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Experimental Data and Results
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Gait recognition using wearable motion recording sensors

Profile image of Davrondzhon GafurovDavrondzhon Gafurov

2009

https://doi.org/10.1155/2009/415817
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17 pages

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Abstract

This paper presents an alternative approach, where gait is collected by the sensors attached to the person's body. Such wearable sensors record motion (e.g. acceleration) of the body parts during walking. The recorded motion signals are then investigated for person recognition purposes. We analyzed acceleration signals from the foot, hip, pocket and arm. Applying various methods, the best EER obtained for foot-, pocket-, arm-and hip-based user authentication were 5%, 7%, 10% and 13%, respectively. Furthermore, we present the results of our analysis on security assessment of gait. Studying gait-based user authentication (in case of hip motion) under three attack scenarios, we revealed that a minimal effort mimicking does not help to improve the acceptance chances of impostors. However, impostors who know their closest person in the database or the genders of the users can be a threat to gait-based authentication. We also provide some new insights toward the uniqueness of gait in case of foot motion. In particular, we revealed the following: a sideway motion of the foot provides the most discrimination, compared to an up-down or forward-backward directions; and different segments of the gait cycle provide different level of discrimination.

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Spoof Attacks on Gait Authentication System
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Research in biometric gait recognition has increased. Earlier gait recognition works reported promising results, usually with a small sample size. Recent studies with a larger sample size confirm gait potential as a biometric from which individuals can be identified. Despite much research being carried out in gait recognition, the topic of vulnerability of gait to attacks has not received enough attention. In this paper, an analysis of minimal-effort impersonation attack and the closest person attack on gait biometrics are presented. Unlike most previous gait recognition approaches, where gait is captured using a (video) camera from a distance, in our approach, gait is collected by an accelerometer sensor attached to the hip of subjects. Hip acceleration in three orthogonal directions (up-down, forward-backward, and sideways) is utilized for recognition. We have collected 760 gait sequences from 100 subjects. The experiments consisted of two parts. In the first part, subjects walked in their normal walking style, and using the averaged cycle method, an EER of about 13% was obtained. In the second part, subjects were trying to walk as someone else. Analysis based on FAR errors indicates that a minimal-effort impersonation attack on gait biometric does not necessarily improve the chances of an impostor being accepted. However, attackers with knowledge of their closest person in the database can be a serious threat to the authentication system.

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Paper1 Spoof Attacks on Gait Authentication System
Davrondzhon Gafurov

Research in biometric gait recognition has increased. Earlier gait recognition works reported promising results, usually with a small sample size. Recent studies with a larger sample size confirm gait potential as a biometric from which individuals can be identified. Despite much research being carried out in gait recognition, the topic of vulnerability of gait to attacks has not received enough attention. In this paper, an analysis of minimal-effort impersonation attack and the closest person attack on gait biometrics are presented. Unlike most previous gait recognition approaches, where gait is captured using a (video) camera from a distance, in our approach, gait is collected by an accelerometer sensor attached to the hip of subjects. Hip acceleration in three orthogonal directions (up-down, forward-backward, and sideways) is utilized for recognition. We have collected 760 gait sequences from 100 subjects. The experiments consisted of two parts. In the first part, subjects walked in their normal walking style, and using the averaged cycle method, an EER of about 13% was obtained. In the second part, subjects were trying to walk as someone else. Analysis based on FAR errors indicates that a minimal-effort impersonation attack on gait biometric does not necessarily improve the chances of an impostor being accepted. However, attackers with knowledge of their closest person in the database can be a serious threat to the authentication system.

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Robustness of Biometric Gait Authentication Against Impersonation Attack
Davrondzhon Gafurov

Lecture Notes in Computer Science, 2006

This paper presents a gait authentication based on timenormalized gait cycles. Unlike most of the previous works in gait recognition, using machine vision techniques, in our approach gait patterns are obtained from a physical sensor attached to the hip. Acceleration in 3 directions: up-down, forward-backward and sideways of the hip movement, which is obtained by the sensor, is used for authentication. Furthermore, we also present a study on the security strength of gait biometric against imitating or mimicking attacks, which has not been addressed in biometric gait recognition so far. 1 Gait is a person's manner of walking.

