait & Posture. 95:219-220
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::5dce3e181ffe0d5b10f86c417bfeb29c
https://doi.org/10.1016/j.gaitpost.2018.06.205
https://doi.org/10.1016/j.gaitpost.2018.06.205
Publikováno v:
Multibody System Dynamics. 48:105-126
This work addresses the synergistic fusion of optimal control simulations and marker-based optical measurements of human motion. The latter is a widespread capturing technology in biomechanics and movement science. In the context of optimal control s
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::d8eb86f4c4fa85d4ca4b45965893b6f6
https://doi.org/10.1007/s11044-019-09701-4
https://doi.org/10.1007/s11044-019-09701-4
Autor:
Michael Fröhlich, Markus Miezal, Wolfgang Teufl, Ursula Trinler, Carlo Dindorf, Aidan Hogan, Gabriele Bleser, Bertram Taetz
Publikováno v:
Clinical biomechanics (Bristol, Avon). 89
Background Machine learning approaches for the classification of pathological gait based on kinematic data, e.g. derived from inertial sensors, are commonly used in terms of a multi-class classification problem. However, there is a lack of research r
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::931692099175c663961bedd100253169
https://pubmed.ncbi.nlm.nih.gov/34481198
https://pubmed.ncbi.nlm.nih.gov/34481198