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A special issue of Sustainability (ISSN 2071-1050). This special issue belongs to the section "Sustainable Engineering and Science".

Deadline for manuscript submissions: closed (31 December 2022) | Viewed by 23983

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Special Issue Editors

Prof. Kibum Kim

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Guest Editor

Department of Human-Computer Interaction, Hanyang University, Ansan 15588, South Korea
Interests: Human-Computer Interaction; Virtual Reality, Augmented Reality, Mixed Reality; Assistive Technology; Serious Game; Multimodal User Interfaces
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Xiangshi Ren

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Guest Editor

School of Information, Kochi University of Technology, Japan
Interests: Human-Computer Interaction; Pen-based Computing; Human-Engaged Computing; Multi-touch Interaction, User Interfaces for Older Users and Blind Users

Prof. Dr. Ahmad Jalal

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Guest Editor

Department of Computer Science, Air University, Pakistan
Interests: Artificial Intelligence; Pattern Recognition: Image, Signal and Video Processing: Multimedia Technologies; Computer Vision; Virtual Reality

Special Issue Information

Dear Colleagues,

Human-computer interaction (HCI) broadens its boundaries, takes an important role in tackling global sustainability issues, and entrenches its multi-scalar perspectives to address complex sustainability challenges.

This special issue aims to publish high-quality research work on the inter-disciplinary field of sustainable HCI (SHCI) in terms of highlighting opportunities for SHCI to contribute in achieving a more sustainable future. Beyond the traditional HCI research which emphasizes efficient, productive, and novel consumer products, we will explore the alternative design concepts such as appropriate technology, slow technology, not-to-technology, negation of design by design, designing digital limitations, and pro-environmental design. Any work on sustainable HCI that has a relation to any of these fields is welcome.

In this special issue, we seek submissions of original research in the field of interaction design and sustainability, demonstrating the diversity of approaches across HCI communities.

Prof. Kibum Kim
Prof. Xiangshi Ren
Prof. Ahmad Jalal
Guest Editors

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. Sustainability 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

Sustainable human-computer interaction (SHCI)
Sustainable interaction design (SID)
Green IT
Ecological design
Sustainable VR/AR
Self-powered interfaces and interactions
Novel interfaces afforeded by low-power and energy-harvesting materials
Use cases or design scenarios for sustainable interfaces and interactions
Eco driving
Sustainbale interaction with machines
Automation on sustaianablity
Computational sustainability
Computer vision for sustainability

Published Papers (7 papers)

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Research

18 pages, 3199 KiB

Open AccessArticle

Designing a Mobile Application for Working Memory Training through Understanding the Psychological and Physiological Characteristics of Older Adults

by Di Zhu, Yuchen Jing, Ruonan Huang, Yan Gao, Yue Liu, Zheng Zou and Wei Liu

Sustainability 2022, 14(21), 14152; https://doi.org/10.3390/su142114152 - 30 Oct 2022

Cited by 3 | Viewed by 1825

Abstract

Cognitive function declines with age, and when cognitive deterioration reaches a critical value and pathological changes occur, the brain neurons are irreversible. The aging of working memory even has profound adverse effects on older adults. This study aims to understand the psychological and physiological characteristics of older adults and to achieve mobile application design solutions that train working memory. According to the user study, the factors influencing the design of mobile applications for working memory training for older adults were mainly focused on six dimensions: training content, motivation, emotion, interaction, current state, and experience. Design opportunities were transformed, and seven new design strategies were obtained. Nine product functions with the highest priority were selected: daily practice, challenge mode, level-by-level difficulty selection, novice teaching, practice mode, sharing function, two-player mode, ranking, and desktop components. Finally, an interactive prototype was designed for usability testing, and the product solution was iterated based on expert evaluation and user feedback. The results indicate that the interface design provides a good user experience when applied daily. The process and results will be applied to make more solutions for training cognitive functions to be used in different situations. Full article

(This article belongs to the Special Issue Sustainable Human-Computer Interaction and Engineering)

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26 pages, 3999 KiB

Open AccessArticle

Hand Gesture Recognition Based on Auto-Landmark Localization and Reweighted Genetic Algorithm for Healthcare Muscle Activities

by Hira Ansar, Ahmad Jalal, Munkhjargal Gochoo and Kibum Kim

Sustainability 2021, 13(5), 2961; https://doi.org/10.3390/su13052961 - 09 Mar 2021

