Sit to stand sensing using wearable IMUs based on adaptive Neuro Fuzzy and Kalman Filter
Sit to stand sensing using wearable IMUs based on adaptive Neuro Fuzzy and Kalman Filter
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DOI：
10.1109/hic.2014.7038931
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发表时间：
2014-10
期刊：
影响因子：
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通讯作者：
Omar Salah;A. Ramadan;S. Sessa;Ahmed M. R. Fath El-Bab;A. Abo-Ismail;M. Zecca;Yo Kobayashi;A. Takanishi;M. Fujie
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文献类型：
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作者：
Omar Salah;A. Ramadan;S. Sessa;Ahmed M. R. Fath El-Bab;A. Abo-Ismail;M. Zecca;Yo Kobayashi;A. Takanishi;M. Fujie
本文提出了一种使用惯性传感器测量人体从坐到站运动的不同阶段的姿势的方法。所提出的方法使用自适应神经模糊推理系统（ANFIS）和卡尔曼滤波器（KF）融合来自放置在躯干和大腿中的惯性传感器的数据。当执行测量更新步骤时，ANFIS 尝试在每个采样时刻估计人类肩膀的位置。卡尔曼滤波器监督 AFIS 的性能，旨在减少估计值与实际值之间的不匹配。该方法的性能通过 VICON（运动分析系统）的测量得到验证。所得结果表明该算法在预测人体肩部位置方面的有效性，x、y 方向均方根误差分别为 0.018 m 和 0.016 m。
This paper present a method for measuring the posture of a human body during different phases of sit to stand motion using inertial sensors. The proposed method fuses data from inertial sensors placed in trunk and thigh using Adaptive Neuro-Fuzzy Inference System (ANFIS) followed by a Kalman Filter (KF). The ANFIS attempts to estimate the position of shoulder of the human, at each sampling instant when measurement update step is carried out. The Kalman filter supervises the performance of the ANFIS with the aim of reducing the mismatch between the estimated and actual. The performance of the method is verified by measurements from VICON (motion analysis system). The obtained results show the effectiveness of the proposed algorithm in prediction the human shoulder position with root mean square error 0.018 m and 0.016 m in the x and y direction, respectively.