{"title":"Researches on Inertial Mechatronic Motion Analysis Systems, Based on MEMS","authors":"C. Badea","doi":"10.1515/bsmm-2018-0019","DOIUrl":null,"url":null,"abstract":"Abstract The aim of this paper is to provide an introduction into the inertial motion analysis field, focusing its attention on the analysis that is performed using modern mechatronic inertial motion capture systems, highlighting both the advantages and drawbacks of using such a system and outlining the main constituent elements of these systems as well as the necessary steps to be carried out in order to be able to accomplish such analysis. A modern mechatronic inertial motion analysis system’s evolution it’s based on MEMS (Micro-Electro-Mechanical-Systems) sensory network, each of which contains a combination of accelerometer, gyroscope, magnetometer. The signals from these MEMS are processed, by a microcontroller, using advanced algorithms in order to provide accurate data regarding body’s segments kinematics, global positioning and magnetic field. Those data are being transferred to a biomechanical model for the analysis. Despite its tremendous advantages, such as portability and real-time analysis capabilities, over alternative motion analysis systems, such as optical or mechanical systems, that based on external transmitters and/or video camera networks, restricting their use to special laboratory conditions and/or large workspace, mechatronic inertial systems are not based on an external infrastructure, they present a major disadvantage, namely the positional drift. The estimation of the human subject body’s segments positions and orientation, absolutely requires an initial calibration procedure, called „sensor to segment calibration”, that starts from the positional and orientation information received from the MEMS network, which are then transferred onto a biomechanical (scaled) model of the human body.","PeriodicalId":30754,"journal":{"name":"Scientific Bulletin of Valahia University Materials and Mechanics","volume":"20 1","pages":"44 - 50"},"PeriodicalIF":0.0000,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"4","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Scientific Bulletin of Valahia University Materials and Mechanics","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1515/bsmm-2018-0019","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 4
Abstract
Abstract The aim of this paper is to provide an introduction into the inertial motion analysis field, focusing its attention on the analysis that is performed using modern mechatronic inertial motion capture systems, highlighting both the advantages and drawbacks of using such a system and outlining the main constituent elements of these systems as well as the necessary steps to be carried out in order to be able to accomplish such analysis. A modern mechatronic inertial motion analysis system’s evolution it’s based on MEMS (Micro-Electro-Mechanical-Systems) sensory network, each of which contains a combination of accelerometer, gyroscope, magnetometer. The signals from these MEMS are processed, by a microcontroller, using advanced algorithms in order to provide accurate data regarding body’s segments kinematics, global positioning and magnetic field. Those data are being transferred to a biomechanical model for the analysis. Despite its tremendous advantages, such as portability and real-time analysis capabilities, over alternative motion analysis systems, such as optical or mechanical systems, that based on external transmitters and/or video camera networks, restricting their use to special laboratory conditions and/or large workspace, mechatronic inertial systems are not based on an external infrastructure, they present a major disadvantage, namely the positional drift. The estimation of the human subject body’s segments positions and orientation, absolutely requires an initial calibration procedure, called „sensor to segment calibration”, that starts from the positional and orientation information received from the MEMS network, which are then transferred onto a biomechanical (scaled) model of the human body.