Gait Modeling for Human Identification

Huang, Bufu; Chen, Meng; Huang, Panfeng; Xu, Yangsheng

doi:10.1109/robot.2007.364224

Cited by 38 publications

(37 citation statements)

References 5 publications

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“…Huang et al [23] identified a human subject based on the wearer's gait against eight other human subjects. Jochen Klucken et al [24] were able to successfully distinguish PD patients from healthy subjects with an accuracy of 81%.…”

Section: Gait Analysismentioning

confidence: 99%

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A Comparative Review of Footwear-Based Wearable Systems

2016

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Abstract:Footwear is an integral part of daily life. Embedding sensors and electronics in footwear for various different applications started more than two decades ago. This review article summarizes the developments in the field of footwear-based wearable sensors and systems. The electronics, sensing technologies, data transmission, and data processing methodologies of such wearable systems are all principally dependent on the target application. Hence, the article describes key application scenarios utilizing footwear-based systems with critical discussion on their merits. The reviewed application scenarios include gait monitoring, plantar pressure measurement, posture and activity classification, body weight and energy expenditure estimation, biofeedback, navigation, and fall risk applications. In addition, energy harvesting from the footwear is also considered for review. The article also attempts to shed light on some of the most recent developments in the field along with the future work required to advance the field.

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Section: Gait Analysismentioning

confidence: 99%

“…Systems in [5,11,19,23] would need further human subject studies as they have not yet been fully tested. Additionally, it is important to note that none of the above-described systems have undergone a longitudinal free-living study, which is essential to understand the gait behavior of wearers in community living.…”

Section: Gait Analysismentioning

confidence: 99%

A Comparative Review of Footwear-Based Wearable Systems

2016

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“…Accelerometer, waist at center back 5 sequences on different days Huang et al [14] 9 NA Both accelerometer and gyroscope attached on shoe Our database 736 382 males and 354 females, ages ranging from 2 to 78 years Waist, both accelerometer and gyroscope, 2 sequences/subject database was frequently used to evaluate the algorithms in the same research group [12,4,8,7]. This database supplies quite a large variation of age and gender for reliable performance evaluation and they reached an performance evaluation of 13% EER (Equal Error Rate) [12].…”

Section: Related Workmentioning

confidence: 99%

Performance evaluation of gait recognition using the largest inertial sensor-based gait database

Trung

Makihara

Nagahara

et al. 2012

2012 5th IAPR International Conference on Biometrics (ICB)

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“…To be specific, let be the feature under examination, and , , and are the empirical probability density functions of for the three different walking styles. We define the speed-dependence measure (SDM) for feature as follows: (4) where is the symmetrical KLD between the two distributions and , defined as follows:…”

Section: B Feature Selectionmentioning

confidence: 99%

“…While most research for people identification using gait has been focused on computer vision based techniques, there has been research addressing gait recognition using foot pressure information [1], [4]- [6], [8]- [12], [16]- [18], and [21]. For example, Orr and Abowd [8] have researched people identification based on the pressure profile over time during a foot step on a load-cell sensor.…”

Section: Introductionmentioning

confidence: 99%

People Identification Using Floor Pressure Sensing and Analysis

2010

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This paper presents an approach to people identification based on gait using floor pressure data. By using a large-area, high-resolution pressure sensing floor, we are able to obtain both the 1D pressure profile and 2D position trajectories of the centers of pressure (COP) of both feet to form a 3D COP trajectories over a footstep. From the 3D COP trajectories of a pair of footsteps, a set of features are extracted and used together with other features such as the mean pressure and stride length for people identification. The Fisher linear discriminant is used as the classifier. The speed-invariant properties of different features are examined and we have shown that the pressure features have good speed-invariance properties, much better than the stride length feature. The proposed algorithm has been extensively tested using a floor pressure dataset collected from 11 subjects in different walking styles, including varying speed walking and free style walking. Decent people identification results have been obtained using the proposed method with an average recognition rate of 92.3% and false alarm rate of 6.79% using the best performing feature set.

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Gait Modeling for Human Identification

Cited by 38 publications

References 5 publications

A Comparative Review of Footwear-Based Wearable Systems

A Comparative Review of Footwear-Based Wearable Systems

Performance evaluation of gait recognition using the largest inertial sensor-based gait database

People Identification Using Floor Pressure Sensing and Analysis

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