Voice Control Intelligent Wheelchair Movement Using CNNs

Sharifuddin, Mohammad Shahrul Izham; Nordin, Sharifalillah; Ali, Azliza Mohd

doi:10.1109/aidas47888.2019.8970865

Cited by 11 publications

(9 citation statements)

References 16 publications

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“…Figure 1 presents the flow of the proposed system [5]. We described the flow based on three modules: the input (the user i.e.…”

Section: Classification Layermentioning

confidence: 99%

“…There are also urban noise and white noise data collected for the experiment. 2,373 urban noise data are collected from Google while 2,300 white noise data are self-recorded [5] because the data is not available online. Each voice data is prepared in one-second length and is saved in WAV format.…”

Section: Data Samplementioning

confidence: 99%

“…The improvement of voice and speech identification technology has been started by Texas Instruments in 1960 [5]. Recently, voice recognition or speech recognition has been applied in assisting people doing work through digital devices such as mobile phone, tablets, and personal computer.…”

Section: Introductionmentioning

confidence: 99%

“…Guan [21] optimise performance in speech recognition using CNNs and the recognition performance has increased with an error rate of 13.88%. Sharifuddin et.al compared CNNs and BPNNs on voice control wheelchair applications and produces high accuracy on CNNs technique [5].…”

Section: Introductionmentioning

confidence: 99%

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Comparison of CNNs and SVM for voice control wheelchair

Sharifuddin

Nordin

Ali

2020

IJ-AI

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In this paper, we develop an intelligent wheelchair using CNNs and SVM voice recognition methods. The data is collected from Google and some of them are self-recorded. There are four types of data to be recognized which are go, left, right, and stop. Voice data are extracted using MFCC feature extraction technique. CNNs and SVM are then used to classify and recognize the voice data. The motor driver is embedded in Raspberry PI 3B+ to control the movement of the wheelchair prototype. CNNs produced higher accuracy i.e. 95.30% compared to SVM which is only 72.39%. On the other hand, SVM only took 8.21 seconds while CNNs took 250.03 seconds to execute. Therefore, CNNs produce better result because noise are filtered in the feature extraction layer before classified in the classification layer. However, CNNs took longer time due to the complexity of the networks and the less complexity implementation in SVM give shorter processing time.

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“…Figure 1 presents the flow of the proposed system [5]. We described the flow based on three modules: the input (the user i.e.…”

Section: Classification Layermentioning

confidence: 99%

Section: Data Samplementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Comparison of CNNs and SVM for voice control wheelchair

Sharifuddin

Nordin

Ali

2020

IJ-AI

Self Cite

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“…Machine learning techniques, particularly deep learning (Saufi et al, 2018) (Yahya et al, 2020) (Sharifuddin et al, 2019) has been proven to solve real-world applications related to computer vision. Despite achieving high accuracy in classification, detection and recognition applications, the deep learning approach is still slow in execution time even with high-end hardware.…”

Section: Introductionmentioning

confidence: 99%

Comparing MobileNet-SSD and YOLO v3 Learning Architecture for Real-time Driver’s Fatigue Detection

Jamil¹,

Fadhil²,

Hamzah³

et al. 2021

IJARBSS

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Convolutional Neural Network is known to achieve high accuracy in solving classification, recognition, and detection problems. In a real-time environment, time is an important factor of consideration. Even though most CNN-based architectures achieved considerably high accuracy, they are still slow even with high-end hardware. Therefore, this paper compares the time-accuracy tradeoff between two recent CNN-based learning architectures in detecting a driver's fatigue status. In our work, we define fatigue based on the rate of eye blinking. We developed a proof of concept systems, and evaluate the systems based on accuracy and detection speed. The accuracy and speed of both learning architectures were trained and tested using the Closed Eyes in the Wild (CEW) containing 1,193 closed eyes images and 1,232 opened eyes images. As MobileNet-SSD and YOLO v3 were pre-trained using a general COCO dataset, they were further configured and fine-tuned to optimize the results based on the CEW datasets, The results showed that YOLO v3 has slightly higher meanAveragePrecision(mAP) than MobileNet-SSD but slower detection speed(ms), while MobileNet-SSD proved that it has much faster speed but still maintaining high accuracy. The results of the research also showed that there is a trade-off between speed and accuracy which there was a loss of accuracy to obtain faster speed. This research also proved that lightweight MobileNet-SSD can minimize the accuracy loss to gain speed. The accuracy of the MobileNet-SSD learning model was still considered high and the detection speed was far higher than YOLO v3 learning model. Therefore, MobileNetSSD learning model was selected to have the best speed and accuracy trade-off in this research.

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