Annu Int Conf IEEE Eng Med Biol Soc. 2022 Jul;2022:1805-1808. doi: 10.1109/EMBC48229.2022.9871201.
Several biomedical contexts such as diagnosis, rehabilitation, and ergonomics require an accurate estimate of human upper limbs kinematics. Wearable inertial measurement units (IMU s) represent a suitable solution because of their unobtrusiveness, portability, and low-cost. However, the time-integration of the gyroscope angular velocity leads to an unbounded orientation drift affecting both angular and linear displacements over long observation interval. In this work, a Denavit-Hartenberg model of the upper limb was defined in accordance with the guidelines of the International Society of Biomechanics and exploited to design an optimization kinematics process. This procedure estimated the joint angles by minimizing the difference between the modelled and IMU-driven orientation of upper arm and forearm. In addition, reasonable constraints were added to limit the drift influence on the final joint kinematics accuracy. The validity of the procedure was tested on synthetic and experimental data acquired with a robotic arm over 20 minutes. Average rms errors amounted to 2.8 deg and 1.1 for synthetic and robot data, respectively. Clinical Relevance - The proposed method has the potential to improve robustness and accuracy of multi-joint kinematics estimation in the general contexts of home-based tele-rehabilitation interventions. In this respect adoption of multi-segmental kinematic model along with physiological joint constraints could contribute to address current limitations associated to unsupervised analysis in terms of monitoring and outcome assessment.
在一些生物医学领域,如诊断、康复和人体工程学,需要准确估计人体上肢的运动学。可穿戴惯性测量单元(IMU)因其非侵入性、便携性和低成本而成为一种合适的解决方案。然而,陀螺仪角速度的时间积分会导致无界的姿态漂移,从而影响长时间观测间隔内的角位移和线位移。在这项工作中,根据国际生物力学学会的指南,定义了上肢的 Denavit-Hartenberg 模型,并利用该模型设计了一种优化运动学过程。该过程通过最小化上臂和前臂的模型化和 IMU 驱动的姿态之间的差异来估计关节角度。此外,还添加了合理的约束条件,以限制漂移对最终关节运动学精度的影响。该方法在使用机器人手臂采集的 20 多分钟的合成和实验数据上进行了验证。合成数据和机器人数据的平均均方根误差分别为 2.8 度和 1.1 度。临床相关性- 该方法有可能提高家庭远程康复干预等一般情况下多关节运动学估计的鲁棒性和准确性。在这方面,采用多节段运动学模型和生理关节约束条件可能有助于解决与监测和结果评估相关的无监督分析相关的当前限制。