Purpose

The purpose of this paper is to present gait analysis for five different terrains: level ground, ramp ascent, ramp descent, stair ascent and stair descent.

Design/methodology/approach

Gait analysis has been carried out using a combination of the following sensors: force-sensitive resistor (FSR) sensors fabricated in foot insole to sense foot pressure, a gyroscopic sensor to detect the angular velocity of the shank and MyoWare electromyographic muscle sensors to detect muscle’s activities. All these sensors were integrated around the Arduino nano controller board for signal acquisition and conditioning purposes. In the present scheme, the muscle activities were obtained from the tibialis anterior and medial gastrocnemius muscles using electromyography (EMG) electrodes, and the acquired EMG signals were correlated with the simultaneously attained signals from the FSR and gyroscope sensors. The nRF24L01+ transceivers were used to transfer the acquired data wirelessly to the computer for further analysis. For the acquisition of sensor data, a Python-based graphical user interface has been designed to analyze and display the processed data. In the present paper, the authors got motivated to design and develop a reliable real-time gait phase detection technique that can be used later in designing a control scheme for the powered ankle-foot prosthesis.

Findings

The effectiveness of the gait phase detection was obtained in an open environment. Both off-line and real-time gait events and gait phase detections were accomplished for the FSR and gyroscopic sensors. Both sensors showed their usefulness for detecting the gait events in real-time, i.e. within 10 ms. The heuristic rules and a zero-crossing based-algorithm for the shank angular rate correctly identified all the gait events for the locomotion in all five terrains.

Practical implications

This study leads to an understanding of human gait analysis for different types of terrains. A real-time standalone system has been designed and realized, which may find application in the design and development of ankle-foot prosthesis having real-time control feature for the above five terrains.

Originality/value

The noise-free data from three sensors were collected in the same time frame from both legs using a wireless sensor network between two transmitters and a single receiver. Unlike the data collection using a treadmill in a laboratory environment, this setup is useful for gait analysis in an open environment for different terrains.

中文翻译：

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基于FSR和IMU传感器的人体步态相位检测及其与不同地形行走的EMG信号的相关性

目的

本文的目的是介绍五种不同地形的步态分析：平地、斜坡上升、斜坡下降、楼梯上升和楼梯下降。

设计/方法/方法

步态分析是使用以下传感器的组合进行的：在脚垫中制造的力敏电阻 (FSR) 传感器来感应足部压力，陀螺仪传感器来检测小腿的角速度，以及 MyoWare 肌电图肌肉传感器来检测肌肉的活动。所有这些传感器都集成在 Arduino nano 控制器板周围，用于信号采集和调节。在本方案中，使用肌电图 (EMG) 电极从胫骨前肌和腓肠肌内侧获得肌肉活动，并将获得的 EMG 信号与来自 FSR 和陀螺仪传感器的同时获得的信号相关联。nRF24L01+ 收发器用于将采集的数据无线传输到计算机以进行进一步分析。对于传感器数据的采集，设计了一个基于 Python 的图形用户界面来分析和显示处理后的数据。在本文中，作者有动力设计和开发一种可靠的实时步态相位检测技术，该技术可用于以后设计电动踝足假肢的控制方案。

发现

在开放环境中获得步态检测的有效性。FSR 和陀螺仪传感器完成了离线和实时步态事件和步态相位检测。两种传感器都显示了它们在实时（即在 10 毫秒内）检测步态事件方面的有用性。小腿角速率的启发式规则和基于零交叉的算法正确识别了所有五种地形中运动的所有步态事件。

实际影响

这项研究有助于了解不同类型地形的人类步态分析。设计并实现了一个实时独立系统，可应用于具有上述五种地形实时控制功能的踝足假肢的设计和开发中。

原创性/价值

使用两个发射器和单个接收器之间的无线传感器网络，在同一时间范围内从两条腿收集来自三个传感器的无噪声数据。与在实验室环境中使用跑步机收集数据不同，此设置对于不同地形的开放环境中的步态分析非常有用。