An instance-based algorithm with Auxiliary Similarity Information for the estimation of gait kinematics from wearable sensors
An instance-based algorithm with Auxiliary Similarity Information for the estimation of gait kinematics from wearable sensors
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DOI：
10.1109/tnn.2008.2000808
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发表时间：
2008-09-01
影响因子：
--
通讯作者：
Howard, David
中科院分区：
文献类型：
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作者：
Goulermas, John Y.;Findlow, Andrew H.;Howard, David
可穿戴的人类运动测量系统越来越流行，作为在现实情况下捕获人类运动数据的一种手段。先前的工作试图在步行中从脚步加速和角速度数据中估算细分运动学。在本文中，我们提出了一个新型的神经网络[具有辅助相似性信息（GASI）]，该网络通过自适应加权来考虑接近性和步态轨迹斜率信息来估计关节运动学。此外，使用多个可以适应局部数据密度的内核带宽参数。为了证明气体算法的值，使用市售可穿戴的传感器收集的脚和柄的加速度和角速度数据估算了臀部，膝盖和踝关节运动。参考髋关节，膝盖和脚踝运动学数据是在受试者以不同速度行走时的外部安装的反射标记和红外摄像机获得的。结果提供了进一步的证据，表明对联合运动学估计的神经净方法是可行的，并显示出希望，但其他实际问题也必须是。在这种方法已经足够成熟之前解决临床实施。此外，它们证明了新的气体算法的实用性，用于从连续的周期性数据中估算，包括噪声和显着的可变性。
Wearable human movement measurement systems are increasingly popular as a means of capturing human movement data in real-world situations. Previous work has attempted to estimate segment kinematics during walking from foot acceleration and angular velocity data. In this paper, we propose a novel neural network [GRNN with Auxiliary Similarity Information (GASI)] that estimates joint kinematics by taking account of proximity and gait trajectory slope information through adaptive weighting. Furthermore, multiple kernel bandwidth parameters are used that can adapt to the local data density. To demonstrate the value of the GASI algorithm, hip, knee, and ankle joint motions are estimated from acceleration and angular velocity data for the foot and shank, collected using commercially available wearable sensors. Reference hip, knee, and ankle kinematic data were obtained using externally mounted reflective markers and infrared cameras for subjects while they walked at different speeds. The results provide further evidence that a neural net approach to the estimation of joint kinematics is feasible and shows promise, but other practical issues must be. addressed before this approach is mature enough for clinical implementation. Furthermore, they demonstrate the utility of the new GASI algorithm for making estimates from continuous periodic data that include noise and a significant level of variability.