scholarly journals Social Touch Gesture Recognition Using Convolutional Neural Network

2018 ◽  
Vol 2018 ◽  
pp. 1-10 ◽  
Author(s):  
Saad Albawi ◽  
Oguz Bayat ◽  
Saad Al-Azawi ◽  
Osman N. Ucan
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Gesture Recognition ◽  
Classification Accuracy ◽  
Recognition System ◽  
Human Robot Interaction ◽  
Sensor Data ◽  
Social Touch ◽  
Minimum Number ◽  
Original Frame

Recently, social touch gesture recognition has been considered an important topic for touch modality, which can lead to highly efficient and realistic human-robot interaction. In this paper, a deep convolutional neural network is selected to implement a social touch recognition system for raw input samples (sensor data) only. The touch gesture recognition is performed using a dataset previously measured with numerous subjects that perform varying social gestures. This dataset is dubbed as the corpus of social touch, where touch was performed on a mannequin arm. A leave-one-subject-out cross-validation method is used to evaluate system performance. The proposed method can recognize gestures in nearly real time after acquiring a minimum number of frames (the average range of frame length was from 0.2% to 4.19% from the original frame lengths) with a classification accuracy of 63.7%. The achieved classification accuracy is competitive in terms of the performance of existing algorithms. Furthermore, the proposed system outperforms other classification algorithms in terms of classification ratio and touch recognition time without data preprocessing for the same dataset.

scholarly journals Surface EMG-Based Instantaneous Hand Gesture Recognition Using Convolutional Neural Network with the Transfer Learning Method

Sensors ◽  
2021 ◽  
Vol 21 (7) ◽  
pp. 2540
Author(s):  
Zhipeng Yu ◽  
Jianghai Zhao ◽  
Yucheng Wang ◽  
Linglong He ◽  
Shaonan Wang
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Transfer Learning ◽  
Gesture Recognition ◽  
Recognition System ◽  
Surface Emg ◽  
Hand Gesture Recognition ◽  
Hand Gesture ◽  
Training Time ◽  
Generalization Ability

In recent years, surface electromyography (sEMG)-based human–computer interaction has been developed to improve the quality of life for people. Gesture recognition based on the instantaneous values of sEMG has the advantages of accurate prediction and low latency. However, the low generalization ability of the hand gesture recognition method limits its application to new subjects and new hand gestures, and brings a heavy training burden. For this reason, based on a convolutional neural network, a transfer learning (TL) strategy for instantaneous gesture recognition is proposed to improve the generalization performance of the target network. CapgMyo and NinaPro DB1 are used to evaluate the validity of our proposed strategy. Compared with the non-transfer learning (non-TL) strategy, our proposed strategy improves the average accuracy of new subject and new gesture recognition by 18.7% and 8.74%, respectively, when up to three repeated gestures are employed. The TL strategy reduces the training time by a factor of three. Experiments verify the transferability of spatial features and the validity of the proposed strategy in improving the recognition accuracy of new subjects and new gestures, and reducing the training burden. The proposed TL strategy provides an effective way of improving the generalization ability of the gesture recognition system.

scholarly journals Hand gesture recognition using discrete wavelet transform and convolutional neural network

2020 ◽  
Vol 9 (3) ◽  
pp. 996-1004 ◽  
Author(s):  
Muhammad Biyan Priatama ◽  
Ledya Novamizanti ◽  
Suci Aulia ◽  
Erizka Banuwati Candrasari
Keyword(s):  
Neural Network ◽  
Wavelet Transform ◽  
Convolutional Neural Network ◽  
Discrete Wavelet Transform ◽  
Gesture Recognition ◽  
Public Services ◽  
Recognition System ◽  
Hand Gesture Recognition ◽  
Hand Gesture ◽  
Discrete Wavelet

Public services are available to all communities including people with disabilities. One obstacle that impedes persons with disabilities from participating in various community activities and enjoying the various public services available to the community is information and communication barriers. One way to communicate with people with disabilities is with hand gestures. Therefore, the hand gesture technology is needed, in order to facilitate the public to interact with the disability. This study proposes a reliable hand gesture recognition system using the convolutional neural network method. The first step, carried out pre-processing, to separate the foreground and background. Then the foreground is transformed using the discrete wavelet transform (DWT) to take the most significant subband. The last step is image classification with convolutional neural network. The amount of training and test data used are 400 and 100 images repectively, containing five classes namely class A, B, C, # 5, and pointing. This study engendered a hand gesture recognition system that had an accuracy of 100% for dataset A and 90% for dataset B.

