| dc.rights.license | Visos teisės saugomos / All rights reserved | en_US |
| dc.contributor.author | Dumpis, Martynas | |
| dc.contributor.author | Gedminas, Dovydas | |
| dc.contributor.author | Serackis, Artūras | |
| dc.date.accessioned | 2025-12-19T07:50:31Z | |
| dc.date.available | 2025-12-19T07:50:31Z | |
| dc.date.issued | 2022 | |
| dc.identifier.isbn | 9781665450492 | en_US |
| dc.identifier.uri | https://etalpykla.vilniustech.lt/handle/123456789/159598 | |
| dc.description.abstract | The paper focuses on the quantification of upper limb motion during human-operated exercises, dedicated to rehabilitation. Continuous tracking of a measurement of each exercise helps the patient individually monitor the current progress of rehabilitation treatment and provide a source of quantified data for the preparation of biological feedback. While the camera-based solutions may provide more precise upper limb tracking, such systems may cause some privacy issues or might be uncomfortable for the patient to use in daily manner. The sensor-based solution is a good option here. Inertial sensors In this investigation, an algorithm for automated calibration of a single sensor with precise estimation of upper limb movement is presented. The study focuses on the measurement of rehabilitation exercises and the collection of parameters that are important to medical diagnosis and long-term monitoring of the change in these parameters. The suggested solution simplifies the human motion estimation process by correctly estimating human motion even inertial sensor's axis is not aligned correctly with the body frame. Additionally, a system with two sensors is proposed to compensate for upper limb movement quantification uncertainty during specific exercises. | en_US |
| dc.format.extent | 6 p. | en_US |
| dc.format.medium | Tekstas / Text | en_US |
| dc.language.iso | en | en_US |
| dc.relation.uri | https://etalpykla.vilniustech.lt/handle/123456789/159399 | en_US |
| dc.source.uri | https://ieeexplore.ieee.org/document/9781729 | en_US |
| dc.subject | motion sensor | en_US |
| dc.subject | kalman filter | en_US |
| dc.subject | rehabilitation | en_US |
| dc.subject | up-per limb tracking | en_US |
| dc.subject | quantification | en_US |
| dc.title | Inertial Sensor based System for Upper Limb Motion Quantification | en_US |
| dc.type | Konferencijos publikacija / Conference paper | en_US |
| dcterms.accrualMethod | Rankinis pateikimas / Manual submission | en_US |
| dcterms.issued | 2022-05-30 | |
| dcterms.references | 13 | en_US |
| dc.description.version | Taip / Yes | en_US |
| dc.contributor.institution | Vilniaus Gedimino technikos universitetas | en_US |
| dc.contributor.institution | Vilnius Gediminas Technical University | en_US |
| dc.contributor.department | Elektroninių sistemų katedra / Department of Electronic Systems | en_US |
| dcterms.sourcetitle | 2022 IEEE Open Conference of Electrical, Electronic and Information Sciences (eStream), April 21, 2022, Vilnius, Lithuania | en_US |
| dc.identifier.eisbn | 9781665450485 | en_US |
| dc.identifier.eissn | 2690-8506 | en_US |
| dc.publisher.name | IEEE | en_US |
| dc.publisher.country | United States of America | en_US |
| dc.publisher.city | New York | en_US |
| dc.identifier.doi | https://doi.org/10.1109/eStream56157.2022.9781729 | en_US |