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首页> 外文期刊>Journal of Biomechanics >A smart device inertial-sensing method for gait analysis
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A smart device inertial-sensing method for gait analysis

机译：一种用于步态分析的智能设备惯性传感方法

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The purpose of this study was to establish and cross-validate a method for analyzing gait patterns determined by the center of mass (COM) through inertial sensors embedded in smart devices. The method employed an extended Kalman filter in conjunction with a quaternion rotation matrix approach to transform accelerations from the object onto the global frame. Derived by double integration, peak-to-trough changes in vertical COM position captured by a motion capture system, inertial measurement unit, and smart device were compared in terms of averaged and individual steps. The inter-rater reliability and levels of agreement for systems were discerned through intraclass correlation coefficients (ICC) and Bland-Altman plots. ICCs corresponding to inter-rater reliability were good-to-excellent for position data (ICCs,.80-.95) and acceleration data (ICCs,.54-.81). Levels of agreements were moderate for position data (LOA, 3.1-19.3%) and poor for acceleration data (LOA, 6.8%-17.8%). The Bland-Altman plots, however, revealed a small systematic error, in which peak-to-trough changes in vertical COM position were underestimated by 2.2 mm; the Kalman filter's accuracy requires further investigation to minimize this oversight. More importantly, however, the study's preliminary results indicate that the smart device allows for reliable COM measurements, opening up a cost-effective, user-friendly, and popular solution for remotely monitoring movement. The long-term impact of the smart device method on patient rehabilitation and therapy cannot be underestimated: not only could healthcare expenditures be curbed (smart devices being more affordable than today's motion sensors), but a more refined grasp of individual functioning, activity, and participation within everyday life could be attained. (C) 2014 Elsevier Ltd. All rights reserved.
机译：本研究的目的是建立和交叉验证通过嵌入智能设备中的惯性传感器来分析由质量中心（COM）确定的步态模式的方法。该方法采用扩展的卡尔曼滤波器结合四元数旋转矩阵方法来将从物体的加速度转换到全局帧。通过双积分来源，通过运动捕获系统，惯性测量单元和智能设备捕获的垂直COM位置的峰到槽的变化在平均和单个步骤方面进行了比较。通过脑相关系数（ICC）和Bland-Altman图来识别系统间的帧间可靠性和协议级别。对应于帧间间可靠性的ICC对于位置数据（ICCS，.80-.95）和加速度数据（ICCS，.54-.81）是良好的。协议水平适用于地位数据（LOA，3.1-19.3％），加速数据（LOA，6.8％-17.8％）。然而，Bland-Altman绘图揭示了一个小的系统误差，其中垂直COM位置的峰槽变化低估了2.2毫米;卡尔曼滤波器的准确性需要进一步调查以尽量减少这种监督。然而，更重要的是，该研究的初步结果表明智能设备允许可靠的COM测量，开辟了经济效益，用户友好和流行的远程监控运动解决方案。智能设备方法对患者康复和治疗的长期影响不能低估：不仅可以遏制医疗保健支出（智能设备比今天的运动传感器更具实惠），而且更加精致地掌握个人功能，活动和可以获得日常生活中的参与。 （c）2014年elestvier有限公司保留所有权利。

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