Topical Collection "Selected Papers from the PETRA Conference Series"

Editor

Dr. Fillia Makedon
E-Mail Website
Collection Editor
Department of Computer Sciences & Engineering, University of Texas at Arlington, 701 S Nedderman Drive, Arlington, TX 76019, USA
Interests: human–computer interaction; human–robot interaction; user interfaces; cognitive computing; Virtual Reality; Mixed Reality
Special Issues, Collections and Topics in MDPI journals

Topical Collection Information

Dear Colleagues,

We are planning to publish a Topic Collection related to the PETRA conference series. The latest events can be found at http://www.petrae.org/cfp.html. All the participants of the PETRA conference series and their colleagues are encouraged to submit their work to this Topic Collection. The deadline for the authors in PETRA2021 is 31 December 2021.

The PETRA conference publishes its proceedings in the ACM digital library, and the US National Science Foundation has supported the conference through its Doctoral Consortium Program for 12 years in a row, giving the opportunity to hundreds of student authors to participate by granting generous travel awards.

Research areas of interest include, but are not limited to, the following:

  • Healthcare informatics;
  • Big data management;
  • Data privacy and remote health monitoring;
  • Games for physical therapy and rehabilitation;
  • User interface design and usability;
  • Reasoning systems and machine learning;
  • Affective computing;
  • Cyberlearning: theory, methods, and technologies;
  • Human–robot interaction;
  • Human-centered computing;
  • Human monitoring;
  • Haptics;
  • Gesture and motion tracking;
  • Cognitive modeling;
  • Wearable computing;
  • Interactions and the Internet of Things (IoT);
  • Cognitive computing;
  • Disability computing.

Applications include, but are not limited to, the following:

  • Sensor networks for pervasive healthcare;
  • Mobile and wireless technologies;
  • Healthcare privacy and data security;
  • Smart rehabilitation systems;
  • Game design for cognitive assessment and social interaction;
  • Behavior monitoring systems;
  • Computer vision in healthcare;
  • Virtual and augmented reality environments;
  • Ambient assisted living;
  • Navigation systems;
  • Collaboration and data sharing;
  • Wearable devices;
  • Drug delivery evaluation;
  • Vocational safety and health monitoring;
  • Eyetracking;
  • Telemedicine and biotechnology;
  • Technologies for senior living;
  • Social impact of pervasive technologies;
  • Intelligent assistive environments;
  • Technologies to provide assessment and intervention for Stroke recovery, spinal cord injury, and multiple sclerosis;
  • Technologies for improving quality of daily living;
  • Robotics research for rehabilitation and tele-rehabilitation;
  • Innovative design for smart wheelchairs and smart canes;
  • Computer-based training systems for artificial limbs and prosthetics;
  • Disability computing: smart systems to assist persons with visual, hearing, and loss of limb functionalities;
  • Smart cities of the future;
  • Assistive technologies for urban environments.

Prof. Dr. Fillia Makedon
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 papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the collection 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. Technologies is an international peer-reviewed open access quarterly 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 1400 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.

Published Papers (13 papers)

