Journal of Jilin University(Engineering and Technology Edition) ›› 2020, Vol. 50 ›› Issue (1): 1-18.doi: 10.13229/j.cnki.jdxbgxb20190264

   

Methods and technologies of human gait recognition

Yi-bin LI1(),Jia-min GUO1,Qin ZHANG1,2()   

  1. 1. School of Control Science and Engineering, Shandong University, Jinan 250061, China
    2. School of Electrical Engineering, University of Jinan, Jinan 250022, China
  • Received:2019-03-21 Online:2020-01-01 Published:2020-02-06
  • Contact: Qin ZHANG E-mail:liyb@sdu.edu.cn;cse_zhangq@ujn.edu.cn

Abstract:

Gait recognition is a hotspot in the field of pattern recognition, information security and clinical medicine in recent years. This paper mainly reviewed the four aspects of the gait data acquisition instruments, common gait databases, gait feature extraction and the methods of gait recognition. Firstly, the advantages and disadvantages, reliability, and application scenarios of the commonly used gait data acquisition instruments were introduced. Secondly, the common gait data sets were compared and analyzed from six aspects of the establishment of institutions, the sample size, the sampling rate, the environments, the instruments and the variables. Third, the existing gait parameter extraction methods were divided into model-based methods and non-model-based methods to elaborate in detail. Fourth, the gait recognition was introduced from support vector machine, auto-encoder and convolutional neural network, respectively, and the above methods from the two application directions of personal identification and abnormal gait identification were compared respectively. Finally, the shortcomings of current research and the future development directions were pointed out based on practical application.

Key words: artificial intelligence, gait data acquisition, gait database, gait feature extraction, gait recognition

CLC Number: 

  • TP18

Fig.1

Axivity AX3 sensor"

Fig.2

Common accelerometer-based pedometer"

Fig.3

Shoes with air pressure sensor and airbag"

Fig.4

ARTISTIC insole system"

Fig.5

Smart insole designed by Lin et al"

Fig.6

Ultrasonic wearable instrument designed by Qi et al"

Fig.7

Installation diagram of ultrasonic sensor at achilles tendon"

Fig.8

Vicon Vero series camera"

Fig.9

Kinect V1"

Fig.10

Kinect V2"

Table 1

Common gait dataset parameters"

建立机构库 名样本容量采样率/(帧?s-1)环 境仪 器变 量
加州大学圣地亚哥分校USDC[39]6人,42序列30室外1台相机位置
佐治亚理工大学Georgia[40,41]20人,188序列120室内,室外3D运动捕捉系统距离、速度
佐治亚理工大学Georgia [53]18人,106序列和8人,49序列30室内Ascension电磁动作捕捉系统**
卡耐基梅隆大学CMU Mobo[42]25人,600序列30室内6台索尼DXC 9000视角、衣着、速度、倾斜、抱球
南佛罗里达大学USF[43]

122人,

1 870序列

**室外2台相机、一个标定板地面、衣着、时间
中国科学院自动化所CASIA(A)[44]20人,240序列25室外一台松下NV?DX100EN数码相机视角
中国科学院自动化所CASIA(B)[45]124人,1 364序列25室内11台相机视角、衣着
中国科学院自动化所CASIA(C)[46]153人,1 530序列25室外(夜间)红外(热感)相机衣着、速度
南安普顿大学USMT[54]103人,1 005序列30multi?biometric tunnel8台Point Grey Dragonfly和1台IEEE1394**
南安普顿大学SOTON[55]超过100人,每人约8序列25室内,室外3台佳能MV30i和3台索尼TRV900E PAL视角
大阪大学OU?ISIR Treadmill A[56]34人,408序列60室内25台相机速度
大阪大学OU?ISIR Treadmill B[56]68人,1 350序列60室内25台相机服装
大阪大学OU?ISIR Treadmill D[56]185人,370序列60室内25台相机步态波动
大阪大学OU?ISIR LP[56]超过4 000人30室内2台相机视角
深圳大学SZU RGB?D[57]99人,792序列**室内ASUS Xtion PRO LIV?E、OpenNI SD?K视角
佩洛塔斯联邦大学Andresson[58]140人,每人500~600帧30室内Kinect**
PhysioNetGait Dynamics in Neuro?Degenerative Disease Data Base[59]64人,64份记录**室内力敏电阻疾病类型
PhysioNetGait in Parkinson’s Disease[59]166人100室内压力传感器帕金森

