Gait Recognition with Multi-region Size Convolutional Neural Network for Authentication with Wearable Sensors

Nguyen, Khac-Tuan; Vo-Tran, Thanh-Luong; Dinh, Dat-Thanh; Tran, Minh-Triet

doi:10.1007/978-3-319-70004-5_14

Cited by 17 publications

(25 citation statements)

References 22 publications

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“…Recently, deep learning has gained an extraordinary development and dramatically improved the state-of-the-art researches in many pattern recognition and machine learning tasks such as speech recognition, visual object recognition/detection [35]. Following that trend, many studies have adopted deep leaning techniques for the task of inertial sensor-based gait recognition and achieved new state-of-theart results [9]- [12], [14]- [16], [36].…”

Section: A Gait Recognitionmentioning

confidence: 99%

“…The IDNet was evaluated on a dataset of 50 subjects and achieved a very promising performance as 0.15% equal error rate (EER). After that, several studies [14], [36] followed this trend by proposing others CNN architectures, and evaluated with different datasets (e.g., [19], [22]). On the other hand, some researches adopted CNN on the fused data of multiple sensors placing in different positions of the human body to improve the accuracy [9], [11].…”

Section: A Gait Recognitionmentioning

confidence: 99%

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Data Augmentation for Inertial Sensor-Based Gait Deep Neural Network

Tran

Choi

2020

IEEE Access

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Inertial sensor-based gait has been discovered as an attractive method for user recognition. Recently, with the approaching of deep learning techniques, new state-of-the-art researches have been established. However, the scarcity of training data still endures as an obstacle that impedes to build a robust deep gait model. In this study, we address that problem by proposing a novel approach for inertial sensor-based gait data augmentation. First, two label-preserving transformation algorithms, namely Arbitrary Time Deformation (ATD) and Stochastic Magnitude Perturbation (SMP), are proposed to generate more training data from the real gait data. The ATD algorithm adjusts the timing information of gait data with random values, on the other hand, SMP alters the magnitude arbitrarily, to create variations on the augmenting data. Then, we design a generic gait recognition model using convolutional neural network, in which, the ATD and SMP algorithms are coordinated appropriately to produce augmenting data varied naturally in both time and magnitude as real data. The proposed approach was evaluated on two public datasets, one was collected in unconstrained conditions, and the other had the largest number of participating users. The experiment showed that, under different amounts of training data, using ATD or SMP alone could increase the recognition performance effectively, and their combination even attained higher accuracy. With ATD and SMP, our model achieved competitive performance on both two datasets comparing to state-ofthe-art researches.

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Section: A Gait Recognitionmentioning

confidence: 99%

Section: A Gait Recognitionmentioning

confidence: 99%

Data Augmentation for Inertial Sensor-Based Gait Deep Neural Network

Tran

Choi

2020

IEEE Access

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“…The recognition of gait activities has been studied from two machine learning approaches: (i) shallow or traditional learning [ 16 , 17 , 18 , 19 , 20 ], and (ii) deep learning [ 21 , 22 , 23 , 24 , 25 ]. As an example of the first category, in [ 16 ], accelerometer and gyroscope data were used to classify six gait actions: sitting, standing, lying, walking, running, and cycling.…”

Section: Related Workmentioning

confidence: 99%

“…In the previously mentioned studies [ 16 , 21 ], the authors collected data from some subjects and used it to evaluate their proposed methods. There are also studies that have collected a large-scale dataset of gait activities from hundreds of people [ 17 , 18 , 19 , 20 , 22 , 23 , 24 , 25 ]. One of these datasets is the OU-ISIR gait dataset, which is described in Section 3.1 .…”

Section: Related Workmentioning

confidence: 99%

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Gait Activity Classification on Unbalanced Data from Inertial Sensors Using Shallow and Deep Learning

López-Nava

Valentín-Coronado

García-Constantino

et al. 2020

Sensors

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Activity recognition is one of the most active areas of research in ubiquitous computing. In particular, gait activity recognition is useful to identify various risk factors in people’s health that are directly related to their physical activity. One of the issues in activity recognition, and gait in particular, is that often datasets are unbalanced (i.e., the distribution of classes is not uniform), and due to this disparity, the models tend to categorize into the class with more instances. In the present study, two methods for classifying gait activities using accelerometer and gyroscope data from a large-scale public dataset were evaluated and compared. The gait activities in this dataset are: (i) going down an incline, (ii) going up an incline, (iii) walking on level ground, (iv) going down stairs, and (v) going up stairs. The proposed methods are based on conventional (shallow) and deep learning techniques. In addition, data were evaluated from three data treatments: original unbalanced data, sampled data, and augmented data. The latter was based on the generation of synthetic data according to segmented gait data. The best results were obtained with classifiers built with augmented data, with F-measure results of 0.812 (σ = 0.078) for the shallow learning approach, and of 0.927 (σ = 0.033) for the deep learning approach. In addition, the data augmentation strategy proposed to deal with the unbalanced problem resulted in increased classification performance using both techniques.

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