OpenSense: An open-source toolbox for inertial-measurement-unit-based measurement of lower extremity kinematics over long durations.
OpenSense: An open-source toolbox for inertial-measurement-unit-based measurement of lower extremity kinematics over long durations.
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DOI：
10.1186/s12984-022-01001-x
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发表时间：
2022-02-20
影响因子：
5.1
通讯作者：
Delp S
中科院分区：
文献类型：
--
作者：
Al Borno M;O'Day J;Ibarra V;Dunne J;Seth A;Habib A;Ong C;Hicks J;Uhlrich S;Delp S
使用惯性测量单元(IMU)在自然环境中长时间测量关节运动学的能力可以实现对神经和肌肉骨骼疾病的家庭监测和个性化治疗。然而，漂移或误差随着时间的积累，阻碍了对长时间内运动的准确测量。我们试图开发一种开源的工作流程，从IMU数据中估计出准确的、能够评估和减轻漂移的下肢关节运动学。我们使用传感器融合和带约束的生物力学模型的逆运动学方法来计算基于IMU的运动学估计。我们测量了11名受试者在进行两个10分钟的试验时的运动学：行走和重复一系列不同的下肢运动。为了验证该方法，我们将IMU方向计算的关节角度与使用均方根(RMS)差和皮尔逊相关性从光学运动捕捉计算的关节角度进行比较，并使用对每个受试者随时间的RMS差异的线性回归来估计漂移。基于IMU的运动学估计与光学运动捕捉一致；除髋关节旋转外，所有受试者和所有分钟的RMS中位数差异在3到6度之间，相关系数从中到强(r = 0.60-0.87)。我们观察到均方根差异在10度/分钟内漂移最小；对这些数据的线性拟合的平均斜率接近零(−0.14-0.17度/分钟)。我们的工作流程产生的关节运动学与光学运动捕获估计的运动学一致，即使在连续行走而不休息的试验中也可以缓解运动学漂移，这可能消除在研究类似活动时当前漂移缓解方法中使用的显式传感器重新校准(例如，坐着或站着几秒钟或零速度更新)的需要。这可以实现长时间的测量，使该领域离估计自然环境中的运动学又近了一步。网上版载有补充材料，可在10.1186/s12984-022-01001-x查阅。
The ability to measure joint kinematics in natural environments over long durations using inertial measurement units (IMUs) could enable at-home monitoring and personalized treatment of neurological and musculoskeletal disorders. However, drift, or the accumulation of error over time, inhibits the accurate measurement of movement over long durations. We sought to develop an open-source workflow to estimate lower extremity joint kinematics from IMU data that was accurate and capable of assessing and mitigating drift. We computed IMU-based estimates of kinematics using sensor fusion and an inverse kinematics approach with a constrained biomechanical model. We measured kinematics for 11 subjects as they performed two 10-min trials: walking and a repeated sequence of varied lower-extremity movements. To validate the approach, we compared the joint angles computed with IMU orientations to the joint angles computed from optical motion capture using root mean square (RMS) difference and Pearson correlations, and estimated drift using a linear regression on each subject’s RMS differences over time. IMU-based kinematic estimates agreed with optical motion capture; median RMS differences over all subjects and all minutes were between 3 and 6 degrees for all joint angles except hip rotation and correlation coefficients were moderate to strong (r = 0.60–0.87). We observed minimal drift in the RMS differences over 10 min; the average slopes of the linear fits to these data were near zero (− 0.14–0.17 deg/min). Our workflow produced joint kinematics consistent with those estimated by optical motion capture, and could mitigate kinematic drift even in the trials of continuous walking without rest, which may obviate the need for explicit sensor recalibration (e.g. sitting or standing still for a few seconds or zero-velocity updates) used in current drift-mitigation approaches when studying similar activities. This could enable long-duration measurements, bringing the field one step closer to estimating kinematics in natural environments. The online version contains supplementary material available at 10.1186/s12984-022-01001-x.
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DOI：
10.1109/tbme.2021.3103201
发表时间：
2022-03
期刊：
IEEE transactions on bio-medical engineering
影响因子：
--
作者：
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通讯作者：
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影响因子：
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DOI：
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发表时间：
2021-01-01
影响因子：
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