Cited by 40 | Viewed by 5131

Abstract

Due to the constantly increasing demand for the automatic localization of landmarks in hand gesture recognition, there is a need for a more sustainable, intelligent, and reliable system for hand gesture recognition. The main purpose of this study was to develop an accurate hand gesture recognition system that is capable of error-free auto-landmark localization of any gesture dateable in an RGB image. In this paper, we propose a system based on landmark extraction from RGB images regardless of the environment. The extraction of gestures is performed via two methods, namely, fused and directional image methods. The fused method produced greater extracted gesture recognition accuracy. In the proposed system, hand gesture recognition (HGR) is done via several different methods, namely, (1) HGR via point-based features, which consist of (i) distance features, (ii) angular features, and (iii) geometric features; (2) HGR via full hand features, which are composed of (i) SONG mesh geometry and (ii) active model. To optimize these features, we applied gray wolf optimization. After optimization, a reweighted genetic algorithm was used for classification and gesture recognition. Experimentation was performed on five challenging datasets: Sign Word, Dexter1, Dexter + Object, STB, and NYU. Experimental results proved that auto landmark localization with the proposed feature extraction technique is an efficient approach towards developing a robust HGR system. The classification results of the reweighted genetic algorithm were compared with Artificial Neural Network (ANN) and decision tree. The developed system plays a significant role in healthcare muscle exercise. Full article

(This article belongs to the Special Issue Sustainable Human-Computer Interaction and Engineering)

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28 pages, 9867 KiB

Open AccessArticle

HF-SPHR: Hybrid Features for Sustainable Physical Healthcare Pattern Recognition Using Deep Belief Networks

by Madiha Javeed, Munkhjargal Gochoo, Ahmad Jalal and Kibum Kim

Sustainability 2021, 13(4), 1699; https://doi.org/10.3390/su13041699 - 04 Feb 2021

Cited by 47 | Viewed by 2489

Abstract

The daily life-log routines of elderly individuals are susceptible to numerous complications in their physical healthcare patterns. Some of these complications can cause injuries, followed by extensive and expensive recovery stages. It is important to identify physical healthcare patterns that can describe and convey the exact state of an individual’s physical health while they perform their daily life activities. In this paper, we propose a novel Sustainable Physical Healthcare Pattern Recognition (SPHR) approach using a hybrid features model that is capable of distinguishing multiple physical activities based on a multiple wearable sensors system. Initially, we acquired raw data from well-known datasets, i.e., mobile health and human gait databases comprised of multiple human activities. The proposed strategy includes data pre-processing, hybrid feature detection, and feature-to-feature fusion and reduction, followed by codebook generation and classification, which can recognize sustainable physical healthcare patterns. Feature-to-feature fusion unites the cues from all of the sensors, and Gaussian mixture models are used for the codebook generation. For the classification, we recommend deep belief networks with restricted Boltzmann machines for five hidden layers. Finally, the results are compared with state-of-the-art techniques in order to demonstrate significant improvements in accuracy for physical healthcare pattern recognition. The experiments show that the proposed architecture attained improved accuracy rates for both datasets, and that it represents a significant sustainable physical healthcare pattern recognition (SPHR) approach. The anticipated system has potential for use in human–machine interaction domains such as continuous movement recognition, pattern-based surveillance, mobility assistance, and robot control systems. Full article

(This article belongs to the Special Issue Sustainable Human-Computer Interaction and Engineering)

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30 pages, 4474 KiB

Open AccessArticle

Modeling Two-Person Segmentation and Locomotion for Stereoscopic Action Identification: A Sustainable Video Surveillance System

by Nida Khalid, Munkhjargal Gochoo, Ahmad Jalal and Kibum Kim

Sustainability 2021, 13(2), 970; https://doi.org/10.3390/su13020970 - 19 Jan 2021

Cited by 46 | Viewed by 3191

Abstract

Due to the constantly increasing demand for automatic tracking and recognition systems, there is a need for more proficient, intelligent and sustainable human activity tracking. The main purpose of this study is to develop an accurate and sustainable human action tracking system that is capable of error-free identification of human movements irrespective of the environment in which those actions are performed. Therefore, in this paper we propose a stereoscopic Human Action Recognition (HAR) system based on the fusion of RGB (red, green, blue) and depth sensors. These sensors give an extra depth of information which enables the three-dimensional (3D) tracking of each and every movement performed by humans. Human actions are tracked according to four features, namely, (1) geodesic distance; (2) 3D Cartesian-plane features; (3) joints Motion Capture (MOCAP) features and (4) way-points trajectory generation. In order to represent these features in an optimized form, Particle Swarm Optimization (PSO) is applied. After optimization, a neuro-fuzzy classifier is used for classification and recognition. Extensive experimentation is performed on three challenging datasets: A Nanyang Technological University (NTU) RGB+D dataset; a UoL (University of Lincoln) 3D social activity dataset and a Collective Activity Dataset (CAD). Evaluation experiments on the proposed system proved that a fusion of vision sensors along with our unique features is an efficient approach towards developing a robust HAR system, having achieved a mean accuracy of 93.5% with the NTU RGB+D dataset, 92.2% with the UoL dataset and 89.6% with the Collective Activity dataset. The developed system can play a significant role in many computer vision-based applications, such as intelligent homes, offices and hospitals, and surveillance systems. Full article

(This article belongs to the Special Issue Sustainable Human-Computer Interaction and Engineering)

