Alessandro Bonfiglio, David Tacconi, Raoul M. Bongers, Elisabetta Farella
{"title":"Effects of IMU sensor-to-segment calibration on clinical 3D elbow joint angles estimation","authors":"Alessandro Bonfiglio, David Tacconi, Raoul M. Bongers, Elisabetta Farella","doi":"10.3389/fbioe.2024.1385750","DOIUrl":null,"url":null,"abstract":"Introduction: Inertial Measurement Units (IMU) require a sensor-to-segment calibration procedure in order to compute anatomically accurate joint angles and, thereby, be employed in healthcare and rehabilitation. Research literature proposes several algorithms to address this issue. However, determining an optimal calibration procedure is challenging due to the large number of variables that affect elbow joint angle accuracy, including 3D joint axis, movement performed, complex anatomy, and notable skin artefacts. Therefore, this paper aims to compare three types of calibration techniques against an optical motion capture reference system during several movement tasks to provide recommendations on the most suitable calibration for the elbow joint.Methods: Thirteen healthy subjects were instrumented with IMU sensors and optical marker clusters. Each participant performed a series of static poses and movements to calibrate the instruments and, subsequently, performed single-plane and multi-joint tasks. The metrics used to evaluate joint angle accuracy are Range of Motion (ROM) error, Root Mean Squared Error (RMSE), and offset. We performed a three-way RM ANOVA to evaluate the effect of joint axis and movement task on three calibration techniques: N-Pose (NP), Functional Calibration (FC) and Manual Alignment (MA).Results: Despite small effect sizes in ROM Error, NP displayed the least precision among calibrations due to interquartile ranges as large as 24.6°. RMSE showed significant differences among calibrations and a large effect size where MA performed best (RMSE = 6.3°) and was comparable with FC (RMSE = 7.2°). Offset showed a large effect size in the calibration*axes interaction where FC and MA performed similarly.Conclusion: Therefore, we recommend MA as the preferred calibration method for the elbow joint due to its simplicity and ease of use. Alternatively, FC can be a valid option when the wearer is unable to hold a predetermined posture.","PeriodicalId":508781,"journal":{"name":"Frontiers in Bioengineering and Biotechnology","volume":"37 13","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2024-05-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Frontiers in Bioengineering and Biotechnology","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.3389/fbioe.2024.1385750","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
Introduction: Inertial Measurement Units (IMU) require a sensor-to-segment calibration procedure in order to compute anatomically accurate joint angles and, thereby, be employed in healthcare and rehabilitation. Research literature proposes several algorithms to address this issue. However, determining an optimal calibration procedure is challenging due to the large number of variables that affect elbow joint angle accuracy, including 3D joint axis, movement performed, complex anatomy, and notable skin artefacts. Therefore, this paper aims to compare three types of calibration techniques against an optical motion capture reference system during several movement tasks to provide recommendations on the most suitable calibration for the elbow joint.Methods: Thirteen healthy subjects were instrumented with IMU sensors and optical marker clusters. Each participant performed a series of static poses and movements to calibrate the instruments and, subsequently, performed single-plane and multi-joint tasks. The metrics used to evaluate joint angle accuracy are Range of Motion (ROM) error, Root Mean Squared Error (RMSE), and offset. We performed a three-way RM ANOVA to evaluate the effect of joint axis and movement task on three calibration techniques: N-Pose (NP), Functional Calibration (FC) and Manual Alignment (MA).Results: Despite small effect sizes in ROM Error, NP displayed the least precision among calibrations due to interquartile ranges as large as 24.6°. RMSE showed significant differences among calibrations and a large effect size where MA performed best (RMSE = 6.3°) and was comparable with FC (RMSE = 7.2°). Offset showed a large effect size in the calibration*axes interaction where FC and MA performed similarly.Conclusion: Therefore, we recommend MA as the preferred calibration method for the elbow joint due to its simplicity and ease of use. Alternatively, FC can be a valid option when the wearer is unable to hold a predetermined posture.
引言惯性测量单元(IMU)需要一个传感器到分段的校准程序,才能计算出解剖学上精确的关节角度,从而应用于医疗保健和康复领域。研究文献提出了多种算法来解决这一问题。然而,由于影响肘关节角度准确性的变量较多,包括三维关节轴、已执行的运动、复杂的解剖结构和明显的皮肤伪影,因此确定最佳校准程序具有挑战性。因此,本文旨在比较三种校准技术与光学运动捕捉参考系统在几项运动任务中的表现,为肘关节最合适的校准提供建议:方法:13 名健康受试者配备了 IMU 传感器和光学标记集群。每位受试者都进行了一系列静态姿势和运动以校准仪器,随后进行了单平面和多关节任务。用于评估关节角度准确性的指标包括运动范围(ROM)误差、均方根误差(RMSE)和偏移量。我们进行了三方 RM 方差分析,以评估关节轴和运动任务对三种校准技术的影响:结果发现,尽管ROM误差、RMSE和偏移量对关节轴和运动任务的影响较小,但对运动任务的影响较大:结果:尽管 ROM 误差的效应大小较小,但 NP 在校准中的精度最低,因为其四分位间范围高达 24.6°。均方根误差(RMSE)显示出校准之间的显著差异和较大的效应大小,其中 MA 的表现最好(RMSE = 6.3°),与 FC 相当(RMSE = 7.2°)。在校准*轴线交互作用中,偏移显示出较大的效应大小,FC 和 MA 的表现类似:因此,我们建议将 MA 作为肘关节的首选校准方法,因为它简单易用。另外,当佩戴者无法保持预定姿势时,FC 也是一种有效的选择。