Center for Bionics, Korea Institute of Science and Technology, Seoul 02792, Korea.
Electronic Materials Engineering, Kwangwoon University, Seoul 01890, Korea.
Sensors (Basel). 2021 Mar 18;21(6):2145. doi: 10.3390/s21062145.
The operation of wearable robots, such as gait rehabilitation robots, requires real-time classification of the standing or walking state of the wearer. This report explains a technique that measures the ground reaction force (GRF) using an insole device equipped with force sensing resistors, and detects whether the insole wearer is standing or walking based on the measured results. The technique developed in the present study uses the waveform length that represents the sum of the changes in the center of pressure within an arbitrary time window as the determining factor, and applies this factor to a conventional threshold method and an artificial neural network (ANN) model for classification of the standing and walking states. The results showed that applying the newly developed technique could significantly reduce classification errors due to shuffling movements of the patient, typically noticed in the conventional threshold method using GRF, i.e., real-time classification of the standing and walking states is possible in the ANN model. The insole device used in the present study can be applied not only to gait analysis systems used in wearable robot operations, but also as a device for remotely monitoring the activities of daily living of the wearer.
可穿戴机器人（如步态康复机器人）的操作需要实时分类佩戴者的站立或行走状态。本报告介绍了一种使用足底压力测量鞋垫设备（内置测力电阻）测量地面反力（GRF）的技术，并根据测量结果检测足底压力测量鞋垫佩戴者是处于站立状态还是行走状态。本研究中开发的技术使用代表任意时间窗口内压力中心变化总和的波形长度作为判定因素，并将该因素应用于传统阈值方法和人工神经网络（ANN）模型，以对站立和行走状态进行分类。结果表明，应用新开发的技术可以显著减少因患者的晃动运动导致的分类错误，这在传统的基于 GRF 的阈值方法中是常见的，即在 ANN 模型中可以实时分类站立和行走状态。本研究中使用的足底压力测量鞋垫设备不仅可以应用于可穿戴机器人操作中的步态分析系统，还可以作为一种设备来远程监测佩戴者的日常生活活动。
