Biometric identification based on plantar pressure sensor and deep learning

被引：0

作者：

Zhou B. ^{[1
]}

Chen S. ^{[1
]}

Cheng Y. ^{[1
]}

Tan L. ^{[1
]}

Xiang M. ^{[1
]}

机构：

[1] School of Advanced Materials and Mechatronic Engineering, Hubei Minzu University, Enshi

来源：

Yi Qi Yi Biao Xue Bao/Chinese Journal of Scientific Instrument | 2021年 / 42卷 / 07期

关键词：

Biometric identification; Convolutional neural networks; Gait feature; Plantar pressure;

D O I：

10.19650/j.cnki.cjsi.J2107986

中图分类号：

学科分类号：

摘要：

According to recent research, plantar pressure can reflect various characteristics of the human body, which is promising for biometric identification. In our study, the feasibility and methodology of biometric identification by plantar pressure is discussed. Insoles with eight pressure sensors are used to collect over 14 000 steps of 14 participant as database. After that, the scientificity of classification is discussed by unsupervised learning, and the influence of ground conditions on pressure data is discussed. Convolutional neural network (CNN) is used as the classifier to evaluate the classification performance, and the effects of gait segmentation and multiple gait cycles on improving the accuracy are studied. Experimental results show that the accuracy of data classification after segmentation is 98.8%, while that without segmentation is 93.6%. When using 3 and 5 gait cycles for classification, the accuracy rise up to 99.4% and 99.8%. The results suggest that CNN with segmented data and selecting multiple gait cycles for classification has practical value in biometric identification utilizing plantar pressure. © 2021, Science Press. All right reserved.

引用

页码：108 / 115

页数：7

共 28 条

[1]

CASTRO F, SAVARIS W, ARAUJO R, Et al., Plantar pressure measurement system with improved isolated drive feedback circuit and ANN: Development and characterization, IEEE Sensors Journal, 20, 19, pp. 11034-11043, (2020)

[2]

MEI Q, GU Y, XIANG L, Et al., Foot shape and plantar pressure relationships in shod and barefoot populations, Biomechanics and modeling in mechanobiology, 19, pp. 1211-1224, (2019)

[3]

QAISER Z, FARAZ A, JOHNSON S., Feasibility study of a rapid evaluate and adjust device (READ) for custom foot orthoses prescription, IEEE Transactions on Neural Systems and Rehabilitation Engineering, 28, 2, pp. 1760-1770, (2020)

[4]

JIA X H, WANG T, LIU J Y, Et al., Gait recognition and intention perception method based on human body model mapping, Chinese Journal of Scientific Instrument, 41, 12, pp. 236-244, (2020)

[5]

WANG L, JONES D, CHAPMAN G J, Et al., A review of wearable sensor systems to monitor plantar loading in the assessment of diabetic foot ulcers, IEEE Transactions on Biomedical Engineering, 67, 7, pp. 1989-2004, (2020)

[6]

BELVEDERE C, GIACOMOZZI C, CARRARA C, Et al., Correlations between weight-bearing 3D bone architecture and dynamic plantar pressure measurements in the diabetic foot, Journal of Foot and Ankle Research, 13, 1, pp. 1-11, (2020)

[7]

ZHENG J, CAO H, CHEN D, Et al., Designing deep reinforcement learning systems for musculoskeletal modeling and locomotion analysis using wearable sensor feedback, IEEE Sensors Journal, 20, 16, pp. 9274-9282, (2020)

[8]

HEGDE N, BRIES M, SWIBAS T, Et al., Automatic recognition of activities of daily living utilizing insole-based and wrist-worn wearable sensors, IEEE Journal of Biomedical and Health Informatics, 22, 4, pp. 979-988, (2018)

[9]

HUA R, WANG Y., A customized convolutional neural network model integrated with acceleration-based smart insole toward personalized foot gesture recognition, IEEE Sensors Letters, 4, 4, pp. 2475-1472, (2020)

[10]

SI W, YANG G, XIANG G C, Et al., Gait identification using fractal analysis and support vector machine, Soft Computing, 23, 19, pp. 9287-9297, (2018)

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