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首页> 外文期刊>Biomedical and Health Informatics, IEEE Journal of >Towards User-Friendly Wearable Platforms for Monitoring Unconstrained Indoor and Outdoor Activities
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Towards User-Friendly Wearable Platforms for Monitoring Unconstrained Indoor and Outdoor Activities

机译：对于用户友好的可穿戴平台，用于监控无约束室内和户外活动

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Developing wearable platforms for unconstrained monitoring of limb movements has been an active recent topic of research due to potential applications such as clinical and athletic performance evaluation. However, practicality of these platforms might be affected by the dynamic and complexity of movements as well as characteristics of the surrounding environment. This paper addresses such issues by proposing a novel method for obtaining kinematic information of joints using a custom-designed wearable platform. The proposed method uses data from two gyroscopes and an array of textile stretch sensors to accurately track three-dimensional movements, including extension, flexion, and rotation, of a joint. More specifically, gyroscopes provide angular velocity data of two sides of a joint, while their relative orientation is estimated by a machine learning algorithm. An Unscented Kalman Filter (UKF) algorithm is applied to directly fuse angular velocity/relative orientation data and estimate the kinematic orientation of the joint. Experimental evaluations were carried out using data from 10 volunteers performing a series of predefined as well as unconstrained random three-dimensional trunk movements. Results show that the proposed sensor setup and the UKF-based data fusion algorithm can accurately estimate the orientation of the trunk relative to pelvis with an average error of less than 1.72 degrees in predefined movements and a comparable accuracy of 3.00 degrees in random movements. Moreover, the proposed platform is easy to setup, does not restrict body motion, and is not affected by environmental disturbances. This study is a further step towards developing user-friendly wearable sensor systems than can be readily used in indoor and outdoor settings without requiring bulky equipment or a tedious calibration phase.
机译：由于潜在的应用，如临床和运动性能评估等潜在应用，开发用于无约束监测的可耐磨平台是一种活跃的研究主题。然而，这些平台的实用性可能受到运动的动态和复杂性以及周围环境的特征的影响。本文通过提出使用定制设计的可穿戴平台提出用于获得关节运动信息的新方法来解决这些问题。所提出的方法使用来自两个陀螺仪和纺织拉伸传感器阵列的数据，以精确地跟踪关节的三维运动，包括延伸，屈曲和旋转。更具体地，陀螺仪提供关节两侧的角速度数据，而它们的相对取向由机器学习算法估算。将不合适的卡尔曼滤波器（UKF）算法应用于直接熔断角速度/相对取向数据并估计关节的运动学方向。使用来自10个预定义的10个志愿者的数据进行实验评估，该系列预定义以及无约束的随机三维行李箱运动。结果表明，所提出的传感器设置和基于UKF的数据融合算法可以准确地估计行李箱相对于骨盆的方向，平均误差小于1.72度，在预定义的运动中，随机运动的相当精度为3.00度。此外，所提出的平台易于设置，不限制身体运动，不受环境干扰的影响。该研究是开发用户友好的可穿戴传感器系统的另一个步骤，而不是在室内和室外设置中容易使用而不需要庞大的设备或繁琐的校准阶段。

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