Sign to Speech Convolutional Neural Network-Based Filipino Sign Language Hand Gesture Recognition System

2021 ◽  
Author(s):  
Mark Benedict D. Jarabese ◽  
Charlie S. Marzan ◽  
Jenelyn Q. Boado ◽  
Rushaine Rica Mae F. Lopez ◽  
Lady Grace B. Ofiana ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Sign Language ◽  
Gesture Recognition ◽  
Recognition System ◽  
Hand Gesture Recognition ◽  
Hand Gesture

scholarly journals Data-driven Radar Processing Using a Parametric Convolutional Neural Network for Human Activity Classification

2021 ◽  
Author(s):  
Thomas Stadelmayer ◽  
Avik Santra
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Human Activity ◽  
Recognition System ◽  
Radar Data ◽  
Sensor Data ◽  
Fast Time ◽  
Activity Classification ◽  
Deep Convolutional Neural Networks ◽  
Radar Sensors

Radar sensors offer a promising and effective sensing modality for human activity classification. Human activity classification enables several smart homes applications for energy saving, human-machine interface for gesture controlled appliances and elderly fall-motion recognition. Present radar-based activity recognition system exploit micro-Doppler signature by generating Doppler spectrograms or video of range-Doppler images (RDIs), followed by deep neural network or machine learning for classification. Although, deep convolutional neural networks (DCNN) have been shown to implicitly learn features from raw sensor data in other fields, such as camera and speech, yet for the case of radar DCNN preprocessing followed by feature image generation, such as video of RDI or Doppler spectrogram, is required to develop a scalable and robust classification or regression application. In this paper, we propose a parametric convolutional neural network that mimics the radar preprocessing across fast-time and slow-time radar data through 2D sinc filter or 2D wavelet filter kernels to extract features for classification of various human activities. It is demonstrated that our proposed solution shows improved results compared to equivalent state-of-art DCNN solutions that rely on Doppler spectrogram or video of RDIs as feature images.

A visual terrain classification method for mobile robots’ navigation based on convolutional neural network and support vector machine

2021 ◽  
pp. 014233122098791
Author(s):  
Wanli Wang ◽  
Botao Zhang ◽  
Kaiqi Wu ◽  
Sergey A Chepinskiy ◽  
Anton A Zhilenkov ◽  
...  
Keyword(s):  
Neural Network ◽  
Support Vector Machine ◽  
Mobile Robots ◽  
Convolutional Neural Network ◽  
Hybrid Method ◽  
Classification Accuracy ◽  
Support Vector ◽  
High Classification Accuracy ◽  
Enhance Efficiency ◽  
Multi Class Classification

In this paper, a hybrid method based on deep learning is proposed to visually classify terrains encountered by mobile robots. Considering the limited computing resource on mobile robots and the requirement for high classification accuracy, the proposed hybrid method combines a convolutional neural network with a support vector machine to keep a high classification accuracy while improve work efficiency. The key idea is that the convolutional neural network is used to finish a multi-class classification and simultaneously the support vector machine is used to make a two-class classification. The two-class classification performed by the support vector machine is aimed at one kind of terrain that users are mostly concerned with. Results of the two classifications will be consolidated to get the final classification result. The convolutional neural network used in this method is modified for the on-board usage of mobile robots. In order to enhance efficiency, the convolutional neural network has a simple architecture. The convolutional neural network and the support vector machine are trained and tested by using RGB images of six kinds of common terrains. Experimental results demonstrate that this method can help robots classify terrains accurately and efficiently. Therefore, the proposed method has a significant potential for being applied to the on-board usage of mobile robots.

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