Human–Computer Interactive Gesture Feature Capture and Recognition in Virtual Reality

Zhang, Fan

doi:10.1177/1064804620924133

Cited by 5 publications

(5 citation statements)

References 15 publications

(15 reference statements)

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“…Work on hand gesture recognition was originally seen as a method of human-computer interaction, with [3] exploring three different gesture feature extraction applications, scale invariant feature transform (SIFT), local binary pattern (LBP), and histogram of oriented gradients (HOG). Most applications have been developed on static gesture recognition, i.e., geometric feature extraction, the number of fingers lifted in a gesture, the distance from the center of the palm of the hand to the fingertips and the valley between the fingers [4].…”

Section: Related Workmentioning

confidence: 99%

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A Moroccan Sign Language Recognition Algorithm Using a Convolution Neural Network

Herbaz

Idrissi

Badri

2022

JICTS

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Gesture recognition is an open phenomenon in computer vision, and one of the topics of current interest. Gesture recognition has many applications in the interpretation of sign language in deaf-mutes, one is in human-computer interaction, and the other is in immersive game technology. For this purpose, we have developed a model of image processing recognition of gestures, based on Artificial Neural Networks, starting from data collection, identification, tracking and classification of gestures, to the display of the obtained results. We propose an approach to contribute to the translation of sign language into voice/text format. In this paper, we present a Moroccan sign language recognition system using a Convolutional Neural Network (CNN). This system includes an important data set of more than 20 files. Each file contains 1,000 static images of each signal from several different angles that we collected with the camera. Different Sign Language models were evaluated and compared with the proposed CNN model. The proposed system achieved 99.33% and achieved the best performance with an accuracy rate of 98.7%.

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Section: Related Workmentioning

confidence: 99%

“…It is performed by remembering long-term information and deciding which value to pass on to the other time step block, with W i and b being the weights and bias parameters associated with each Z i gate. These parameters are used to minimize the error of our training data, as shown in Equations ( 2) and (3).…”

Section: Equationsmentioning

confidence: 99%

A Moroccan Sign Language Recognition Algorithm Using a Convolution Neural Network

Herbaz

Idrissi

Badri

2022

JICTS

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“…With the continuous progress of novel sensing and computing techniques, Human Activity Recognition (HAR) has attracted extensive attention and become a promising approach for applications in medical treatment, body kinematics, Human-Computer Interaction (HCI), Virtual Reality (VR), motion analysis, Daily Living Assistant (DLA) and elderly care [1]- [4]. Compared with Computer Vision (CV)-based techniques, the wearable sensing device-based solution breaks the limits of space by getting rid of the fixed and bulky equipment.…”

Section: Introductionmentioning

confidence: 99%

Integrated Sensing and Computing for Wearable Human Activity Recognition with MEMS IMU and BLE Network

Zhang

Liu

Meng

et al. 2022

Measurement Science Review

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The miniature sensor devices and power-efficient Body Area Networks (BANs) for Human Activity Recognition (HAR) have gained increasing interest in different fields, including Daily Life Assistants (DLAs), medical treatment, sports analysis, etc. The HAR systems normally collect data with wearable sensors and implement the computational tasks with a host machine, where real-time transmission and processing of sensor data raise a challenge for both the network and the host machine. This investigation focuses on the hardware/software co-design for optimized sensing and computing of wearable HAR sensor networks. The contributions include (1) design of a miniature wearable sensor node integrating a Micro-Electro-Mechanical System Inertial Measurement Unit (MEMS IMU) with a Bluetooth Low Energy (BLE) in-built Micro-Control Unit (MCU) for unobtrusive wearable sensing; (2) task-centric optimization of the computation by shifting data pre-processing and feature extraction to sensor nodes for in-situ computing, which reduces data transmission and relieves the load of the host machine; (3) optimization and evaluation of classification algorithms Particle Swarm Optimization-based Support Vector Machine (PSO-SVM) and Cross Validation-based K-Nearest Neighbors (CV-KNN) for HAR with the presented techniques. Finally, experimental studies were conducted with two sensor nodes worn on the wrist and elbow to verify the effectiveness of the recognition of 10 virtual handwriting activities, where 10 recruited participants each repeated an activity 5 times. The results demonstrate that the proposed system can implement HAR tasks effectively with an accuracy of 99.20 %.

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“…Human-machine interaction (HMI) may be a critical area, where human gesture, posture, or motion can be recognized for the efficient interaction between machines covering a wide field of applications, including human-object interaction, virtual reality, immersive entertainment, etc. [4][5][6]. Human activity recognition and motion analysis has also been an effective way for sports analysis evaluation, while peer investigations are identified for golf swing analysis [7], swimming velocity estimation [8], and sports training [9].…”

Section: Introductionmentioning

confidence: 99%

Recent Progress in Sensing and Computing Techniques for Human Activity Recognition and Motion Analysis

et al. 2020

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The recent scientific and technical advances in Internet of Things (IoT) based pervasive sensing and computing have created opportunities for the continuous monitoring of human activities for different purposes. The topic of human activity recognition (HAR) and motion analysis, due to its potentiality in human–machine interaction (HMI), medical care, sports analysis, physical rehabilitation, assisted daily living (ADL), children and elderly care, has recently gained increasing attention. The emergence of some novel sensing devices featuring miniature size, a light weight, and wireless data transmission, the availability of wireless communication infrastructure, the progress of machine learning and deep learning algorithms, and the widespread IoT applications has promised new opportunities for a significant progress in this particular field. Motivated by a great demand for HAR-related applications and the lack of a timely report of the recent contributions to knowledge in this area, this investigation aims to provide a comprehensive survey and in-depth analysis of the recent advances in the diverse techniques and methods of human activity recognition and motion analysis. The focus of this investigation falls on the fundamental theories, the innovative applications with their underlying sensing techniques, data fusion and processing, and human activity classification methods. Based on the state-of-the-art, the technical challenges are identified, and future perspectives on the future rich, sensing, intelligent IoT world are given in order to provide a reference for the research and practices in the related fields.

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Human–Computer Interactive Gesture Feature Capture and Recognition in Virtual Reality

Cited by 5 publications

References 15 publications

A Moroccan Sign Language Recognition Algorithm Using a Convolution Neural Network

A Moroccan Sign Language Recognition Algorithm Using a Convolution Neural Network

Integrated Sensing and Computing for Wearable Human Activity Recognition with MEMS IMU and BLE Network

Recent Progress in Sensing and Computing Techniques for Human Activity Recognition and Motion Analysis

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