Development of Ground Reaction Force Sensor for Gait Phase Classification of Powered Gait Orthosis for Paraplegic Patients

Author(s):  
S. J. Hwang ◽  
J. H. Bae ◽  
G. S. Kim
Author(s):  
Jiajia Zheng ◽  
Jianhua Chen ◽  
Mingxing Yang ◽  
Song Chen

Gait analysis is of great importance to ensure that gait phases induced by robotic exoskeleton are tailored to each individual and external complex environments. The objective of this work is to develop a pressure insole system with redundant functionality for gait phase classification based on the analysis of ground reaction force on unstructured terrains. A support vector machine optimized by particle swarm optimization was proposed for classifying four gait phases including initial contact, mid stance, terminal stance and swing phase. Seven pressure sensors are employed according to the plantar distribution contour of ground reaction force and walking data acquisition is conducted on treadmill, concrete pavement and wild grassland, respectively. Two classifiers, support vector machine-based classifier I and PSO-SVM-based classifier II are constructed on the basis of gait data set obtained on treadmill. Experimental results showed that the proposed PSO-SVM algorithm exhibits distinctive advantages on gait phase classification and improves the classification accuracy up to 32.9%–42.8%, compared with that of classifier based solely on support vector machine. In addition, some unwanted errors, intentional attacks or failures can be successfully solved with fast convergence rate and good robustness by using particle swarm optimization.


2020 ◽  
Vol 20 (18) ◽  
pp. 10851-10861 ◽  
Author(s):  
Hyo Seung Han ◽  
Juyoung Yoon ◽  
Seungkyu Nam ◽  
Sangin Park ◽  
Dong Jin Hyun

2012 ◽  
Vol 24 (5) ◽  
pp. 828-837 ◽  
Author(s):  
Kazuya Kawamura ◽  
◽  
Yuya Morita ◽  
Jun Okamoto ◽  
Kohei Saito ◽  
...  

In gait rehabilitation, achieving a gait analysis method using a simple system during long-distance walking is important. This method is required to measure all gait parameters in a single measurement. In addition, it is required that the measurement system is not spatially constrained. Therefore, we have been developing a gait tracking system with acceleration sensors for long-distance gait rehabilitation. In this paper, we describe a gait phase detection method using foot acceleration data for estimating ground reaction force during long-distance gait rehabilitation. To develop this method, we focused on the jerk of each foot in vertical axis direction. Using two accelerometers mounted on the left and right feet, we carried out three experiments. First, we measured the jerk of each foot during a free gait to verify the relation with the walking speed. Second, we measured the jerk of each foot during walking faster than normal for each subject. We then compared these results with the results of first experiments. Finally, we measured the jerk of each foot during left-right asymmetrical walking. The results confirmed that gait phase could be detected using the jerk of each leg, calculated from acceleration data in vertical axis direction. In particular, the timing of Heel-contact / Toe-off could be obtained with an average error of 0.03 s. And as a preliminary study, we estimated the ground reaction force using the one of the results.


2013 ◽  
Vol 2013.19 (0) ◽  
pp. 127-128
Author(s):  
Kazuhiro SUGAWARA ◽  
Nozomu KOIKE ◽  
Naoya KOSAKI ◽  
Jyun KOBAYASHI ◽  
Yoshihiro KAI

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