Stride and cadence as a biometric in automatic person identification and verification

BenAbdelkader, Chiraz; Cutler, Ross; Davis, Larry S.

doi:10.1109/afgr.2002.1004182

Cited by 194 publications

(148 citation statements)

References 11 publications

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“…The choice of taking 3 sec window has been motivated by: i) the cadence of an average person walking is within [90, 130] steps/min [33,34]; ii) at least a full walking cycle (two steps) is preferred on each window sample. Figure 3 shows samples of acceleration shapes.…”

Section: Preprocessingmentioning

confidence: 99%

UniMiB SHAR: A Dataset for Human Activity Recognition Using Acceleration Data from Smartphones

Micucci¹,

Mobilio²,

Napoletano³

2017

Preprint

106

110

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Smartphones, smartwatches, fitness trackers, and ad-hoc wearable devices are being increasingly used to monitor human activities. Data acquired by the hosted sensors are usually processed by machine-learning-based algorithms to classify human activities. The success of those algorithms mostly depends on the availability of training (labeled) data that, if made publicly available, would allow researchers to make objective comparisons between techniques. Nowadays, publicly available data sets are few, often contain samples from subjects with too similar characteristics, and very often lack of specific information so that is not possible to select subsets of samples according to specific criteria. In this article, we present a new smartphone accelerometer dataset designed for activity recognition. The dataset includes 11,771 activities performed by 30 subjects of ages ranging from 18 to 60 years. Activities are divided in 17 fine grained classes grouped in two coarse grained classes: 9 types of activities of daily living (ADL) and 8 types of falls. The dataset has been stored to include all the information useful to select samples according to different criteria, such as the type of ADL performed, the age, the gender, and so on. Finally, the dataset has been benchmarked with two different classifiers and with different configurations. The best results are achieved with k-NN classifying ADLs only, considering personalization, and with both windows of 51 and 151 samples.

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Section: Preprocessingmentioning

confidence: 99%

UniMiB SHAR: A Dataset for Human Activity Recognition Using Acceleration Data from Smartphones

Micucci¹,

Mobilio²,

Napoletano³

2017

Preprint

106

110

View full text Add to dashboard Cite

show abstract

“…Consequently, they are unsuitable for a wide range of real life applications. The early parametric gait recognition method was proposed by BenAbdelkader et al [9], where they estimated two spatiotemporal parameters of gait, namely stride length and cadence as two distinctive biometric traits. Following to that, Urtasun and Fua [10] In a similar way suggested by Yam et al [11] human leg motion and structure is modeled to differentiate between gait signatures extracted from walking and running samples.…”

Section: Related Workmentioning

confidence: 99%

Human gait identification using Kinect sensor

Sabir

Ahmed

Al-Talabani

et al. 2017

KJAR

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“…unwrapped silhouette [8]; silhouette similarity [9]; relational statistics [10]; self similarity [11]; key frame analysis [12]; frieze patterns [13]; area [14]; symmetry [15]; key poses [16] eigenspace sequences [19]; average silhouette [20]; moments [21]; ellipsoidal fits [22]; kinematic features [23]; gait style and content [24] stride parameters [25]; human parameters [26]; joint trajectories [27]; hidden Markov model [28][29]; articulated model [32]; dual oscillator [33]; linked feature trajectories [34] video oscillations [30] Accurate infrared target tracking is critical in many military weapons systems where common knowledge indicates that improving infrared target detection and tracking has the potential to simultaneously minimize unwanted collateral damage and maximize the probability of successful target elimination [35].…”

Section: Without Motion With Motionmentioning

confidence: 99%