Estimation of Vertical Ground Reaction Forces and Sagittal Knee Kinematics During Running Using Three Inertial Sensors

Autor: Frank J. Wouda, Matteo Giuberti, Giovanni Bellusci, Erik Maartens, Jasper Reenalda, Bert-Jan F. van Beijnum, Peter H. Veltink

Publikováno v: Frontiers in Physiology, Vol 9 (2018)

Analysis of running mechanics has traditionally been limited to a gait laboratory using either force plates or an instrumented treadmill in combination with a full-body optical motion capture system. With the introduction of inertial motion capture s

Externí odkaz: https://doaj.org/article/5b5be35b315645c6ac1cca875abc9b72

Estimation of Full-Body Poses Using Only Five Inertial Sensors: An Eager or Lazy Learning Approach?

Autor: Frank J. Wouda, Matteo Giuberti, Giovanni Bellusci, Peter H. Veltink

Publikováno v: Sensors, Vol 16, Iss 12, p 2138 (2016)

Human movement analysis has become easier with the wide availability of motion capture systems. Inertial sensing has made it possible to capture human motion without external infrastructure, therefore allowing measurements in any environment. As high

Externí odkaz: https://doaj.org/article/319a283941bb4fd89c3d4960c4676a43

Time Coherent Full-Body Poses Estimated Using Only Five Inertial Sensors: Deep versus Shallow Learning

Autor: Matteo Giuberti, Nina Rudigkeit, Peter H. Veltink, Bert-Jan van Beijnum, Mannes Poel, Frank J. Wouda

Publikováno v: Sensors (Basel, Switzerland)
Sensors, Vol 19, Iss 17, p 3716 (2019)
Sensors (Switzerland), 19:3716. MDPI
Sensors
Volume 19
Issue 17

Full-body motion capture typically requires sensors/markers to be placed on each rigid body segment, which results in long setup times and is obtrusive. The number of sensors/markers can be reduced using deep learning or offline methods. However, thi

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::9a82976ac991b117070c3bd13a25d463
https://pubmed.ncbi.nlm.nih.gov/31461958

On the Validity of Different Motion Capture Technologies for the Analysis of Running

Autor: Peter H. Veltink, Giovanni Bellusci, Matteo Giuberti, Erik Maartens, Bert-Jan van Beijnum, Jasper Reenalda, Frank J. Wouda

Publikováno v: 2018 7th IEEE International Conference on Biomedical Robotics and Biomechatronics (Biorob), 1175-1180
STARTPAGE=1175;ENDPAGE=1180;TITLE=2018 7th IEEE International Conference on Biomedical Robotics and Biomechatronics (Biorob)
BioRob

An increasing diversity of available motion capture technologies allows for measurement of human kinematics in various environments. However, little is known about the differences in quality of measured kinematics by such technologies. Therefore, thi

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::55fb67c61f4e8e82c9ad7dd730627ea6
https://research.utwente.nl/en/publications/5058533b-10fb-42af-952f-c91c9c1108bc

A hybrid radio/accelerometric approach to arm posture recognition

Autor: Gianluigi Ferrari, Matteo Giuberti, Marco Martalo

Publikováno v: Journal of Ambient Intelligence and Smart Environments. 7:563-578

In this paper, we investigate the feasibility of a hybrid radio/accelerometric approach to perform arm posture recogni- tion. A radio fingerprinting-based approach, through measurements of the Received radio Signal Strengths (RSSs) from anchor nodes,

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::e89331cb2e52b39eea239eaa0fe2bcff
https://doi.org/10.3233/ais-150326