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Real-Time Foot Clearance and Environment Estimation Based on Foot-Mounted Wearable Sensors

机译:基于脚上可穿戴传感器的实时脚部净空和环境估计

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The ambulatory gait monitoring systems (AGMS) using wearable devices have a great attention for health monitoring of individuals. Foot clearance is one of gait parameters and an indicator of gait quality and safety. Conventionally, foot clearance is calculated by post-processing using several methods such as extended Kalman filter (EKF), a weighted Fourier linear combiner (WFLC), a simple biomechanical foot model, zero velocity update (ZVU), and optimally filtered direct and reverse integration (OFDRI). However, real-time foot clearance (FC) estimation is required to apply to control of neural prostheses and assistive devices to prevent falling down. In addition, since humans adapt to several environments during gait motion, walking patterns are different among leveled walk, ramp walk, and stair walk. Therefore, environmental recognition also has an important role for the devices. Aim of this study is an implication of a system which performs real-time foot clearance and environment estimation in several situations. Different configurations of infrared (IR) distance sensors are utilized with a foot-mounted inertia measurement unit (IMU: an acceleration sensor, a gyro sensor, a magnetometer, and an air pressure sensor) sensor. Two IR sensors are attached to both sides of a shoe with different orientation. Developed system is tested in case of leveled walk, ramp walk, and stair walk. As the result, the developed system can estimate foot clearance and find characteristics of walking in different gait motion.
机译:使用可穿戴设备的步行步态监控系统(AGMS)在个人健康监控方面引起了极大的关注。脚间隙是步态参数之一,是步态质量和安全性的指标。通常,脚部间隙是通过使用多种方法进行后处理来计算的,例如扩展卡尔曼滤波器(EKF),加权傅立叶线性组合器(WFLC),简单的生物力学脚部模型,零速度更新(ZVU)以及经过最佳滤波的正向和反向集成(OFDRI)。但是,需要实时脚部间隙(FC)估计以应用于神经假体和辅助设备的控制,以防止跌倒。另外,由于人类在步态运动期间适应多种环境,因此在平坦步行,坡道步行和楼梯步行之间的步行模式是不同的。因此,环境识别对于设备也具有重要作用。这项研究的目的是在几种情况下执行实时脚部间隙和环境估计的系统的含义。红外(IR)距离传感器的不同配置与安装在脚上的惯性测量单元(IMU:加速度传感器,陀螺仪传感器,磁力计和气压传感器)一起使用。两个红外传感器以不同的方向连接到鞋子的两侧。在水平步行,坡道步行和楼梯步行的情况下,对开发的系统进行了测试。结果,开发的系统可以估计脚的间隙并找到不同步态运动的步行特征。

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