A fusion framework to estimate plantar ground force distributions and ankle dynamics
A fusion framework to estimate plantar ground force distributions and ankle dynamics
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DOI：
10.1016/j.inffus.2017.09.008
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发表时间：
2018-05
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影响因子：
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通讯作者：
F. Deligianni;Charence Wong;Benny P. L. Lo;Guang-Zhong Yang
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文献类型：
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作者：
F. Deligianni;Charence Wong;Benny P. L. Lo;Guang-Zhong Yang
步态分析在骨科疾病患者的康复以及监测神经状况、心理健康问题和老年受试者的幸福感等方面发挥着重要作用。它还构成了良好姿势的指数，因此可以用来预防运动员受伤和监测典型受试者的心理健康。通常，准确的步态分析是基于脚踝动力学和地面反作用力的测量。因此，它需要昂贵的多摄像头系统和压力传感器，这在自由生活的环境中不容易使用。我们提出了一种融合框架，在关键步态事件期间使用耳朵佩戴活动识别(e-AR)传感器和单个摄像机来估计脚角。为此，我们使用典型相关分析和融合套索惩罚，分两步进行，首先仅基于e-AR信号学习地面反作用力的定时分布模型，然后基于e-AR传感器和视频的组合特征对脚踝的外翻/内翻和背伸进行建模。结果表明，结合视频记录的脚踝角度信息的不变特征，大大提高了对脚步前进角度的估计。
Gait analysis plays an important role in several conditions, including the rehabilitation of patients with orthopaedic problems and the monitoring of neurological conditions, mental health problems and the well-being of elderly subjects. It also constitutes an index of good posture and thus it can be used to prevent injuries in athletes and monitor mental health in typical subjects. Usually, accurate gait analysis is based on the measurement of ankle dynamics and ground reaction forces. Therefore, it requires expensive multi-camera systems and pressure sensors, which cannot be easily employed in a free-living environment. We propose a fusion framework that uses an ear worn activity recognition (e-AR) sensor and a single video camera to estimate foot angle during key gait events. To this end we use canonical correlation analysis with a fused-lasso penalty in a two-steps approach that firstly learns a model of the timing distribution of ground reaction forces based on e-AR signal only and subsequently models the eversion/inversion as well as the dorsiflexion of the ankle based on the combined features of e-AR sensor and the video. The results show that incorporating invariant features of angular ankle information from the video recordings improves the estimation of the foot progression angle, substantially.