scholarly journals Self-Tuning Threshold Method for Real-Time Gait Phase Detection Based on Ground Contact Forces Using FSRs

Sensors ◽  
2018 ◽  
Vol 18 (2) ◽  
pp. 481 ◽  
Author(s):  
Jing Tang ◽  
Jianbin Zheng ◽  
Yang Wang ◽  
Lie Yu ◽  
Enqi Zhan ◽  
...  
Keyword(s):  
Real Time ◽  
Contact Forces ◽  
Ground Contact ◽  
Phase Detection ◽  
Threshold Method ◽  
Self Tuning ◽  
Gait Phase

Adaptive method for real-time gait phase detection based on ground contact forces

2015 ◽  
Vol 41 (1) ◽  
pp. 269-275 ◽  
Author(s):  
Lie Yu ◽  
Jianbin Zheng ◽  
Yang Wang ◽  
Zhengge Song ◽  
Enqi Zhan
Keyword(s):  
Real Time ◽  
Adaptive Method ◽  
Contact Forces ◽  
Ground Contact ◽  
Phase Detection ◽  
Gait Phase

scholarly journals Curve Similarity Model for Real-Time Gait Phase Detection Based on Ground Contact Forces

Sensors ◽  
2019 ◽  
Vol 19 (14) ◽  
pp. 3235
Author(s):  
Huacheng Hu ◽  
Jianbin Zheng ◽  
Enqi Zhan ◽  
Lie Yu
Keyword(s):  
Real Time ◽  
Error Rate ◽  
Reference Method ◽  
Contact Forces ◽  
Ground Contact ◽  
Phase Detection ◽  
Gait Patterns ◽  
Similarity Model ◽  
Curve Similarity ◽  
Degree Of Similarity

This paper proposed a new novel method to adaptively detect gait patterns in real time through the ground contact forces (GCFs) measured by load cell. The curve similarity model (CSM) is used to identify the division of off-ground and on-ground statuses, and differentiate gait patterns based on the detection rules. Traditionally, published threshold-based methods detect gait patterns by means of setting a fixed threshold to divide the GCFs into on-ground and off-ground statuses. However, the threshold-based methods in the literature are neither an adaptive nor a real-time approach. In this paper, the curve is composed of a series of continuous or discrete ordered GCF data points, and the CSM is built offline to obtain a training template. Then, the testing curve is compared with the training template to figure out the degree of similarity. If the computed degree of similarity is less than a given threshold, they are considered to be similar, which would lead to the division of off-ground and on-ground statuses. Finally, gait patterns could be differentiated according to the status division based on the detection rules. In order to test the detection error rate of the proposed method, a method in the literature is introduced as the reference method to obtain comparative results. The experimental results indicated that the proposed method could be used for real-time gait pattern detection, detect the gait patterns adaptively, and obtain a low error rate compared with the reference method.

FSR and IMU sensors-based human gait phase detection and its correlation with EMG signal for different terrain walk

Sensor Review ◽  
2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Sachin Negi ◽  
Shiru Sharma ◽  
Neeraj Sharma
Keyword(s):  
Gait Analysis ◽  
Real Time ◽  
Medial Gastrocnemius ◽  
Human Gait ◽  
Phase Detection ◽  
Zero Crossing ◽  
Content Type ◽  
Open Environment ◽  
Emg Signal ◽  
Gait Phase

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.

scholarly journals Real-Time Detection of Seven Phases of Gait in Children with Cerebral Palsy Using Two Gyroscopes

Sensors ◽  
2019 ◽  
Vol 19 (11) ◽  
pp. 2517 ◽  
Author(s):  
Ahad Behboodi ◽  
Nicole Zahradka ◽  
Henry Wright ◽  
James Alesi ◽  
Samuel. C. K. Lee
Keyword(s):  
Cerebral Palsy ◽  
Angular Velocity ◽  
Real Time ◽  
Phase Detection ◽  
Typically Developing ◽  
Typically Developing Children ◽  
Real Time System ◽  
Viable Solution ◽  
Children With Cerebral Palsy ◽  
Gait Phase

A recently designed gait phase detection (GPD) system, with the ability to detect all seven phases of gait in healthy adults, was modified for GPD in children with cerebral palsy (CP). A shank-attached gyroscope sent angular velocity to a rule-based algorithm in LabVIEW to identify the distinct characteristics of the signal. Seven typically developing children (TD) and five children with CP were asked to walk on treadmill at their self-selected speed while using this system. Using only shank angular velocity, all seven phases of gait (Loading Response, Mid-Stance, Terminal Stance, Pre-Swing, Initial Swing, Mid-Swing and Terminal Swing) were reliably detected in real time. System performance was validated against two established GPD methods: (1) force-sensing resistors (GPD-FSR) (for typically developing children) and (2) motion capture (GPD-MoCap) (for both typically developing children and children with CP). The system detected over 99% of the phases identified by GPD-FSR and GPD-MoCap. Absolute values of average gait phase onset detection deviations relative to GPD-MoCap were less than 100 ms for both TD children and children with CP. The newly designed system, with minimized sensor setup and low processing burden, is cosmetic and economical, making it a viable solution for real-time stand-alone and portable applications such as triggering functional electrical stimulation (FES) in rehabilitation systems. This paper verifies the applicability of the GPD system to identify specific gait events for triggering FES to enhance gait in children with CP.

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