Bonfiglio Alessandro, Tacconi David, Bongers Raoul M, Farella Elisabetta
Information Engineering and Computer Science Department (DISI), University of Trento, Trento, Italy.
Euleria Health, Rovereto, Italy.
Front Bioeng Biotechnol. 2024 May 17;12:1385750. doi: 10.3389/fbioe.2024.1385750. eCollection 2024.
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. 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). 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. 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.
惯性测量单元（IMU）需要一个传感器到节段的校准程序，以便计算出解剖学上准确的关节角度，从而应用于医疗保健和康复领域。研究文献提出了几种算法来解决这个问题。然而，由于影响肘关节角度准确性的变量众多，包括三维关节轴、执行的运动、复杂的解剖结构和明显的皮肤伪影，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多个运动任务中的表现，为肘关节最适合的校准提供建议。13名健康受试者配备了IMU传感器和光学标记簇。每个参与者执行一系列静态姿势和动作来校准仪器，随后执行单平面和多关节任务。用于评估关节角度准确性的指标是运动范围（ROM）误差、均方根误差（RMSE）和偏移量。我们进行了三因素重复测量方差分析，以评估关节轴和运动任务对三种校准技术的影响：N姿势（NP）、功能校准（FC）和手动对齐（MA）。尽管ROM误差的效应量较小，但由于四分位间距高达24.6°，NP在校准中显示出最低的精度。RMSE在校准之间显示出显著差异，且效应量较大，其中MA表现最佳（RMSE = 6.3°），与FC相当（RMSE = 7.2°）。偏移量在校准*轴交互作用中显示出较大的效应量，其中FC和MA表现相似。因此，我们推荐MA作为肘关节首选的校准方法，因为它简单易用。或者，当佩戴者无法保持预定姿势时，FC可以是一个有效的选择。
