Footstep-Identification System Based on Walking Interval

Wang, Xuan; Yang, Tengfei; Yu, Yao; Zhang, Ruixin; Guo, Fang

doi:10.1109/mis.2015.5

Cited by 29 publications

(36 citation statements)

References 15 publications

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“…Finally, the classification accuracies under automobile or manufactory environmental sounds were evaluated. A comparison of the performance of our method with the recent footstep identification methods including Shoji's [11], Itai's [13], Carvalho's [15], Miyoshi's [16] and our early work [5] Table 1. It can be concluded from these results that the proposed method yields a better performance in terms of precisions and recalls compared with the other methods.…”

Section: Simulation Results and Performance Analysismentioning

confidence: 99%

“…In contrast with other behavioural traits such as voice and keystroke, there are few published research papers on footstep identification [5]. In 2004, Shoji et al [10] first proposed a footstep identification method using mel-frequency analysis.…”

Section: Related Workmentioning

confidence: 99%

“…In contrast with other behavioural traits such as voice and keystroke, there are few published research papers on footstep identification [5]. In 2004, Shoji et al .…”

Section: Related Workmentioning

confidence: 99%

“…In our early work [5], we calculated the footstep duration and interval times for the features. The diversification similarity degree is introduced for similarity measure, which is invariant to the change of examinee's psychological makeup between two footstep recording courses.…”

Section: Related Workmentioning

confidence: 99%

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Robust footstep identification system based on acoustic local features

Guo

Wang

2017

IET biom.

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Footsteps, as a main kind of behavioural trait are a universally available signal. However, it remains a challenging problem to construct a robust footstep authentication system. Since, in practise, footsteps are usually accompanied with noisy and environmental sounds, it is difficult to extract stable footstep features from the mixed sounds. This study describes a novel robust footstep identification system. To extract stable features from the footsteps mixed with noisy or environmental sounds, a robust acoustic local feature extraction method is proposed. In the proposed method, the main frequency components of footsteps are determined, and then their local distributions and variations in time-frequency domain are obtained and regarded as the acoustic local features. These local features are robust to white noise, pink noise and invariant to the intensity of the footsteps. However, the conventional pattern recognition methods are not suitable for them due to that these local features are observably different from the frequently used acoustic features, and so the authors introduce a Bayesian decision classifier to implement footstep identification. Theoretical and experimental results demonstrate that this system is relatively robust to white noise, pink noise, environmental sounds and yields a better classification performance compared with the existing methods.

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Section: Simulation Results and Performance Analysismentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

“…In contrast with other behavioural traits such as voice and keystroke, there are few published research papers on footstep identification [5]. In 2004, Shoji et al .…”

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

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Robust footstep identification system based on acoustic local features

Guo

Wang

2017

IET biom.

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“…It also can be applied in smart parking areas [17]. In addition to security systems, biometric factors can be used as recognition systems [18].…”

Section: Biometricmentioning

confidence: 99%

Footstep Recognition Using Mel Frequency Cepstral Coefficients and Artificial Neural Network

Siagian

Nuha

Yasirandi

2020

RESTI

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Footstep recognition is relatively new biometrics and based on the learning of footsteps signals captured from people walking on the sensing area. The footstep signals classification process for security systems still has a low level of accuracy. Therefore, we need a classification system that has a high accuracy for security systems. Most systems are generally developed using geometric and holistic features but still provide high error rates. In this research, a new system is proposed by using the Mel Frequency Cepstral Coefficients (MFCCs) feature extraction, because it has a good linear frequency as a copycat of the human hearing system and Artificial Neural Network (ANN) as a classification algorithm because it has a good level of accuracy with a dataset of 500 recording footsteps. The classification results show that the proposed system can achieve the highest accuracy of validation loss value 57.3, Accuracy testing 92.0%, loss value 193.8, and accuracy training 100%, the accuracy results are an evaluation of the system in improving the foot signal recognition system for security systems in the smart home environment.

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Magnetically Actuated Surface Microstructures for Efficient Transport and Tunable Separation of Droplets and Solids

Kravanja,

Kriegl,

Hribar

et al. 2023

Adv Eng Mater

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Efficient transportation of droplets (≈101–102 μL) and small solid objects (≈101–102 mm3) have important applications in many fields, such as microfluidics, lab‐on‐a‐chip devices, drug delivery, etc. A novel multifunctional surface consisting of a periodic array of micro‐lamellae from a soft magnetoactive elastomer on a plastic substrate is reported for these purposes. The physical origin of the propulsion is the bending of soft magnetic lamellae in nonuniform magnetic fields, which is also observed in uniform magnetic fields. The magnetoactive surface is fabricated using a facile and rapid method of laser ablation. The propulsion of items is realized using a four‐pole rotating magnet. This results in a cyclic lamellar fringe motion over the microstructured surface and brings an advantage of easy reciprocation of transport by rotation reversal. Two modes of object transportation are identified: “pushing” mode for precise control of droplet and solid positioning and “bouncing” mode for heavier solid objects transportation. A water droplet of 5 μL or a glass sphere with a 2.1 mm diameter can be moved at a maximum speed of 60 mm s−1. The multifunctionality of the proposed mechatronic platform is demonstrated on the examples of selective solid–liquid separation and droplet merging.

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Footstep-Identification System Based on Walking Interval

Cited by 29 publications

References 15 publications

Robust footstep identification system based on acoustic local features

Robust footstep identification system based on acoustic local features

Footstep Recognition Using Mel Frequency Cepstral Coefficients and Artificial Neural Network

Magnetically Actuated Surface Microstructures for Efficient Transport and Tunable Separation of Droplets and Solids

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