2021

Jump to: 2020

Article
Visual Robotic Perception System with Incremental Learning for Child–Robot Interaction Scenarios
Technologies 2021, 9(4), 86; https://doi.org/10.3390/technologies9040086 - 15 Nov 2021
Viewed by 304
Abstract
This paper proposes a novel lightweight visual perception system with Incremental Learning (IL), tailored to child–robot interaction scenarios. Specifically, this encompasses both an action and emotion recognition module, with the former wrapped around an IL system, allowing novel actions to be easily added. [...] Read more.
This paper proposes a novel lightweight visual perception system with Incremental Learning (IL), tailored to child–robot interaction scenarios. Specifically, this encompasses both an action and emotion recognition module, with the former wrapped around an IL system, allowing novel actions to be easily added. This IL system enables the tutor aspiring to use robotic agents in interaction scenarios to further customize the system according to children’s needs. We perform extensive evaluations of the developed modules, achieving state-of-the-art results on both the children’s action BabyRobot dataset and the children’s emotion EmoReact dataset. Finally, we demonstrate the robustness and effectiveness of the IL system for action recognition by conducting a thorough experimental analysis for various conditions and parameters. Full article
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Article
Multiclass Confusion Matrix Reduction Method and Its Application on Net Promoter Score Classification Problem
Technologies 2021, 9(4), 81; https://doi.org/10.3390/technologies9040081 - 02 Nov 2021
Viewed by 312
Abstract
The current paper presents a novel method for reducing a multiclass confusion matrix into a 2×2 version enabling the exploitation of the relevant performance metrics and methods such as the receiver operating characteristic and area under the curve for the assessment [...] Read more.
The current paper presents a novel method for reducing a multiclass confusion matrix into a 2×2 version enabling the exploitation of the relevant performance metrics and methods such as the receiver operating characteristic and area under the curve for the assessment of different classification algorithms. The reduction method is based on class grouping and leads to a special type of matrix called the reduced confusion matrix. The developed method is then exploited for the assessment of state of the art machine learning algorithms applied on the net promoter score classification problem in the field of customer experience analytics indicating the value of the proposed method in real world classification problems. Full article
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Article
Image-Label Recovery on Fashion Data Using Image Similarity from Triple Siamese Network
Technologies 2021, 9(1), 10; https://doi.org/10.3390/technologies9010010 - 21 Jan 2021
Cited by 1 | Viewed by 1033
Abstract
Weakly labeled data are inevitable in various research areas in artificial intelligence (AI) where one has a modicum of knowledge about the complete dataset. One of the reasons for weakly labeled data in AI is insufficient accurately labeled data. Strict privacy control or [...] Read more.
Weakly labeled data are inevitable in various research areas in artificial intelligence (AI) where one has a modicum of knowledge about the complete dataset. One of the reasons for weakly labeled data in AI is insufficient accurately labeled data. Strict privacy control or accidental loss may also cause missing-data problems. However, supervised machine learning (ML) requires accurately labeled data in order to successfully solve a problem. Data labeling is difficult and time-consuming as it requires manual work, perfect results, and sometimes human experts to be involved (e.g., medical labeled data). In contrast, unlabeled data are inexpensive and easily available. Due to there not being enough labeled training data, researchers sometimes only obtain one or few data points per category or label. Training a supervised ML model from the small set of labeled data is a challenging task. The objective of this research is to recover missing labels from the dataset using state-of-the-art ML techniques using a semisupervised ML approach. In this work, a novel convolutional neural network-based framework is trained with a few instances of a class to perform metric learning. The dataset is then converted into a graph signal, which is recovered using a recover algorithm (RA) in graph Fourier transform. The proposed approach was evaluated on a Fashion dataset for accuracy and precision and performed significantly better than graph neural networks and other state-of-the-art methods. Full article
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Review
A Survey of Robots in Healthcare
Technologies 2021, 9(1), 8; https://doi.org/10.3390/technologies9010008 - 18 Jan 2021
Cited by 10 | Viewed by 3379
Abstract
In recent years, with the current advancements in Robotics and Artificial Intelligence (AI), robots have the potential to support the field of healthcare. Robotic systems are often introduced in the care of the elderly, children, and persons with disabilities, in hospitals, in rehabilitation [...] Read more.
In recent years, with the current advancements in Robotics and Artificial Intelligence (AI), robots have the potential to support the field of healthcare. Robotic systems are often introduced in the care of the elderly, children, and persons with disabilities, in hospitals, in rehabilitation and walking assistance, and other healthcare situations. In this survey paper, the recent advances in robotic technology applied in the healthcare domain are discussed. The paper provides detailed information about state-of-the-art research in care, hospital, assistive, rehabilitation, and walking assisting robots. The paper also discusses the open challenges healthcare robots face to be integrated into our society. Full article
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2020