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An unsupervised approach for gait-based authentication
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2015 IEEE 12th International Conference on Wearable and Implantable Body Sensor Networks (BSN), 2015

Similar to fingerprint and iris pattern, everyone's gait is unique, and gait has been proposed as a biometric feature for security applications. This paper presents a lightweight accelerometer-based technique for user authentication on smart wearable devices. Designed as an unsupervised classification approach, the proposed authentication technique can learn the user's gait pattern automatically when the user first starts wearing the device. Anomaly detection is then used to verify the device owner. The technique has been evaluated both in controlled and uncontrolled environments, with 20 and 6 healthy volunteers respectively. The Equal Error Rate (EER) in the controlled environments ranged from 5.7% (waist-mounted sensor) to 8.0% (trouser pocket). In the uncontrolled experiment, the device was put in the subject's trouser pocket, and the results were similar to the respective supervised experiment (EER=9.7%).

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User authentication based on foot motion
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    Baojun Chen

    International Journal of Advanced Robotic Systems, 2014

    Locomotion mode recognition is one of the key aspects of control of intelligent prostheses. This paper presents a wireless wearable multi-sensor system for locomotion mode recognition. The sensor suit of the system includes three inertial measurement units (IMUs) and eight force sensors. The system was built to measure both kinematic (tilt angles) and dynamic (ground contact forces) signals of human gaits. To evaluate the recognition performance of the system, seven motion modes and sit-to-stand transition were monitored. With a linear discriminant analysis (LDA) classifier, the proposed system can accurately classify the current states. The overall motion mode recognition accuracy was 99.9% during the stance phase and 98.5% during the swing phase. For sit-to-stand transition recognition, the average accuracy was 99.9%. These promising results show the potential of the designed system for the control of intelligent prostheses.

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    A Study on Secure Medical-Contents Strategies with DRM Based on Cloud Computing
    Jongsun Choi

    Journal of Healthcare Engineering

    Many hospitals and medical clinics have been using a wearable sensor in its health care system because the wearable sensor, which is able to measure the patients’ biometric information, has been developed to analyze their patients remotely. The measured information is saved to a server in a medical center, and the server keeps the medical information, which also involves personal information, on a cloud system. The server and network devices are used by connecting each other, and sensitive medical records are dealt with remotely. However, these days, the attackers, who try to attack the server or the network systems, are increasing. In addition, the server and the network system have a weak protection and security policy against the attackers. In this paper, it is suggested that security compliance of medical contents should be followed to improve the level of security. As a result, the medical contents are kept safely.

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    Gait characteristic analysis and identification based on the iPhone's accelerometer and gyrometer
    Jacob Banda

    Sensors (Basel, Switzerland), 2014

    Gait identification is a valuable approach to identify humans at a distance. In this paper, gait characteristics are analyzed based on an iPhone's accelerometer and gyrometer,and a new approach is proposed for gait identification. Specifically, gait datasets are collected by the triaxial accelerometer and gyrometer embedded in an iPhone. Then, the datasets are processed to extract gait characteristic parameters which include gait frequency, symmetry coefficient, dynamic range and similarity coefficient of characteristic curves. Finally, a weighted voting scheme dependent upon the gait characteristic parameters is proposed forgait identification. Four experiments are implemented to validate the proposed scheme. The attitude and acceleration solutions are verified by simulation. Then the gait characteristics are analyzed by comparing two sets of actual data, and the performance of the weighted voting identification scheme is verified by 40 datasets of 10 subjects.

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    Akıllı Telefonların İvmeölçer Sensörü Yardımıyla Yürüyüş Deseni Analizi
    İBRAHİM KARSLIOĞLU

    Afyon Kocatepe University Journal of Sciences and Engineering, 2021

    Spor alanlarında insan hareketlerini ölçme yeteneği performans ölçüm ve gelişimi için önemli konular arasındadır. Bu durum aynı zamanda klinik değerlendirmelerin de önemli bir parçasıdır. Özellikle elektromanyetik sistemler insan hareketlerini değerlendirmek için en yaygın kullanılan yöntemler arasında yer alır. Buradaki çalışmada 100 metre uzunluğunda bir koridorda 50 farklı kişinin yürüme verileri kullanılmıştır. Yürüme verileri akıllı telefon için geliştirilen bir yazılım ile ivmeölçer sensöründen elde edilmiştir. Verilere üç boyutlu Local Binary Pattern (LBP) yöntemi uygulanmış ve toplam 768 öznitelik çıkarılmıştır. Farklı sınıflandırma algoritmaları ile testler yapılmış ve Subspace KNN ile %97,2 başarılı sınıflandırma elde edilmiştir. Cinsiyete göre yapılan sınıflandırmada ise %99,7 başarılı sınıflandırma elde edilmiştir. Bu yöntem ile yürüme bozukluğu tespitinde yüksek maliyetli cihazlar yerine daha ekonomik yöntemler geliştirileceği düşünülmektedir.

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