Grasp Classification With Weft Knit Data Glove Using a Convolutional Neural Network

Ayodele, Emmanuel; Bao, Tianzhe; Zaidi, Syed Ali Raza; Hayajneh, Ali; Scott, Jane; Zhang, Zhiqiang; McLernon, Des

doi:10.1109/jsen.2021.3059028

Cited by 26 publications

(12 citation statements)

References 32 publications

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“…The recursive neural network is prone to the phenomenon of gradient explosion or disappearance. In the training process, this will lead to the inability to continuously send sequences with very long gradients in the training process and eventually make it difficult for the model to be captured for a long time [11]. As for the phenomenon of gradient explosion, the gradient threshold can be set scientifically and reasonably based on model parameter training.…”

Section: Methodsmentioning

confidence: 99%

Multilingual Machine Translation System Based on Decoder Recurrent Neural Network

Deng

2022

Wireless Communications and Mobile Computing

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In order to solve the problem that traditional machine translation cannot meet users’ needs due to its slow translation speed, an in-depth study on English translation model based on neural network was proposed. Firstly, three methods of model frame design, translation system design, and training frame design are studied. In order to improve the effectiveness and stability of the English translation model of recursive neural network, a model of English-Chinese machine translation system is designed. The system uses a knowledge-based context vector to map English and Chinese words, and uses a codec recursive neural network to achieve the results. After experiments and researches, the neural network can also efficiently deal with the long-distance reordering problem of multilanguage machine translation, which is difficult for statistical machine translation to deal with effectively. The neural network has opened a broad field of vision for machine translation research.

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Section: Methodsmentioning

confidence: 99%

Multilingual Machine Translation System Based on Decoder Recurrent Neural Network

Deng

2022

Wireless Communications and Mobile Computing

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“…With regard to the number and size of convolution filters, the number of convolution blocks, and the dropout layer's probability, these four parameters will significantly affect the performance of CNN [31]. Through the ablation study of 12 CNNs with different configurations and parameters (Table 2), the optimal network structure can be determined.…”

Section: The Selection Of Cnn Architecturementioning

confidence: 99%

A Wearable Real-Time Character Recognition System Based on Edge Computing-Enabled Deep Learning for Air-Writing

et al. 2022

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Nowadays, wearable sensors play a vital role in the detection of human motions, innovating an alternate and intuitive form in human–computer interaction (HCI). In this study, we present a novel real-time wearable system for finger air-writing recognition in three-dimensional (3D) space based on the Arduino Nano 33 BLE Sense as an edge device, which can run TensorFlow Lite to realize recognition and classification on the device. This system enables users to have the freedom and flexibility to write characters (10 digits and 26 English lower-case letters) in free space by moving fingers and uses a deep learning algorithm to recognize 36 characters from the motion data captured by inertial measurement units (IMUs) and processed by a microcontroller, which are both embedded in an Arduino Nano 33 BLE Sense. We prepared 63000 air-writing stroke data samples of 35 subjects containing 18 males and 17 females for convolutional neural network (CNN) training and achieved a high recognition accuracy at 97.95%.

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“…KNN is a probabilistic pattern recognition technique that classifies a signal output based on the most common class of its k nearest neighbors in the training data. The most common class (also referred to as the similarity function) can be computed as a distance or correlation metric [46]. In this study, we select the Euclidean distance as the similarity function as it is the most commonly used metric in KNN.…”

Section: K-nearest Neighbor (Knn)mentioning

confidence: 99%

Detection and Classification of Human Activity for Emergency Response in Smart Factory Shop Floor

et al. 2021

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Factory shop floor workers are exposed to threats and accidents due to their encounters with tools, equipment, and toxic materials. There are cases of occupational accidents resulting in injuries to workers and precipitating lawsuits, which on the other hand affect company’s operational cost. To ensure the safety of workers within the shop floor, there is a need for proactive activity monitoring. Such activities include detection of falling objects, abnormal vibration, and movement of humans within an acceptable area of the factory floor. Breathing sensor-based monitoring of workers in the smart factory shop floor can also be implemented. This is for the detection of human activity, especially in cases where workers are in isolation with no available emergency assistance. Internet of Things (IoT), Industrial Internet of Things (IIoT), and machine learning (ML) have enabled so many possibilities in this area. In this study, we present a simple test-bed, which is made up of a vibration sensor, a breathing and movement sensor, and a Light Detection and Ranging (LIDAR) sensor. These sensors were used to gather normal and abnormal data of human activities at the factory. We developed a dataset based on possible real-life situations and it is made up of about 10,000 data points. The data was split with a ratio of 75:25 for training and testing the model. We investigated the performance of different ML algorithms, including support vector machine (SVM), linear regression, naive Bayes (NB), K-nearest neighbor (KNN), and convolutional neural network (CNN). From our experiments, the CNN model outperformed other algorithms with an accuracy of 99.45%, 99.78%,100%, and 100%, respectively, for vibration, movement, breathing, and distance. We have also successfully developed a dataset to assist the research community in this field.

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Grasp Classification With Weft Knit Data Glove Using a Convolutional Neural Network

Cited by 26 publications

References 32 publications

Multilingual Machine Translation System Based on Decoder Recurrent Neural Network

Multilingual Machine Translation System Based on Decoder Recurrent Neural Network

A Wearable Real-Time Character Recognition System Based on Edge Computing-Enabled Deep Learning for Air-Writing

Detection and Classification of Human Activity for Emergency Response in Smart Factory Shop Floor

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