Influence of different walking speeds and surfaces on accelerometer-based biometric gait recognition

Muaaz, Muhammad; Nickel, Claudia

doi:10.1109/tsp.2012.6256346

Cited by 36 publications

(36 citation statements)

References 8 publications

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“…For determining gait characteristics, they asked each participant to walk across the surface with their normal pace. Other studies such as presented in [12,31,32] have also demonstrated the influence of surface.…”

Section: Related Workmentioning

confidence: 83%

Risk of Falling Assessment on Different Types of Ground Using the Instrumented TUG

Brahem

Ayena²,

Otis

et al. 2015

2015 IEEE International Conference on Systems, Man, and Cybernetics

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Abstract-Degradation of postural control observed with aging is responsible for balance problems in the elderly, especially during the activity of walking. This gradual loss of performance generates abnormal gait, and therefore increases the risk of falling. This risk worsens in people with neuronal pathologies like Parkinson and Ataxia diseases. Many clinical tests are used for fall assessment such as the Timed up and go (TUG) test. Recently, many works have improved this test by using instrumentation, especially body-worn sensors. However, during the instrumented TUG (iTUG) test, the type of ground can influence risk of falling. In this paper, we present a new methodology for fall risk assessment based on quantitative gait parameters measurement in order to improve instrumented TUG test. The proposed measurement unit is used on different types of ground, which is known to affect human gait. The methodology is closer to the real walking environment and therefore enhances ability to differentiate risks level. Our system assesses the risk of falling's level by quantitative measurement of intrinsic gait parameters using fuzzy logic. He is also able to measure environmental parameters such as temperature, humidity and atmospheric pressure for a better evaluation of the risk in activities of daily living (ADL).

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Section: Related Workmentioning

confidence: 83%

Risk of Falling Assessment on Different Types of Ground Using the Instrumented TUG

Brahem

Ayena²,

Otis

et al. 2015

2015 IEEE International Conference on Systems, Man, and Cybernetics

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“…For our experiments, we used the same data set as employed in [21]. This biometric gait data was collected using an Android phone Google G1.…”

Section: Description Of Dataset Usedmentioning

confidence: 99%

“…We used only x-axis data for our experiments because it is more discriminant as compared to y and z-axis [21]. One recorded file contains two walks as shown in figure 3.…”

Section: Data Preprocessingmentioning

confidence: 99%

An Analysis of Different Approaches to Gait Recognition Using Cell Phone Based Accelerometers

Muaaz

Mayrhofer

2013

Proceedings of International Conference on Advances in Mobile Computing &Amp; Multimedia - MoMM '13

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Biometric gait authentication using Personal Mobile Device (PMD) based accelerometer sensors offers a user-friendly, unobtrusive, and periodic way of authenticating individuals on PMD. In this paper, we present a technique for gait cycle extraction by incorporating the Piecewise Linear Approximation (PLA) technique. We also present two new approaches to classify gait features extracted from the cycle-based segmentation by using Support Vector Machines (SVMs); a) pre-computed data matrix, b) pre-computed kernel matrix. In the first approach, we used Dynamic Time Warping (DTW) distance to compute data matrices, and in the later DTW is used for constructing an elastic similarity measure based kernel function called Gaussian Dynamic Time Warp (GDTW) kernel. Both approaches utilize the DTW similarity measure and can be used for classifying equal length gait cycles, as well as different length gait cycles. To evaluate our approaches we used normal walk biometric gait data of 51 participants. This gait data is collected by attaching a PMD to the belt around the waist, on the right-hand side of the hip. Results show that these new approaches need to be studied more, and potentially lead us to design more robust and reliable gait authentication systems using PMD based accelerometer sensor.

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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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Influence of different walking speeds and surfaces on accelerometer-based biometric gait recognition

Cited by 36 publications

References 8 publications

Risk of Falling Assessment on Different Types of Ground Using the Instrumented TUG

Risk of Falling Assessment on Different Types of Ground Using the Instrumented TUG

An Analysis of Different Approaches to Gait Recognition Using Cell Phone Based Accelerometers

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

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