Gesture Recognition Benchmark Based on Mobile Phone

Xie, Chunyu; Luan, Shangzhen; Wang, Hainan; Zhang, Baochang

doi:10.1007/978-3-319-46654-5_48

Cited by 11 publications

(4 citation statements)

References 20 publications

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“…After preprocessing, the length of each data sequence is set to 1000, thus each input sample (3-axis accelerometer and gyrometer signals) is a matrix of 1000 × 6. Experiment on BUAA Mobile Gesture Database [47]. This database has 1120 samples for gestures A, B, C, D, 1, 2, 3, 4.…”

Section: Comparison With State-of-the-artsmentioning

confidence: 99%

Deep Fisher discriminant learning for mobile hand gesture recognition

Xie

Zhang

et al. 2018

Pattern Recognition

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Section: Comparison With State-of-the-artsmentioning

confidence: 99%

Deep Fisher discriminant learning for mobile hand gesture recognition

Xie

Zhang

et al. 2018

Pattern Recognition

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“…We use Tensorflow toolbox as the deep learning platform and an NVIDIA GTX 1070 GPU to run the experiments. In order to validate the effectiveness of our proposed Fisher criterion in LSTM for modeling temporal sequences, we compare our methods, F-BLSTM and F-BGRU, with the state-of-the-art baselines (BLSTM and BGRU [46]) on three benchmarks including our proposed database (MGD), and two previous databases: the BUAA Mobile Gesture database [47] and the SmartWatch Gestures database [46]. We comprehensively evaluate the performance of the proposed model under different parameter settings of δ, α and θ in Sec.…”

Section: Implementation Detailsmentioning

confidence: 99%

“…By the study in last section, we set θ to 0.1, δ to 0.01 and α to 0.5 in the F-BLSTM model, and set the parameters to 0.3, 0.01, 0.5 in the F-BGRU model, respectively. Experiment on BUAA Mobile Gesture Database [47]. This database has 1120 samples for gestures A, B, C, D, 1, 2, 3, 4.…”

Section: Analysis Of Model Effectmentioning

confidence: 99%

Deep Fisher Discriminant Learning for Mobile Hand Gesture Recognition

Xie¹,

Li²,

Zhang³

et al. 2017

Preprint

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Gesture recognition is a challenging problem in the field of biometrics. In this paper, we integrate Fisher criterion into Bidirectional Long-Short Term Memory (BLSTM) network and Bidirectional Gated Recurrent Unit (BGRU), thus leading to two new deep models termed as F-BLSTM and F-BGRU. Both Fisher discriminative deep models can effectively classify the gesture based on analyzing the acceleration and angular velocity data of the human gestures. Moreover, we collect a large Mobile Gesture Database (MGD) based on the accelerations and angular velocities containing 5547 sequences of 12 gestures. Extensive experiments are conducted to validate the superior performance of the proposed networks as compared to the state-of-the-art BLSTM and BGRU on MGD database and two benchmark databases (i.e. BUAA mobile gesture and SmartWatch gesture).

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“…Pedro Lopez-Rodriguez 등은 합성곱 신경망과 장단기 메모리(Long Short-Term Memory)를 활용하여 터치스크린에 26개의 소문자 알파벳과 0~9의 숫자를 썼을 때 이것을 분별하는 방법을 제안하였다 [17]. Amitangshu Pal은 입력 신호의 패턴을 분석하고 획 구분 방법을 통해 입력된 문자를 구분하는 방법을 제안하였다 [12]. Sandip Agrawal 등은…”

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Visualization of Signals with Variable Length for Character Recognition

Lee¹,

Hwang²,

Choi³

2023

APJCRI

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Research on human motion recognition using various sensors has been steadily conducted. However, despite the increasing spread of smartphones and the activation of research using smartphone sensors, research on character recognition methods using signals with variable length is still at a standstill. The reason is that even with the same motion, the length of the signal is always different, making it difficult to process. To solve the problem, the study proposes a method of recognizing characters using an object detection neural network as a classifier. In this study, five different subjects collect data by drawing three alphabets, I, S, and Z letters 100 times each in the air and the collected data is used to train an object detection neural network and to evaluate the performance of the network. The data changes into images through denoising and normalization. Third-order spline interpolation and Fourier transform are used to remove noise from the raw signal. The values of the acceleration sensors x, y, and z axes correspond to the values of the R, G, and B channels of an image, respectively. As a result of evaluating character recognition performance by applying image data to YOLOv5, it showed an average accuracy of 99% for three characters.

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Gesture Recognition Benchmark Based on Mobile Phone

Cited by 11 publications

References 20 publications

Deep Fisher discriminant learning for mobile hand gesture recognition

Deep Fisher discriminant learning for mobile hand gesture recognition

Deep Fisher Discriminant Learning for Mobile Hand Gesture Recognition

Visualization of Signals with Variable Length for Character Recognition

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