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Special Issue "Vision and Sensor-Based Sensing in Human Action Recognition"

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Sensing and Imaging".

Deadline for manuscript submissions: 28 February 2023 | Viewed by 6535

Special Issue Editor

Prof. Dr. Jungpil Shin
E-Mail Website
Guest Editor
Pattern Processing Lab, School of Computer Science and Engineering, The University of Aizu, Aizu-Wakamatsu, Fukushima 965-8580, Japan
Interests: pattern recognition; character recognition; image processing; computer vision; human computer interaction; neurological disease analysis; machine learning

Special Issue Information

The goal of this Special Issue is to help to overcome the gap between human action recognition and its involvement in the development of many important applications such as human–computer interaction (HCI), virtual reality, security, the internet of things (IoT), healthcare facilities, and so on.

Over the past few decades, video-based or sensor-based sensing for human action recognition has received tremendous attention from the research community due to its wide range of applications and the recent advancement of computational performance, camera and sensor technology, and algorithms of machine learning and deep learning.

In this Special Issue of vision and sensor-based sensing in human action recognition, we are aiming to publish novel and technically sound research articles that demonstrate theoretical and practical contributions to computer vision, machine learning, AI, sensing, and medical and social issues.

Topics of interest include, but are not limited to:

  • Human action recognition from camera, video, and other relevant sensor data
  • Nontouch and touch interfaces using human action
  • Deep learning approach for human action recognition
  • Handwriting action analysis and recognition
  • Medical diagnosis and recognition using human action
  • Biosignal processing for human action recognition
  • Health care application using human action
  • Virtual reality, augmented reality, and other applications using human action
  • Human action analysis and recognition for social issues
  • Large datasets on human action recognition
  • Current state-of-the-art and future trends of human action recognition

Prof. Dr. Jungpil Shin
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Human–computer interaction
  • Hand gesture
  • Nontouch and touch interfaces
  • Handwriting action
  • Wearable sensors, nonwearable sensors
  • Video-based sensors
  • Medical diagnosis and recognition
  • Biosignal processing
  • Virtual reality, augmented reality
  • Machine learning
  • Deep learning

Published Papers (6 papers)

