A 3DCNN-LSTM Hybrid Framework for sEMG-Based Noises Recognition in Exercise

Lin, Min-Wen; Ruan, Shanq-Jang; Tu, Ya-Wen

doi:10.1109/access.2020.3021344

Cited by 13 publications

(6 citation statements)

References 18 publications

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“…Towards this, we investigated the recent trend in deep learning models for spatiotemporal features analysis. As a result, we discovered that learning in-depth spatiotemporal features employing 3D-CNN to analyze local spatial information and short-term temporal features is more powerful and reasonable than using 2D-CNN for action recognition, but 3D-CNN is not suitable to capture long-term dependencies [28][29][30]. On the other hand, researchers used RNN/LSTM networks to capture long-term temporal information in sequential data of varying lengths [30].…”

Section: Without Any Sequence Learning For the Drowsiness Actionsmentioning

confidence: 99%

“…These shortcomings inspire researchers to combine the benefits of 3D-CNN with LSTM to understand deep, long-term spatiotemporal correlation. Such as for gesture recognition [30,31], analysis of muscular activity [29], radar echo nowcasting task [32], lip reading [33], action detection [34]. However, 3DCNN has more parameters than 2D-CNN, thereby greatly increasing the running time of the system.…”

Section: Without Any Sequence Learning For the Drowsiness Actionsmentioning

confidence: 99%

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A Lightweight Driver Drowsiness Detection System Using 3DCNN With LSTM

Alameen¹,

Alhothali²

2023

Computer Systems Science and Engineering

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Today, fatalities, physical injuries, and significant economic losses occur due to car accidents. Among the leading causes of car accidents is drowsiness behind the wheel, which can affect any driver. Drowsiness and sleepiness often have associated indicators that researchers can use to identify and promptly warn drowsy drivers to avoid potential accidents. This paper proposes a spatiotemporal model for monitoring drowsiness visual indicators from videos. This model depends on integrating a 3D convolutional neural network (3D-CNN) and long short-term memory (LSTM). The 3DCNN-LSTM can analyze long sequences by applying the 3D-CNN to extract spatiotemporal features within adjacent frames. The learned features are then used as the input of the LSTM component for modeling high-level temporal features. In addition, we investigate how the training of the proposed model can be affected by changing the position of the batch normalization (BN) layers in the 3D-CNN units. The BN layer is examined in two different placement settings: before the non-linear activation function and after the non-linear activation function. The study was conducted on two publicly available drowsy drivers datasets named 3MDAD and YawDD. 3MDAD is mainly composed of two synchronized datasets recorded from the frontal and side views of the drivers. We show that the position of the BN layers increases the convergence speed and reduces overfitting on one dataset but not the other. As a result, the model achieves a test detection accuracy of 96%, 93%, and 90% on YawDD, Side-3MDAD, and Front-3MDAD, respectively.

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Section: Without Any Sequence Learning For the Drowsiness Actionsmentioning

confidence: 99%

Section: Without Any Sequence Learning For the Drowsiness Actionsmentioning

confidence: 99%

A Lightweight Driver Drowsiness Detection System Using 3DCNN With LSTM

Alameen¹,

Alhothali²

2023

Computer Systems Science and Engineering

View full text Add to dashboard Cite

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“…To verify the performance of the proposed model, this article compares it with six highly competitive models. These models are EEGConv (Zeng et al, 2018), EEGConvR (Zeng et al, 2018), ESTCNN (Gao et al, 2019), R3DCNNs (Zhang et al, 2019), 3D-CNN (Lin et al, 2020), and 3D-LSTM (Lin et al, 2020). This article reproduces these models by introducing models, parameters, and some details in the original literature.…”

Section: Comparative Experimentsmentioning

confidence: 99%

3D-STCNN: Spatiotemporal Convolutional Neural Network Based on EEG 3D Features for Detecting Driving Fatigue

Peng

Gao

Wang

et al. 2023

JDSIS

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Fatigue driving has become one of the main causes of traffic accidents, and driving fatigue detection based on electroencephalogram (EEG) can effectively evaluate the driver’s mental state and avoid the occurrence of traffic accidents. This article evaluates a feature extraction method for extracting multiple features of EEG signals and establishes a Spatiotemporal Convolutional Neural Network (STCNN) to detect driver fatigue. Firstly, we constructed a three-dimensional feature of the EEG signal, which includes the frequency domain, time domain, and spatial features of the EEG signal. Then, we use STCNN for fatigue state classification. STCNN is composed of an Attention Time Network (ATNet) based on attention mechanism and an Attention Convolutional Neural Network (ACNN) based on attention mechanism. In addition, we conducted fatigue driving experiments and collected EEG signals from 14 subjects in both awake and fatigued states, ultimately collecting EEG data under three different driving task loads. We conducted extensive experiments on this basis and compared the effectiveness of STCNN and six competitive methods. The results show that the classification accuracy of STCNN is 87.55%, which can effectively detect the fatigue status of drivers.

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“…An efficient method of gesture identification based on the combination of two signals using supervised learning and univariate feature selection was implemented. Lin et al [21] proposed two dataaugmentation approaches to expend their sEMG data set, which were the simulation of the surface electrodes displacement on the skin and the muscle fatigue.…”

Section: Related Workmentioning

confidence: 99%

Musculoskeletal Rehabilitation Status Monitoring Based on sEMG

Han

Zhao²,

Wang³

et al. 2021

Mobile Information Systems

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The reduction and improper movements in people’s modern life will lead to physical discomfort, pain, and inflammation, which have generally affected the quality of people’s daily life and work efficiency. The pain caused by improper movements are called musculoskeletal pain, which can be relieved or eliminated with treatment. Musculoskeletal disorders are actually one of the most common medical conditions, which affects approximately one quarter of all adults in the world. Although surface electromyography (sEMG) is an acknowledged technology in musculoskeletal rehabilitation study, it is considerably significant to monitor the musculoskeletal rehabilitation status based on sEMG. In order to monitor the musculoskeletal rehabilitation status, we combine fuzzy theory with neural network. This article proposes variable size, sliding window-based, generalized, dynamic, fuzzy neural network (GD-FNN), musculoskeletal rehabilitation status monitoring, that is, the window length of sliding window of sample data changes with the size of sample period. Finally, this study made a simulation on subjects, and the experimental results show that the proposed variable size, sliding window-based GD-FNN, musculoskeletal rehabilitation status monitoring method not only has good monitoring effect but also put on a good performance in root-mean-squared error (RMSE) and mean absolute percentage error (MAPE).

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A 3DCNN-LSTM Hybrid Framework for sEMG-Based Noises Recognition in Exercise

Cited by 13 publications

References 18 publications

A Lightweight Driver Drowsiness Detection System Using 3DCNN With LSTM

A Lightweight Driver Drowsiness Detection System Using 3DCNN With LSTM

3D-STCNN: Spatiotemporal Convolutional Neural Network Based on EEG 3D Features for Detecting Driving Fatigue

Musculoskeletal Rehabilitation Status Monitoring Based on sEMG

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