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21 pages, 4563 KiB

Open AccessArticle

Sustainable Wearable System: Human Behavior Modeling for Life-Logging Activities Using K-Ary Tree Hashing Classifier

by Ahmad Jalal, Mouazma Batool and Kibum Kim

Sustainability 2020, 12(24), 10324; https://doi.org/10.3390/su122410324 - 10 Dec 2020

Cited by 51 | Viewed by 2208

Abstract

Human behavior modeling (HBM) is a challenging classification task for researchers seeking to develop sustainable systems that precisely monitor and record human life-logs. In recent years, several models have been proposed; however, HBM remains an inspiring problem that is only partly solved. This paper proposes a novel framework of human behavior modeling based on wearable inertial sensors; the system framework is composed of data acquisition, feature extraction, optimization and classification stages. First, inertial data is filtered via three different filters, i.e., Chebyshev, Elliptic and Bessel filters. Next, six different features from time and frequency domains are extracted to determine the maximum optimal values. Then, the Probability Based Incremental Learning (PBIL) optimizer and the K-Ary tree hashing classifier are applied to model different human activities. The proposed model is evaluated on two benchmark datasets, namely DALIAC and PAMPA2, and one self-annotated dataset, namely, IM-LifeLog, respectively. For evaluation, we used a leave-one-out cross validation scheme. The experimental results show that our model outperformed existing state-of-the-art methods with accuracy rates of 94.23%, 94.07% and 96.40% over DALIAC, PAMPA2 and IM-LifeLog datasets, respectively. The proposed system can be used in healthcare, physical activity detection, surveillance systems and medical fitness fields. Full article

(This article belongs to the Special Issue Sustainable Human-Computer Interaction and Engineering)

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24 pages, 7113 KiB

Open AccessArticle

Human Posture Estimation and Sustainable Events Classification via Pseudo-2D Stick Model and K-ary Tree Hashing

by Ahmad Jalal, Israr Akhtar and Kibum Kim

Sustainability 2020, 12(23), 9814; https://doi.org/10.3390/su12239814 - 24 Nov 2020

Cited by 64 | Viewed by 2852

Abstract

This paper suggests that human pose estimation (HPE) and sustainable event classification (SEC) require an advanced human skeleton and context-aware features extraction approach along with machine learning classification methods to recognize daily events precisely. Over the last few decades, researchers have found new mechanisms to make HPE and SEC applicable in daily human life-log events such as sports, surveillance systems, human monitoring systems, and in the education sector. In this research article, we propose a novel HPE and SEC system for which we designed a pseudo-2D stick model. To extract full-body human silhouette features, we proposed various features such as energy, sine, distinct body parts movements, and a 3D Cartesian view of smoothing gradients features. Features extracted to represent human key posture points include rich 2D appearance, angular point, and multi-point autocorrelation. After the extraction of key points, we applied a hierarchical classification and optimization model via ray optimization and a K-ary tree hashing algorithm over a UCF50 dataset, an hmdb51 dataset, and an Olympic sports dataset. Human body key points detection accuracy for the UCF50 dataset was 80.9%, for the hmdb51 dataset it was 82.1%, and for the Olympic sports dataset it was 81.7%. Event classification for the UCF50 dataset was 90.48%, for the hmdb51 dataset it was 89.21%, and for the Olympic sports dataset it was 90.83%. These results indicate better performance for our approach compared to other state-of-the-art methods. Full article

(This article belongs to the Special Issue Sustainable Human-Computer Interaction and Engineering)

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14 pages, 2336 KiB

Open AccessArticle

The Impact of a Multitasking-Based Virtual Reality Motion Video Game on the Cognitive and Physical Abilities of Older Adults

by Xiaoxuan Li, Kavous Salehzadeh Niksirat, Shanshan Chen, Dongdong Weng, Sayan Sarcar and Xiangshi Ren

Sustainability 2020, 12(21), 9106; https://doi.org/10.3390/su12219106 - 02 Nov 2020

Cited by 18 | Viewed by 4546

Abstract

This study demonstrates how playing a well-designed multitasking motion video game in a virtual reality (VR) environment can positively impact the cognitive and physical health of older players. We developed a video game that combines cognitive and physical training in a VR environment. The impact of playing the game was measured through a four-week longitudinal experiment. Twenty healthy older adults were randomly assigned to either an intervention group (i.e., game training) or a control group (i.e., no contact). Participants played three 45-min sessions per week completing cognitive tests for attention, working memory, reasoning and a test for physical balance before and after the intervention. Results showed that compared to the control group, the game group showed significant improvements in working memory and a potential for enhancing reasoning and balance ability. Furthermore, while the older adults enjoyed playing the video game, ability enhancements were associated with their intrinsic motivation to play. Overall, cognitive training with multitasking VR motion video games has positive impacts on the cognitive and physical health of older adults. Full article

(This article belongs to the Special Issue Sustainable Human-Computer Interaction and Engineering)

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