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Research

Article
Toward COVID-19 Contact Tracing though Wi-Fi Probes
Sensors 2022, 22(6), 2255; https://doi.org/10.3390/s22062255 - 14 Mar 2022
Viewed by 487
Abstract
COVID-19 is currently the biggest threat that challenges all of humankind’s health and property. One promising and effective way to control the rapid spreading of this infection is searching for primary close contacts of the confirmed cases. In response, we propose COVID-19 Tracer, [...] Read more.
COVID-19 is currently the biggest threat that challenges all of humankind’s health and property. One promising and effective way to control the rapid spreading of this infection is searching for primary close contacts of the confirmed cases. In response, we propose COVID-19 Tracer, a low-cost passive searching system to find COVID-19 patients’ close contacts. The main idea is utilizing ubiquitous WiFi probe requests to describe the location similarity, which is then achieved by two designed range-free judgment indicators: location similarity coefficient and close contact distance. We have carried out extensive experiments in a school office building, and the experimental results show an average accuracy of more than 98%, demonstrating our system’s effectiveness in judging close contacts. Last but not least, we have developed a prototype system for a school building to find potential close contacts. Full article
(This article belongs to the Special Issue Vision and Sensor-Based Sensing in Human Action Recognition)
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Article
Deep Learning Based Air-Writing Recognition with the Choice of Proper Interpolation Technique
Sensors 2021, 21(24), 8407; https://doi.org/10.3390/s21248407 - 16 Dec 2021
Cited by 1 | Viewed by 713
Abstract
The act of writing letters or words in free space with body movements is known as air-writing. Air-writing recognition is a special case of gesture recognition in which gestures correspond to characters and digits written in the air. Air-writing, unlike general gestures, does [...] Read more.
The act of writing letters or words in free space with body movements is known as air-writing. Air-writing recognition is a special case of gesture recognition in which gestures correspond to characters and digits written in the air. Air-writing, unlike general gestures, does not require the memorization of predefined special gesture patterns. Rather, it is sensitive to the subject and language of interest. Traditional air-writing requires an extra device containing sensor(s), while the wide adoption of smart-bands eliminates the requirement of the extra device. Therefore, air-writing recognition systems are becoming more flexible day by day. However, the variability of signal duration is a key problem in developing an air-writing recognition model. Inconsistent signal duration is obvious due to the nature of the writing and data-recording process. To make the signals consistent in length, researchers attempted various strategies including padding and truncating, but these procedures result in significant data loss. Interpolation is a statistical technique that can be employed for time-series signals to ensure minimum data loss. In this paper, we extensively investigated different interpolation techniques on seven publicly available air-writing datasets and developed a method to recognize air-written characters using a 2D-CNN model. In both user-dependent and user-independent principles, our method outperformed all the state-of-the-art methods by a clear margin for all datasets. Full article
(This article belongs to the Special Issue Vision and Sensor-Based Sensing in Human Action Recognition)
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Article
American Sign Language Alphabet Recognition by Extracting Feature from Hand Pose Estimation
Sensors 2021, 21(17), 5856; https://doi.org/10.3390/s21175856 - 31 Aug 2021
Cited by 6 | Viewed by 1265
Abstract
Sign language is designed to assist the deaf and hard of hearing community to convey messages and connect with society. Sign language recognition has been an important domain of research for a long time. Previously, sensor-based approaches have obtained higher accuracy than vision-based [...] Read more.
Sign language is designed to assist the deaf and hard of hearing community to convey messages and connect with society. Sign language recognition has been an important domain of research for a long time. Previously, sensor-based approaches have obtained higher accuracy than vision-based approaches. Due to the cost-effectiveness of vision-based approaches, researchers have been conducted here also despite the accuracy drop. The purpose of this research is to recognize American sign characters using hand images obtained from a web camera. In this work, the media-pipe hands algorithm was used for estimating hand joints from RGB images of hands obtained from a web camera and two types of features were generated from the estimated coordinates of the joints obtained for classification: one is the distances between the joint points and the other one is the angles between vectors and 3D axes. The classifiers utilized to classify the characters were support vector machine (SVM) and light gradient boosting machine (GBM). Three character datasets were used for recognition: the ASL Alphabet dataset, the Massey dataset, and the finger spelling A dataset. The results obtained were 99.39% for the Massey dataset, 87.60% for the ASL Alphabet dataset, and 98.45% for Finger Spelling A dataset. The proposed design for automatic American sign language recognition is cost-effective, computationally inexpensive, does not require any special sensors or devices, and has outperformed previous studies. Full article
(This article belongs to the Special Issue Vision and Sensor-Based Sensing in Human Action Recognition)
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Article
A Bayesian Dynamical Approach for Human Action Recognition
Sensors 2021, 21(16), 5613; https://doi.org/10.3390/s21165613 - 20 Aug 2021
Cited by 3 | Viewed by 819
Abstract
