Motion-Pattern Recognition System Using a Wavelet-Neural Network

Yang, Wen-Ren; Wang, Chau‐Shing; Chen, Chien-Pu

doi:10.1109/tce.2019.2895050

Cited by 18 publications

(3 citation statements)

References 25 publications

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“…In Ref. [8], a one-level discrete wavelet transforms DWT radial basis neural network (DWT-RBNN) recognition system for elderly daily activity monitoring applications was used to distinguish between walking, jogging, and running activities using Daubechies 10 wavelet as MW for the quadrature mirror filter used in the DWT decomposition process.…”

Section: Literature Reviewmentioning

confidence: 99%

Quantitative Analysis of Mother Wavelet Function Selection for Wearable Sensors-Based Human Activity Recognition

Nematallah,

Rajan

2024

Sensors

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Recent advancements in the Internet of Things (IoT) wearable devices such as wearable inertial sensors have increased the demand for precise human activity recognition (HAR) with minimal computational resources. The wavelet transform, which offers excellent time-frequency localization characteristics, is well suited for HAR recognition systems. Selecting a mother wavelet function in wavelet analysis is critical, as optimal selection improves the recognition performance. The activity time signals data have different periodic patterns that can discriminate activities from each other. Therefore, selecting a mother wavelet function that closely resembles the shape of the recognized activity’s sensor (inertial) signals significantly impacts recognition performance. This study uses an optimal mother wavelet selection method that combines wavelet packet transform with the energy-to-Shannon-entropy ratio and two classification algorithms: decision tree (DT) and support vector machines (SVM). We examined six different mother wavelet families with different numbers of vanishing points. Our experiments were performed on eight publicly available ADL datasets: MHEALTH, WISDM Activity Prediction, HARTH, HARsense, DaLiAc, PAMAP2, REALDISP, and HAR70+. The analysis demonstrated in this paper can be used as a guideline for optimal mother wavelet selection for human activity recognition.

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Section: Literature Reviewmentioning

confidence: 99%

Quantitative Analysis of Mother Wavelet Function Selection for Wearable Sensors-Based Human Activity Recognition

Nematallah,

Rajan

2024

Sensors

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“…More importantly, facial expressions cannot be recorded using this approach. In addition to data gloves, leap motion controller [10,22,23] and accelerometer [21] are also used to capture human motions. Each sensing technology varies with several parameters like accuracy, user comfort, range of motion, and cost [8].…”

Section: Sensor Based Approachmentioning

confidence: 99%

Indian Sign Language Recognition through Hybrid ConvNet-LSTM Networks

H¹,

Gomathi²

2021

emitter

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Dynamic hand gesture recognition is a challenging task of Human-Computer Interaction (HCI) and Computer Vision. The potential application areas of gesture recognition include sign language translation, video gaming, video surveillance, robotics, and gesture-controlled home appliances. In the proposed research, gesture recognition is applied to recognize sign language words from real-time videos. Classifying the actions from video sequences requires both spatial and temporal features. The proposed system handles the former by the Convolutional Neural Network (CNN), which is the core of several computer vision solutions and the latter by the Recurrent Neural Network (RNN), which is more efficient in handling the sequences of movements. Thus, the real-time Indian sign language (ISL) recognition system is developed using the hybrid CNN-RNN architecture. The system is trained with the proposed CasTalk-ISL dataset. The ultimate purpose of the presented research is to deploy a real-time sign language translator to break the hurdles present in the communication between hearing-impaired people and normal people. The developed system achieves 95.99% top-1 accuracy and 99.46% top-3 accuracy on the test dataset. The obtained results outperform the existing approaches using various deep models on different datasets.

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“…Neural networks (NNs) have received extensive attention due to their successful applications in vision system [1], associative memory [2], pattern recognition [3], and image compression [4]. NNs always require stability, which is a prerequisite for many applications.…”

Section: Introductionmentioning

confidence: 99%

Passivity and Synchronization of Multiple Multi-Delayed Neural Networks via Impulsive Control

Yong

Yang

Liu

et al. 2020

Discrete Dynamics in Nature and Society

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This paper is concerned with the passivity and synchronization for multiple multi-delayed neural networks (MMDNNs) under impulsive control. To ensure the passivity, input strict passivity, and output strict passivity in MMDNNs, a suitable impulsive controller is designed. Moreover, an impulsive time-dependent Lyapunov functional is exploited to obtain the synchronization criterion of MMDNNs, where the criterion is formulated by linear matrix inequalities. Numerical examples are given to verify the validity of the theoretical results.

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Motion-Pattern Recognition System Using a Wavelet-Neural Network

Cited by 18 publications

References 25 publications

Quantitative Analysis of Mother Wavelet Function Selection for Wearable Sensors-Based Human Activity Recognition

Quantitative Analysis of Mother Wavelet Function Selection for Wearable Sensors-Based Human Activity Recognition

Indian Sign Language Recognition through Hybrid ConvNet-LSTM Networks

Passivity and Synchronization of Multiple Multi-Delayed Neural Networks via Impulsive Control

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