Gait identification using accelerometer on mobile phone

Thang, Hoang Minh; Viet, Vo Quang; Thuc, Nguyen Dinh; Choi, Deokjai

doi:10.1109/iccais.2012.6466615

Cited by 105 publications

(71 citation statements)

References 12 publications

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“…Some gait-based biometric work utilizes the data within the time domain [12,13,14], but other successful systems map the time-series sensor data into examples using a sliding window approach, which permits the use of conventional classifier induction systems that cannot handle time-series data. This study utilizes the same sliding window approach employed in our prior smartphone-based study [2].…”

Section: Feature Extraction and Data Transformationmentioning

confidence: 99%

Smartwatch-based biometric gait recognition

Johnston

Weiss

2015

2015 IEEE 7th International Conference on Biometrics Theory, Applications and Systems (BTAS)

112

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The advent of commercial smartwatches provides an intriguing new platform for mobile biometrics. Like their smartphone counterparts, these mobile devices can perform gait-based biometric identification because they too contain an accelerometer and a gyroscope. However, smartwatches have several advantages over smartphones for biometric identification because users almost always wear their watch in the same location and orientation. This location (i.e. the wrist) tends to provide more information about a user's movements than the most common location for smartphones (pockets or handbags). In this paper we show the feasibility of using smartwatches for gait-based biometrics by demonstrating the high levels of accuracy that can result from smartwatch-based identification and authentication models. Applications of smartwatch-based biometrics range from a new authentication challenge for use in a multifactor authentication system to automatic personalization by identifying the user of a shared device.

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Section: Feature Extraction and Data Transformationmentioning

confidence: 99%

Smartwatch-based biometric gait recognition

Johnston

Weiss

2015

2015 IEEE 7th International Conference on Biometrics Theory, Applications and Systems (BTAS)

112

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“…They are then stored as referred templates that correspond to the individual. Various distance metrics, such as Dynamic Time Warping (DTW) [9,19,14], Euclidean distance [8,9], auto-correlation [13], and nearest neighbors [11] are used for calculating the similarity score between a given pattern and the referred templates. The ML method is the most popular approach that is used in pattern recognition areas.…”

Section: Related Workmentioning

confidence: 99%

“…For each pattern, features are extracted in time domain, frequency domain, and wavelet domain, or by special techniques such as time delay embedding [18]. Extracted feature vectors are then classified using supervised classifiers like HMM [16], SVM [14,15,17,18,20], and ANN [5], LDA [5]. Some other works propose hybrid approaches in which either distance metrics, such as DTW [7] and Euclidean [10,12] are used to measure the similarity scores of features that have been extracted in time and frequency domains, or where the similarity scores of gait templates can be considered as features that are used for classification [6].…”

Section: Related Workmentioning

confidence: 99%

Adaptive Cross-Device Gait Recognition Using a Mobile Accelerometer

Hoang¹,

Nguyen²,

Luong³

et al. 2013

Journal of Information Processing Systems

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Abstract-Mobile authentication/identification has grown into a priority issue nowadays because of its existing outdated mechanisms, such as PINs or passwords. In this paper, we introduce gait recognition by using a mobile accelerometer as not only effective but also as an implicit identification model. Unlike previous works, the gait recognition only performs well with a particular mobile specification (e.g., a fixed sampling rate). Our work focuses on constructing a unique adaptive mechanism that could be independently deployed with the specification of mobile devices. To do this, the impact of the sampling rate on the preprocessing steps, such as noise elimination, data segmentation, and feature extraction, is examined in depth. Moreover, the degrees of agreement between the gait features that were extracted from two different mobiles, including both the Average Error Rate (AER) and Intra-class Correlation Coefficients (ICC), are assessed to evaluate the possibility of constructing a device-independent mechanism. We achieved the classification accuracy approximately 91.33 ± 0.67 % for both devices, which showed that it is feasible and reliable to construct adaptive cross-device gait recognition on a mobile phone.

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“…To overcome this situation, the obtained data need to be increased its sampling rate depending on the number of samples per second (Hz) which also called as interpolation. According to [11], there are generally 2 methods in implementing interpolation on the gait signal which is linear [12][13][14][15][16][17][18] and cubic spline [12]. There are also papers that do not mentioned the use of any interpolation in gait application [19][20].…”

Section: Introductionmentioning

confidence: 99%

Gait Identification using Smartphone Handheld Placement with Linear Interpolation Factor, Single Magnitude and One-vs-One Classifier Mapping

Raziff¹,

Sulaiman²,

Mustapha³

et al. 2017

IJIES

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Gait signal of a person can be easily obtained using a smartphone sensor. To get the source of the signal, the smartphone need to be placed in the pocket, pouch or attached to other parts of the body. In the real world application, it is hard to place the device on the mentioned position. The easiest way is to put it on hand. In another issue, the single magnitude is known in the use of multiple orientations. However, this method may discard useful features for machine learning classification. Another problem is that the signal captured using a smartphone is not in a fix sampling rate and in the small distance, hence interpolation needs to be applied so that the sampling can be in a fix sequence with more fix point data. However, too much application of interpolation may result in low prediction rate. Finally, a multiclass dataset may contain overlapped class boundary which produces low accuracy on a single classifier mapping. In this paper, hand based smartphone placement position is implemented and evaluated. Single magnitude application is also evaluated in representing multiple positions of a person into one signal. Besides that, the linear interpolation factor is introduced in sampling the signal. Lastly, OvO classification model is implemented in binarizing the multiclass gait dataset. From the experiment, it shows that using the mentioned method do produce satisfactory result hence opening a new gateway in a better gait identification/recognition system.

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Gait identification using accelerometer on mobile phone

Cited by 105 publications

References 12 publications

Smartwatch-based biometric gait recognition

Smartwatch-based biometric gait recognition

Adaptive Cross-Device Gait Recognition Using a Mobile Accelerometer

Gait Identification using Smartphone Handheld Placement with Linear Interpolation Factor, Single Magnitude and One-vs-One Classifier Mapping

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