A Convolutional Neural Network for Gait Recognition Based on Plantar Pressure Images

Li, Yanlin; Zhang, Dexiang; Zhang, Jun; Xun, Lina; Yan, Qing; Zhang, Jingjing; Gao, Qingwei; Xia, Yi

doi:10.1007/978-3-319-69923-3_50

Cited by 13 publications

(8 citation statements)

References 9 publications

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“…The structure of the neural network model proposed in this study is composed as shown in Table 1. The first convolution layer set the number of channels to 32, the kernel size to (3,3), the activation function to ReLU, and zero padding so that the size of the output image and the size of the input image were the same. The ReLU outputs 0 when the input value is less than 0 and outputs the input value as it is when it is greater than 0.…”

Section: Neural Network Model For Personal Classificationmentioning

confidence: 99%

“…As PP contains various information of the human body, the pressure data can be used to evaluate deformations of the foot, overall balance functions, and structural dysfunctions of the foot [1,2]. Recently, researches have been attempted to evaluate the accuracy of personal identification by extracting individual patterns from PP data [3,4].…”

Section: Introductionmentioning

confidence: 99%

“…Biorecognition based on gait video analysis has been studied extensively because video recording systems are easy to install and are inexpensive. However, lowered accuracy of personal identification due to privacy invasion issues, environmental changes due to climate, and clothing can be a limitation [3]. On the other hand, using the PP pattern measured at the sole of the foot as a biorecognition technology has more advantages than video-recording the human body, because it causes relatively less privacy invasion and one's PP pattern is difficult to imitate.…”

Section: Introductionmentioning

confidence: 99%

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Development of Plantar Pressure Measurement System and Personal Classification Study based on Plantar Pressure Image

2021

KSII TIIS

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In this study, a Velostat pressure sensor was manufactured to develop a plantar pressure measurement system and a C#-based application was developed to monitor and collect plantar pressure data in real time. In order to evaluate the characteristics of the proposed plantar pressure measurement system, the accuracy of plantar pressure index and personal classification was verified by comparing with MatScan, a commercial plantar pressure measurement system. As a result, the output characteristics according to the weight of the Velostat pressure sensor were evaluated and a trend line with the reliability of r 2 = 0.98 was detected. The Root Mean Square Error(RMSE) of the weighted area was 11.315 cm 2 , the RMSE of the x coordinate of Center of Pressure(CoPx) was 1.036 cm and the RMSE of the y coordinate of Center of Pressure(CoPy) was 0.936 cm. Finally, inaccuracy of personal classification, the proposed system was 99.47% and MatScan was 96.86%. Based on the advantage of being simple to implement and capable of manufacturing at low cost, it is considered that it can be applied to various fields of measuring vital signs such as sitting posture and breathing in addition to the plantar pressure measurement system.

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Section: Neural Network Model For Personal Classificationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Development of Plantar Pressure Measurement System and Personal Classification Study based on Plantar Pressure Image

2021

KSII TIIS

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“…Li et al proposed a new method based on plantar pressure images for gait recognition. The authors employed the convolution Neural Networks to automate the process of feature extraction and classification.…”

Section: Emerging Biometric Technologiesmentioning

confidence: 99%

State of the art and perspectives on traditional and emerging biometrics: A survey

Traoré

Alshahrani

Obaidat

2018

Security and Privacy

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The last three decades have seen a shift and impressive progress in the biometric technologies landscape. Several major real‐world applications of biometrics have been released and are currently being used around the world. At the same time, several new biometrics modalities have emerged and have started making an impact in securing sensitive data, information, and systems. We present in this paper a survey of traditional and emerging biometrics by emphasizing their connection to human body parts, behavior, and cognition. A review of the state of the art in the key technologies is carried out along with a discussion of major applications currently available in the real world.

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“…A number of works dealing with the subject of identifying people in relation to the way they move have been published since that time [ 6 , 7 , 8 , 9 , 10 ]. Connor and Ross categorized these studies on the basis of sensors used to obtain measurements and divided them into methods using [ 11 ]: video cameras [ 12 , 13 ], the measurement of pressure exerted by a person’s foot on the ground [ 14 , 15 , 16 ], accelerometers and other wearable devices [ 17 , 18 , 19 ], audio [ 20 , 21 ]. …”

Section: Introductionmentioning

confidence: 99%

Recognition of a Person Wearing Sport Shoes or High Heels through Gait Using Two Types of Sensors

Derlatka

Bogdan

2018

Sensors

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Biometrics is currently an area that is both very interesting as well as rapidly growing. Among various types of biometrics the human gait recognition seems to be one of the most intriguing. However, one of the greatest problems within this field of biometrics is the change in gait caused by footwear. A change of shoes results in a significant lowering of accuracy in recognition of people. The following work presents a method which uses data gathered by two sensors: force plates and Microsoft Kinect v2 to reduce this problem. Microsoft Kinect is utilized to measure the body height of a person which allows the reduction of the set of recognized people only to those whose height is similar to that which has been measured. The entire process is preceded by identifying the type of footwear which the person is wearing. The research was conducted on data obtained from 99 people (more than 3400 strides) and the proposed method allowed us to reach a Correct Classification Rate (CCR) greater than 88% which, in comparison to earlier methods reaching CCR’s of <80%, is a significant improvement. The work presents advantages as well as limitations of the proposed method.

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A Convolutional Neural Network for Gait Recognition Based on Plantar Pressure Images

Cited by 13 publications

References 9 publications

Development of Plantar Pressure Measurement System and Personal Classification Study based on Plantar Pressure Image

Development of Plantar Pressure Measurement System and Personal Classification Study based on Plantar Pressure Image

State of the art and perspectives on traditional and emerging biometrics: A survey

Recognition of a Person Wearing Sport Shoes or High Heels through Gait Using Two Types of Sensors

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