Jump to: 2021

Review
A Survey on Contrastive Self-Supervised Learning
Technologies 2021, 9(1), 2; https://doi.org/10.3390/technologies9010002 - 28 Dec 2020
Cited by 22 | Viewed by 5862
Abstract
Self-supervised learning has gained popularity because of its ability to avoid the cost of annotating large-scale datasets. It is capable of adopting self-defined pseudolabels as supervision and use the learned representations for several downstream tasks. Specifically, contrastive learning has recently become a dominant [...] Read more.
Self-supervised learning has gained popularity because of its ability to avoid the cost of annotating large-scale datasets. It is capable of adopting self-defined pseudolabels as supervision and use the learned representations for several downstream tasks. Specifically, contrastive learning has recently become a dominant component in self-supervised learning for computer vision, natural language processing (NLP), and other domains. It aims at embedding augmented versions of the same sample close to each other while trying to push away embeddings from different samples. This paper provides an extensive review of self-supervised methods that follow the contrastive approach. The work explains commonly used pretext tasks in a contrastive learning setup, followed by different architectures that have been proposed so far. Next, we present a performance comparison of different methods for multiple downstream tasks such as image classification, object detection, and action recognition. Finally, we conclude with the limitations of the current methods and the need for further techniques and future directions to make meaningful progress. Full article
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Article
A Review of Extended Reality (XR) Technologies for Manufacturing Training
Technologies 2020, 8(4), 77; https://doi.org/10.3390/technologies8040077 - 10 Dec 2020
Cited by 9 | Viewed by 2494
Abstract
Recently, the use of extended reality (XR) systems has been on the rise, to tackle various domains such as training, education, safety, etc. With the recent advances in augmented reality (AR), virtual reality (VR) and mixed reality (MR) technologies and ease of availability [...] Read more.
Recently, the use of extended reality (XR) systems has been on the rise, to tackle various domains such as training, education, safety, etc. With the recent advances in augmented reality (AR), virtual reality (VR) and mixed reality (MR) technologies and ease of availability of high-end, commercially available hardware, the manufacturing industry has seen a rise in the use of advanced XR technologies to train its workforce. While several research publications exist on applications of XR in manufacturing training, a comprehensive review of recent works and applications is lacking to present a clear progress in using such advance technologies. To this end, we present a review of the current state-of-the-art of use of XR technologies in training personnel in the field of manufacturing. First, we put forth the need of XR in manufacturing. We then present several key application domains where XR is being currently applied, notably in maintenance training and in performing assembly task. We also reviewed the applications of XR in other vocational domains and how they can be leveraged in the manufacturing industry. We finally present some current barriers to XR adoption in manufacturing training and highlight the current limitations that should be considered when looking to develop and apply practical applications of XR. Full article
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Article
A Machine Learning Based Classification Method for Customer Experience Survey Analysis
Technologies 2020, 8(4), 76; https://doi.org/10.3390/technologies8040076 - 07 Dec 2020
Cited by 2 | Viewed by 1628
Abstract
Customer Experience (CX) is monitored through market research surveys, based on metrics like the Net Promoter Score (NPS) and the customer satisfaction for certain experience attributes (e.g., call center, website, billing, service quality, tariff plan). The objective of companies is to maximize NPS [...] Read more.
Customer Experience (CX) is monitored through market research surveys, based on metrics like the Net Promoter Score (NPS) and the customer satisfaction for certain experience attributes (e.g., call center, website, billing, service quality, tariff plan). The objective of companies is to maximize NPS through the improvement of the most important CX attributes. However, statistical analysis suggests that there is a lack of clear and accurate association between NPS and the CX attributes’ scores. In this paper, we address the aforementioned deficiency using a novel classification approach, which was developed based on logistic regression and tested with several state-of-the-art machine learning (ML) algorithms. The proposed method was applied on an extended data set from the telecommunication sector and the results were quite promising, showing a significant improvement in most statistical metrics. Full article
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Article
Comparative Analysis of Real-Time Fall Detection Using Fuzzy Logic Web Services and Machine Learning
Technologies 2020, 8(4), 74; https://doi.org/10.3390/technologies8040074 - 03 Dec 2020
Cited by 2 | Viewed by 904
Abstract
Falls are the main cause of susceptibility to severe injuries in many humans, especially for older adults aged 65 and over. Typically, falls are being unnoticed and interpreted as a mere inevitable accident. Various wearable fall warning devices have been created recently for [...] Read more.
