Smart Glove and Hand Gesture-Based Control Interface for Multi-Rotor Aerial Vehicles in a Multi-Subject Environment

Haratiannejadi, Kianoush; Šelmić, Rastko R.

doi:10.1109/access.2020.3045858

Cited by 7 publications

(2 citation statements)

References 29 publications

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“…Hand gesture recognition can be implemented by using a vision camera or wearable sensors [ 108 ]. Wearable sensors and gesture recognition techniques have been used to develop wearable motion sensors for the hearing- and speech-impaired and wearable gesture-based gadgets for interaction with mobile devices [ 109 ]. Recently, depth-based gesture recognition has received much intention in HCII as well [ 110 ].…”

Section: Backgrounds and Related Workmentioning

confidence: 99%

Sensors and Artificial Intelligence Methods and Algorithms for Human–Computer Intelligent Interaction: A Systematic Mapping Study

Šumak

Brdnik

Pušnik

2021

Sensors

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To equip computers with human communication skills and to enable natural interaction between the computer and a human, intelligent solutions are required based on artificial intelligence (AI) methods, algorithms, and sensor technology. This study aimed at identifying and analyzing the state-of-the-art AI methods and algorithms and sensors technology in existing human–computer intelligent interaction (HCII) research to explore trends in HCII research, categorize existing evidence, and identify potential directions for future research. We conduct a systematic mapping study of the HCII body of research. Four hundred fifty-four studies published in various journals and conferences between 2010 and 2021 were identified and analyzed. Studies in the HCII and IUI fields have primarily been focused on intelligent recognition of emotion, gestures, and facial expressions using sensors technology, such as the camera, EEG, Kinect, wearable sensors, eye tracker, gyroscope, and others. Researchers most often apply deep-learning and instance-based AI methods and algorithms. The support sector machine (SVM) is the most widely used algorithm for various kinds of recognition, primarily an emotion, facial expression, and gesture. The convolutional neural network (CNN) is the often-used deep-learning algorithm for emotion recognition, facial recognition, and gesture recognition solutions.

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Section: Backgrounds and Related Workmentioning

confidence: 99%

Sensors and Artificial Intelligence Methods and Algorithms for Human–Computer Intelligent Interaction: A Systematic Mapping Study

Šumak

Brdnik

Pušnik

2021

Sensors

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“…Gesture serves a crucial function in daily life as a versatile tool for conveying information (Theodoridou et al , 2023; Duarte et al , 2021). Gesture recognition plays a very crucial role in medical rehabilitation (Chen et al , 2020; Amsterdam et al , 2021), virtual and augmented reality (Shen et al , 2022; Park et al , 2021), human–computer interaction (Villani et al , 2023; Carfì and Mastrogiovanni, 2023), drone control (Yu et al , 2022; Haratiannejadi and Selmic, 2020) and other fields. Gesture recognition using wearable devices is a common and effective recognition method, and wearable devices use a variety of sensors for gesture monitoring, common sensors are strain, electromyographic, curvature resistive and textile fabric sensors (Dong et al , 2022; Randhawa et al , 2020).…”

Section: Introductionmentioning

confidence: 99%

Structural design of magnetostrictive sensing glove and its application for gesture recognition

Hu,

Weng,

Liu

et al. 2024

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Purpose Gesture recognition plays an important role in many fields such as human–computer interaction, medical rehabilitation, virtual and augmented reality. Gesture recognition using wearable devices is a common and effective recognition method. This study aims to combine the inverse magnetostrictive effect and tunneling magnetoresistance effect and proposes a novel wearable sensing glove applied in the field of gesture recognition. Design/methodology/approach A magnetostrictive sensing glove with function of gesture recognition is proposed based on Fe-Ni alloy, tunneling magnetoresistive elements, Agilus30 base and square permanent magnets. The sensing glove consists of five sensing units to measure the bending angle of each finger joint. The optimal structure of the sensing units is determined through experimentation and simulation. The output voltage model of the sensing units is established, and the output characteristics of the sensing units are tested by the experimental platform. Fifteen gestures are selected for recognition, and the corresponding output voltages are collected to construct the data set and the data is processed using Back Propagation Neural Network. Findings The sensing units can detect the change in the bending angle of finger joints from 0 to 105 degrees and a maximum error of 4.69% between the experimental and theoretical values. The average recognition accuracy of Back Propagation Neural Network is 97.53% for 15 gestures. Research limitations/implications The sensing glove can only recognize static gestures at present, and further research is still needed to recognize dynamic gestures. Practical implications A new approach to gesture recognition using wearable devices. Social implications This study has a broad application prospect in the field of human–computer interaction. Originality/value The sensing glove can collect voltage signals under different gestures to realize the recognition of different gestures with good repeatability, which has a broad application prospect in the field of human–computer interaction.

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Hand Gesture Control of Video Player

Sangeetha

Hemanth

Nair

et al. 2023

Lecture Notes in Networks and Systems

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Smart Glove and Hand Gesture-Based Control Interface for Multi-Rotor Aerial Vehicles in a Multi-Subject Environment

Cited by 7 publications

References 29 publications

Sensors and Artificial Intelligence Methods and Algorithms for Human–Computer Intelligent Interaction: A Systematic Mapping Study

Sensors and Artificial Intelligence Methods and Algorithms for Human–Computer Intelligent Interaction: A Systematic Mapping Study

Structural design of magnetostrictive sensing glove and its application for gesture recognition

Hand Gesture Control of Video Player

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