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Autonomous Quality Control of Joint Orientation Measured with Inertial Sensors

机译：惯性传感器测量关节方向的自主质量控制

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摘要

Clinical mobility assessment is traditionally performed in laboratories using complex and expensive equipment. The low accessibility to such equipment, combined with the emerging trend to assess mobility in a free-living environment, creates a need for body-worn sensors (e.g., inertial measurement units—IMUs) that are capable of measuring the complexity in motor performance using meaningful measurements, such as joint orientation. However, accuracy of joint orientation estimates using IMUs may be affected by environment, the joint tracked, type of motion performed and velocity. This study investigates a quality control (QC) process to assess the quality of orientation data based on features extracted from the raw inertial sensors’ signals. Joint orientation (trunk, hip, knee, ankle) of twenty participants was acquired by an optical motion capture system and IMUs during a variety of tasks (sit, sit-to-stand transition, walking, turning) performed under varying conditions (speed, environment). An artificial neural network was used to classify good and bad sequences of joint orientation with a sensitivity and a specificity above 83%. This study confirms the possibility to perform QC on IMU joint orientation data based on raw signal features. This innovative QC approach may be of particular interest in a big data context, such as for remote-monitoring of patients’ mobility.
机译：传统上，临床流动性评估是在实验室中使用复杂且昂贵的设备进行的。此类设备的易接近性不足，再加上在自由生活环境中评估机动性的新兴趋势，因此需要能够通过使用以下方法测量电机性能复杂性的人体传感器（例如惯性测量单元-IMU）有意义的测量，例如关节方向。但是，使用IMU进行关节方向估计的准确性可能会受到环境，跟踪的关节，执行的运动类型和速度的影响。这项研究调查了一种质量控制（QC）过程，该过程基于从原始惯性传感器信号中提取的特征来评估方向数据的质量。光学运动捕捉系统和IMU在二十个参与者的关节方向（躯干，臀部，膝盖，脚踝）的操作是在各种条件（速度，环境）。使用人工神经网络对关节定向的好坏序列进行分类，其敏感性和特异性都在83％以上。这项研究证实了基于原始信号特征对IMU关节方向数据进行QC的可能性。这种创新的质量控制方法可能在大数据环境中特别有用，例如用于远程监控患者的活动能力。

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