Falls are the main cause of susceptibility to severe injuries in many humans, especially for older adults aged 65 and over. Typically, falls are being unnoticed and interpreted as a mere inevitable accident. Various wearable fall warning devices have been created recently for older people. However, most of these devices are dependent on local data processing. Various algorithms are used in wearable sensors to track a real-time fall effectively, which focuses on fall detection via fuzzy-as-a-service based on IEEE 1855–2016, Java Fuzzy Markup Language (FML) and service-oriented architecture. Moreover, several approaches are used to detect a fall using machine learning techniques via human movement positional data to avert any accidents. For fuzzy logic web services, analysis is performed using wearable accelerometer and gyroscope sensors, whereas in machine learning techniques, k-NN, decision tree, random forest and extreme gradient boost are used to differentiate between a fall and non-fall. This study aims to carry out a comparative analysis of real-time fall detection using fuzzy logic web services and machine learning techniques and aims to determine which one is better for real-time fall detection. Research findings exhibit that the proposed fuzzy-as-a-service could easily differentiate between fall and non-fall occurrences in a real-time environment with an accuracy, sensitivity and specificity of 90%, 88.89% and 91.67%, respectively, while the random forest algorithm of machine learning achieved 99.19%, 98.53% and 99.63%, respectively. Full article
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Article
Deep Learning Based Fall Detection Algorithms for Embedded Systems, Smartwatches, and IoT Devices Using Accelerometers
Technologies 2020, 8(4), 72; https://doi.org/10.3390/technologies8040072 - 02 Dec 2020
Cited by 6 | Viewed by 1397
Abstract
A fall of an elderly person often leads to serious injuries or even death. Many falls occur in the home environment and remain unrecognized. Therefore, a reliable fall detection is absolutely necessary for a fast help. Wrist-worn accelerometer based fall detection systems are [...] Read more.
A fall of an elderly person often leads to serious injuries or even death. Many falls occur in the home environment and remain unrecognized. Therefore, a reliable fall detection is absolutely necessary for a fast help. Wrist-worn accelerometer based fall detection systems are developed, but the accuracy and precision are not standardized, comparable, or sometimes even known. In this work, we present an overview about existing public databases with sensor based fall datasets and harmonize existing wrist-worn datasets for a broader and robust evaluation. Furthermore, we are analyzing the current possible recognition rate of fall detection using deep learning algorithms for mobile and embedded systems. The presented results and databases can be used for further research and optimizations in order to increase the recognition rate to enhance the independent life of the elderly. Furthermore, we give an outlook for a convenient application and wrist device. Full article
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Article
Using Bias Parity Score to Find Feature-Rich Models with Least Relative Bias
Technologies 2020, 8(4), 68; https://doi.org/10.3390/technologies8040068 - 14 Nov 2020
Viewed by 916
Abstract
Machine learning-based decision support systems bring relief and support to the decision-maker in many domains such as loan application acceptance, dating, hiring, granting parole, insurance coverage, and medical diagnoses. These support systems facilitate processing tremendous amounts of data to decipher the patterns embedded [...] Read more.
Machine learning-based decision support systems bring relief and support to the decision-maker in many domains such as loan application acceptance, dating, hiring, granting parole, insurance coverage, and medical diagnoses. These support systems facilitate processing tremendous amounts of data to decipher the patterns embedded in them. However, these decisions can also absorb and amplify bias embedded in the data. To address this, the work presented in this paper introduces a new fairness measure as well as an enhanced, feature-rich representation derived from the temporal aspects in the data set that permits the selection of the lowest bias model among the set of models learned on various versions of the augmented feature set. Specifically, our approach uses neural networks to forecast recidivism from many unique feature-rich models created from the same raw offender dataset. We create multiple records from one summarizing criminal record per offender in the raw dataset. This is achieved by grouping each set of arrest to release information into a unique record. We use offenders’ criminal history, substance abuse, and treatments taken during imprisonment in different numbers of past arrests to enrich the input feature vectors for the prediction models generated. We propose a fairness measure called Bias Parity (BP) score to measure quantifiable decrease in bias in the prediction models. BP score leverages an existing intuition of bias awareness and summarizes it in a single measure. We demonstrate how BP score can be used to quantify bias for a variety of statistical quantities and how to associate disparate impact with this measure. By using our feature enrichment approach we could increase the accuracy of predicting recidivism for the same dataset from 77.8% in another study to 89.2% in the current study while achieving an improved BP score computed for average accuracy of 99.4, where a value of 100 means no bias for the two subpopulation groups compared. Moreover, an analysis of the accuracy and BP scores for various levels of our feature augmentation method shows consistent trends among scores for a range of fairness measures, illustrating the benefit of the method for picking fairer models without significant loss of accuracy. Full article