Fig.11

Illustration of the skeleton model"

Fig.12

Illustration of wireless inertial sensor placement"

Fig.13

Illustration of GEI"

Fig.14

Illustration of five channel PEI"

Fig.15

Illustration of MSI"

Fig.16

Illustration of Frieze Pattern and SVB frieze pattern"

Fig.17

Illustration of GEnI"

Fig.18

Number of papers retrieved by different keywords"

Table 2

Comparison of gait recognition methods in context of identification"

方法输入特征是否交叉视角是否改变服装条件数据集(平均)识别率/%
Multi?Task GANs[83]PEI×**OU?ISIR93.10
PEI×**CASIA(B)74.60
PEI×**USF94.70
SVM+ELDA[90]边界到质心的距离×**CASIA(B)97.50
边界到质心的距离×**SOTON95.56
SVM+(2D)2PCA+Gabor[92]GEI××**CASIA(B)86.12
自编码器[93]GEI×**CASIA(B)63.72
GEI×带包CASIA(B)40.38
GEI×穿大衣CASIA(B)26.04
GEI×**SZU RGB?D70.00
自编码器[94]GEI×**OU?ISIR Treadmill94.80
CNN[96]GEI×**OU?ISIR LP91.58
CNN[97]成对的步态轮廓序列×**OU?ISIR LP87.40
深度CNNs[98]GEI×**CASIA(B)84.67
GEI×带包CASIA(B)90.77
GEI×穿大衣CASIA(B)62.52
GEI×**OU?ISIR92.78
GEI×**USF96.70
HMM[99]滤波后的轮廓宽度信号××**CMU Mobo90.175
GAN[101]GEI×**CASIA(B)98.75
GEI×带包CASIA(B)72.73
GEI×穿大衣CASIA(B)41.50

Table 3

Comparison of gait recognition methods in context of abnormal gait recognition"

方 法特征提取仪器数据集输入特征异常步态类型识别率(平均)/%
SVM[5]Qualisys运动捕捉系统、Bertec Corp、Mega Electronics Ltd**下肢关节轨迹、地面反作用力、肌电图脑卒中98.21
SVM+k?means[91]ASUS Xtion PRO LIVE**足、腿和踝关节的速度信号、踝关节的角度信号膝伤84.00
二次贝叶斯[104]力敏电阻PhysioNet足部压力信号帕金森、亨廷顿氏病、肌肉萎缩性硬化86.96
KNN[105]Kinect V2**人体25个关节节点的位置和速度偏瘫,帕金森79.03
SVM[105]6台红外相机组成的运动捕捉系统**肩部、肘部、臀部、膝盖和脚踝的角度、时空参数偏瘫、帕金森、腰部疼痛、背部疼痛97.90
DT[105]6台红外相机组成的运动捕捉系统**肩部、肘部、臀部、膝盖和脚踝的角度、时空参数偏瘫、帕金森、腰部疼痛、背部疼痛90.10
KNN[105]6台红外相机组成的运动捕捉系统**肩部、肘部、臀部、膝盖和脚踝的角度、时空参数偏瘫、帕金森、腰部疼痛、背部疼痛100.00
RF[105]6台红外相机组成的运动捕捉系统**肩部、肘部、臀部、膝盖和脚踝的角度、时空参数偏瘫、帕金森、腰部疼痛、背部疼痛99.30
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