Approach for Improving User Interface Based on Gesture Recognition

Gesture recognition technology based on visual detection to acquire gestures information is obtained in a non-contact manner. There are two types of gesture recognition: independent and continuous gesture recognition. The former aims to classify videos or other types of gesture sequences that only contain one isolated gesture instance in each sequence (e.g., RGB-D or skeleton data). In this study, we review existing research methods of visual gesture recognition and will be grouped according to the following family: static, dynamic, based on the supports (Kinect, Leap…etc), works that focus on the application of gesture recognition on robots and works on dealing with gesture recognition at the browser level. Following that, we take a look at the most common JavaScript-based deep learning frameworks. Then we present the idea of defining a process for improving user interface control based on gesture recognition to streamline the implementation of this mechanism.

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Automated Dysarthria Severity Classification Using Deep Learning Frameworks

2020 28th European Signal Processing Conference (EUSIPCO) ◽

10.23919/eusipco47968.2020.9287741 ◽

2021 ◽

Author(s):

Amlu Anna Joshy ◽

Rajeev Rajan

Keyword(s):

Deep Learning ◽

Severity Classification ◽

Learning Frameworks

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Sequence-Based Deep Learning Frameworks on Enhancer-Promoter Interactions Prediction

Current Pharmaceutical Design ◽

10.2174/1381612826666201124112710 ◽

2020 ◽

Vol 26 ◽

Author(s):

Xiaoping Min ◽

Fengqing Lu ◽

Chunyan Li

Keyword(s):

Gene Expression ◽

Deep Learning ◽

Dna Sequences ◽

Experimental Methods ◽

Learning Methods ◽

Comprehensive Review ◽

Genomic Features ◽

Disease Mechanisms ◽

Evaluation Strategies ◽

Learning Frameworks

: Enhancer-promoter interactions (EPIs) in the human genome are of great significance to transcriptional regulation which tightly controls gene expression. Identification of EPIs can help us better deciphering gene regulation and understanding disease mechanisms. However, experimental methods to identify EPIs are constrained by the fund, time and manpower while computational methods using DNA sequences and genomic features are viable alternatives. Deep learning methods have shown promising prospects in classification and efforts that have been utilized to identify EPIs. In this survey, we specifically focus on sequence-based deep learning methods and conduct a comprehensive review of the literatures of them. We first briefly introduce existing sequence-based frameworks on EPIs prediction and their technique details. After that, we elaborate on the dataset, pre-processing means and evaluation strategies. Finally, we discuss the challenges these methods are confronted with and suggest several future opportunities.

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Deep-Learning for Hand-Gesture Recognition with Simultaneous Thermal and Radar Sensors

2020 IEEE Sensors ◽

10.1109/sensors47125.2020.9278683 ◽

2020 ◽

Author(s):

Sruthy Skaria ◽

Da Huang ◽

Akram Al-Hourani ◽

Robin J. Evans ◽

Margaret Lech

Keyword(s):

Deep Learning ◽

Gesture Recognition ◽

Hand Gesture Recognition ◽

Hand Gesture ◽

Radar Sensors

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Intersected EMG Heatmaps and Deep Learning Based Gesture Recognition

Proceedings of the 2020 12th International Conference on Machine Learning and Computing ◽

10.1145/3383972.3383982 ◽

2020 ◽

Cited By ~ 1

Author(s):

Weijie Ke ◽

Yannan Xing ◽

Gaetano Di Caterina ◽

Lykourgos Petropoulakis ◽

John Soraghan

Keyword(s):

Deep Learning ◽

Gesture Recognition

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Foot Gesture Recognition Using High-Compression Radar Signature Image and Deep Learning

Sensors ◽

10.3390/s21113937 ◽

2021 ◽

Vol 21 (11) ◽

pp. 3937

Author(s):

Seungeon Song ◽

Bongseok Kim ◽

Sangdong Kim ◽

Jonghun Lee

Keyword(s):

Deep Learning ◽

Gesture Recognition ◽

Smart Home ◽

Doppler Radar ◽

Radar Images ◽

Radar Signature ◽

Memory Efficiency ◽

High Compression ◽

Value Decomposition ◽

Deep Learning Model

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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