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Article
Performing Realistic Workout Activity Recognition on Consumer Smartphones
Technologies 2020, 8(4), 65; https://doi.org/10.3390/technologies8040065 - 06 Nov 2020
Viewed by 902
Abstract
Smartphones have become an essential part of our lives. Especially its computing power and its current specifications make a modern smartphone a powerful device for human activity recognition tasks. Equipped with various integrated sensors, a modern smartphone can be leveraged for lots of [...] Read more.
Smartphones have become an essential part of our lives. Especially its computing power and its current specifications make a modern smartphone a powerful device for human activity recognition tasks. Equipped with various integrated sensors, a modern smartphone can be leveraged for lots of smart applications. We already investigated the possibility of using an unmodified commercial smartphone to recognize eight strength-based exercises. App-based workouts have become popular in the last few years. The advantage of using a mobile device is that you can practice anywhere at anytime. In our previous work, we proved the possibility of turning a commercial smartphone into an active sonar device to leverage the echo reflected from exercising movement close to the device. By conducting a test study with 14 participants, we showed the first results for cross person evaluation and the generalization ability of our inference models on disjoint participants. In this work, we extended another model to further improve the model generalizability and provided a thorough comparison of our proposed system to other existing state-of-the-art approaches. Finally, a concept of counting the repetitions is also provided in this study as a parallel task to classification. Full article
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Article
Connections between Older Greek Adults’ Implicit Attributes and Their Perceptions of Online Technologies
Technologies 2020, 8(4), 57; https://doi.org/10.3390/technologies8040057 - 25 Oct 2020
Viewed by 1041
Abstract
Older Greek adults make use of web technologies much less than the majority of their peers in Europe. Based on the fact that psychosocial attributes can also affect technology usage, this exploratory quantitative research is an attempt to focus on the implicit factors [...] Read more.
Older Greek adults make use of web technologies much less than the majority of their peers in Europe. Based on the fact that psychosocial attributes can also affect technology usage, this exploratory quantitative research is an attempt to focus on the implicit factors related to older Greek adults’ perceived usability, learnability, and ease-of-use of web technologies. For this aim, a web 2.0 storytelling prototype has been demonstrated to 112 participants and an online questionnaire was applied for data collection. According to the results, distinct correlations emerged between older adults’ characteristics (chronological age, loneliness, future time perspective) and the perceived usability, learnability, and ease-of-use of the presented prototype. These outcomes contribute to the limited literature in the field by probing the connections between older people’s implicit attributes and their evaluative perceptions of online technologies. Full article
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Article
ExerTrack—Towards Smart Surfaces to Track Exercises
Technologies 2020, 8(1), 17; https://doi.org/10.3390/technologies8010017 - 17 Mar 2020
Cited by 5 | Viewed by 2480
Abstract
The concept of the quantified self has gained popularity in recent years with the hype of miniaturized gadgets to monitor vital fitness levels. Smartwatches or smartphone apps and other fitness trackers are overwhelming the market. Most aerobic exercises such as walking, running, or [...] Read more.
The concept of the quantified self has gained popularity in recent years with the hype of miniaturized gadgets to monitor vital fitness levels. Smartwatches or smartphone apps and other fitness trackers are overwhelming the market. Most aerobic exercises such as walking, running, or cycling can be accurately recognized using wearable devices. However whole-body exercises such as push-ups, bridges, and sit-ups are performed on the ground and thus cannot be precisely recognized by wearing only one accelerometer. Thus, a floor-based approach is preferred for recognizing whole-body activities. Computer vision techniques on image data also report high recognition accuracy; however, the presence of a camera tends to raise privacy issues in public areas. Therefore, we focus on combining the advantages of ubiquitous proximity-sensing with non-optical sensors to preserve privacy in public areas and maintain low computation cost with a sparse sensor implementation. Our solution is the ExerTrack, an off-the-shelf sports mat equipped with eight sparsely distributed capacitive proximity sensors to recognize eight whole-body fitness exercises with a user-independent recognition accuracy of 93.5% and a user-dependent recognition accuracy of 95.1% based on a test study with 9 participants each performing 2 full sessions. We adopt a template-based approach to count repetitions and reach a user-independent counting accuracy of 93.6%. The final model can run on a Raspberry Pi 3 in real time. This work includes data-processing of our proposed system and model selection to improve the recognition accuracy and data augmentation technique to regularize the network. Full article
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