We introduce a generative Bayesian switching dynamical model for action recognition in 3D skeletal data. Our model encodes highly correlated skeletal data into a few sets of low-dimensional switching temporal processes and from there decodes to the motion data and their associated action [...] Read more.
We introduce a generative Bayesian switching dynamical model for action recognition in 3D skeletal data. Our model encodes highly correlated skeletal data into a few sets of low-dimensional switching temporal processes and from there decodes to the motion data and their associated action labels. We parameterize these temporal processes with regard to a switching deep autoregressive prior to accommodate both multimodal and higher-order nonlinear inter-dependencies. This results in a dynamical deep generative latent model that parses meaningful intrinsic states in skeletal dynamics and enables action recognition. These sequences of states provide visual and quantitative interpretations about motion primitives that gave rise to each action class, which have not been explored previously. In contrast to previous works, which often overlook temporal dynamics, our method explicitly model temporal transitions and is generative. Our experiments on two large-scale 3D skeletal datasets substantiate the superior performance of our model in comparison with the state-of-the-art methods. Specifically, our method achieved 6.3% higher action classification accuracy (by incorporating a dynamical generative framework), and 3.5% better predictive error (by employing a nonlinear second-order dynamical transition model) when compared with the best-performing competitors. Full article
(This article belongs to the Special Issue Vision and Sensor-Based Sensing in Human Action Recognition)
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Article
INIM: Inertial Images Construction with Applications to Activity Recognition
Sensors 2021, 21(14), 4787; https://doi.org/10.3390/s21144787 - 13 Jul 2021
Cited by 1 | Viewed by 858
Abstract
Human activity recognition aims to classify the user activity in various applications like healthcare, gesture recognition and indoor navigation. In the latter, smartphone location recognition is gaining more attention as it enhances indoor positioning accuracy. Commonly the smartphone’s inertial sensor readings are used [...] Read more.
Human activity recognition aims to classify the user activity in various applications like healthcare, gesture recognition and indoor navigation. In the latter, smartphone location recognition is gaining more attention as it enhances indoor positioning accuracy. Commonly the smartphone’s inertial sensor readings are used as input to a machine learning algorithm which performs the classification. There are several approaches to tackle such a task: feature based approaches, one dimensional deep learning algorithms, and two dimensional deep learning architectures. When using deep learning approaches, feature engineering is redundant. In addition, while utilizing two-dimensional deep learning approaches enables to utilize methods from the well-established computer vision domain. In this paper, a framework for smartphone location and human activity recognition, based on the smartphone’s inertial sensors, is proposed. The contributions of this work are a novel time series encoding approach, from inertial signals to inertial images, and transfer learning from computer vision domain to the inertial sensors classification problem. Four different datasets are employed to show the benefits of using the proposed approach. In addition, as the proposed framework performs classification on inertial sensors readings, it can be applied for other classification tasks using inertial data. It can also be adopted to handle other types of sensory data collected for a classification task. Full article
(This article belongs to the Special Issue Vision and Sensor-Based Sensing in Human Action Recognition)
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Article
Gaze and Event Tracking for Evaluation of Recommendation-Driven Purchase
Sensors 2021, 21(4), 1381; https://doi.org/10.3390/s21041381 - 16 Feb 2021
Cited by 11 | Viewed by 1182
Abstract
Recommendation systems play an important role in e-commerce turnover by presenting personalized recommendations. Due to the vast amount of marketing content online, users are less susceptible to these suggestions. In addition to the accuracy of a recommendation, its presentation, layout, and other visual [...] Read more.
Recommendation systems play an important role in e-commerce turnover by presenting personalized recommendations. Due to the vast amount of marketing content online, users are less susceptible to these suggestions. In addition to the accuracy of a recommendation, its presentation, layout, and other visual aspects can improve its effectiveness. This study evaluates the visual aspects of recommender interfaces. Vertical and horizontal recommendation layouts are tested, along with different visual intensity levels of item presentation, and conclusions obtained with a number of popular machine learning methods are discussed. Results from the implicit feedback study of the effectiveness of recommending interfaces for four major e-commerce websites are presented. Two different methods of observing user behavior were used, i.e., eye-tracking and document object model (DOM) implicit event tracking in the browser, which allowed collecting a large amount of data related to user activity and physical parameters of recommending interfaces. Results have been analyzed in order to compare the reliability and applicability of both methods. Observations made with eye tracking and event tracking led to similar results regarding recommendation interface evaluation. In general, vertical interfaces showed higher effectiveness compared to horizontal ones, with the first and second positions working best, and the worse performance of horizontal interfaces probably being connected with banner blindness. Neural networks provided the best modeling results of the recommendation-driven purchase (RDP) phenomenon. Full article
(This article belongs to the Special Issue Vision and Sensor-Based Sensing in Human Action Recognition)
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