Analysis of Edge-Optimized Deep Learning Classifiers for Radar-based Gesture Recognition

Recently, Doppler radar-based foot gesture recognition has attracted attention as a hands-free tool. Doppler radar-based recognition for various foot gestures is still very challenging. So far, no studies have yet dealt deeply with recognition of various foot gestures based on Doppler radar and a deep learning model. In this paper, we propose a method of foot gesture recognition using a new high-compression radar signature image and deep learning. By means of a deep learning AlexNet model, a new high-compression radar signature is created by extracting dominant features via Singular Value Decomposition (SVD) processing; four different foot gestures including kicking, swinging, sliding, and tapping are recognized. Instead of using an original radar signature, the proposed method improves the memory efficiency required for deep learning training by using a high-compression radar signature. Original and reconstructed radar images with high compression values of 90%, 95%, and 99% were applied for the deep learning AlexNet model. As experimental results, movements of all four different foot gestures and of a rolling baseball were recognized with an accuracy of approximately 98.64%. In the future, due to the radar’s inherent robustness to the surrounding environment, this foot gesture recognition sensor using Doppler radar and deep learning will be widely useful in future automotive and smart home industry fields.

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Hand Gesture Recognition Using Instant High-density EMG Graph via Deep Learning Method

2020 Chinese Automation Congress (CAC) ◽

10.1109/cac51589.2020.9326536 ◽

2020 ◽

Author(s):

Dezhen Xiong ◽

Daohui Zhang ◽

Xingang Zhao ◽

Yiwen Zhao

Keyword(s):

Deep Learning ◽

Gesture Recognition ◽

Hand Gesture Recognition ◽

High Density ◽

Hand Gesture ◽

Learning Method

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Real-time gesture recognition in complex background based on deep learning

2020 IEEE 9th Joint International Information Technology and Artificial Intelligence Conference (ITAIC) ◽

10.1109/itaic49862.2020.9339060 ◽

2020 ◽

Author(s):

Helin Long ◽

Muyang Liu ◽

Muzhi Li

Keyword(s):

Deep Learning ◽

Real Time ◽

Gesture Recognition ◽

Complex Background

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Small Deep Learning Models for Hand Gesture Recognition

2019 IEEE Intl Conf on Parallel & Distributed Processing with Applications, Big Data & Cloud Computing, Sustainable Computing & Communications, Social Computing & Networking (ISPA/BDCloud/SocialCom/SustainCom) ◽

10.1109/ispa-bdcloud-sustaincom-socialcom48970.2019.00205 ◽

2019 ◽

Author(s):

Adam Ahmed Qaid Mohammed ◽

Jiancheng Lv ◽

Md Sajjatul Islam

Keyword(s):

Deep Learning ◽

Gesture Recognition ◽

Hand Gesture Recognition ◽

Hand Gesture ◽

Learning Models

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A semiautomatic annotation approach for sentiment analysis

Journal of Information Science ◽

10.1177/01655515211006594 ◽

2021 ◽

pp. 016555152110065

Author(s):

Rahma Alahmary ◽

Hmood Al-Dossari

Keyword(s):

Deep Learning ◽

Sentiment Analysis ◽

Short Term Memory ◽

Machine Learning Algorithms ◽

Support Vector ◽

Short Term ◽

Term Memory ◽

Annotation Process ◽

Learning Classifiers ◽

Long Short Term Memory

Sentiment analysis (SA) aims to extract users’ opinions automatically from their posts and comments. Almost all prior works have used machine learning algorithms. Recently, SA research has shown promising performance in using the deep learning approach. However, deep learning is greedy and requires large datasets to learn, so it takes more time for data annotation. In this research, we proposed a semiautomatic approach using Naïve Bayes (NB) to annotate a new dataset in order to reduce the human effort and time spent on the annotation process. We created a dataset for the purpose of training and testing the classifier by collecting Saudi dialect tweets. The dataset produced from the semiautomatic model was then used to train and test deep learning classifiers to perform Saudi dialect SA. The accuracy achieved by the NB classifier was 83%. The trained semiautomatic model was used to annotate the new dataset before it was fed into the deep learning classifiers. The three deep learning classifiers tested in this research were convolutional neural network (CNN), long short-term memory (LSTM) and bidirectional long short-term memory (Bi-LSTM). Support vector machine (SVM) was used as the baseline for comparison. Overall, the performance of the deep learning classifiers exceeded that of SVM. The results showed that CNN reported the highest performance. On one hand, the performance of Bi-LSTM was higher than that of LSTM and SVM, and, on the other hand, the performance of LSTM was higher than that of SVM. The proposed semiautomatic annotation approach is usable and promising to increase speed and save time and effort in the annotation process.

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