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1 – 10 of over 1000
Article
Publication date: 18 January 2024

Janarthanan Balakrishnan, Yogesh K. Dwivedi, Anubhav Mishra, F. Tegwen Malik and Mihalis Giannakis

Given the growth of virtual reality (VR)-based tourism experiences in the past five years, this study aims to investigate the impact of VR-based interactions (ergonomics and…

Abstract

Purpose

Given the growth of virtual reality (VR)-based tourism experiences in the past five years, this study aims to investigate the impact of VR-based interactions (ergonomics and embodiment) on memorable experiences and revisit intention mediated by cognitive and emotional responses.

Design/methodology/approach

This study has used an exploratory sequential mixed methodology research design to operationalise this research. Study 1 uses qualitative in-depth interviews to explore the proposed research questions, and Study 2 uses a 3 × 3 factorial experimental research design to test the proposed hypothetical model with 355 samples.

Findings

The results indicate that embodiment plays a more crucial role than VR ergonomics. Also, the cognitive response in the virtual tour indirectly generates a more memorable experience than the emotional response.

Research limitations/implications

This research uses the theory of technological mediation as an overarching framework to conceptualise the research. Also, the research has applied the tenets of cognitive embodiment theory, metacognitive theory and other related theories to develop the arguments. Thus, the results of this research will extend the holistic understanding of these theories.

Practical implications

This research will guide VR tourism developers in understanding the requirements and expectations of tourists. It also serves as a manual to understand how tourists process the VR tour psychologically.

Originality/value

Very minimal focus was given to understanding the tourists’ interaction with technology in VR tours. The concept of ergonomics and embodiment investigated as an experimental variable is a novel approach in technology-based tourism research.

Details

International Journal of Contemporary Hospitality Management, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 0959-6119

Keywords

Article
Publication date: 11 July 2023

Abhinandan Chatterjee, Pradip Bala, Shruti Gedam, Sanchita Paul and Nishant Goyal

Depression is a mental health problem characterized by a persistent sense of sadness and loss of interest. EEG signals are regarded as the most appropriate instruments for…

Abstract

Purpose

Depression is a mental health problem characterized by a persistent sense of sadness and loss of interest. EEG signals are regarded as the most appropriate instruments for diagnosing depression because they reflect the operating status of the human brain. The purpose of this study is the early detection of depression among people using EEG signals.

Design/methodology/approach

(i) Artifacts are removed by filtering and linear and non-linear features are extracted; (ii) feature scaling is done using a standard scalar while principal component analysis (PCA) is used for feature reduction; (iii) the linear, non-linear and combination of both (only for those whose accuracy is highest) are taken for further analysis where some ML and DL classifiers are applied for the classification of depression; and (iv) in this study, total 15 distinct ML and DL methods, including KNN, SVM, bagging SVM, RF, GB, Extreme Gradient Boosting, MNB, Adaboost, Bagging RF, BootAgg, Gaussian NB, RNN, 1DCNN, RBFNN and LSTM, that have been effectively utilized as classifiers to handle a variety of real-world issues.

Findings

1. Among all, alpha, alpha asymmetry, gamma and gamma asymmetry give the best results in linear features, while RWE, DFA, CD and AE give the best results in non-linear feature. 2. In the linear features, gamma and alpha asymmetry have given 99.98% accuracy for Bagging RF, while gamma asymmetry has given 99.98% accuracy for BootAgg. 3. For non-linear features, it has been shown 99.84% of accuracy for RWE and DFA in RF, 99.97% accuracy for DFA in XGBoost and 99.94% accuracy for RWE in BootAgg. 4. By using DL, in linear features, gamma asymmetry has given more than 96% accuracy in RNN and 91% accuracy in LSTM and for non-linear features, 89% accuracy has been achieved for CD and AE in LSTM. 5. By combining linear and non-linear features, the highest accuracy was achieved in Bagging RF (98.50%) gamma asymmetry + RWE. In DL, Alpha + RWE, Gamma asymmetry + CD and gamma asymmetry + RWE have achieved 98% accuracy in LSTM.

Originality/value

A novel dataset was collected from the Central Institute of Psychiatry (CIP), Ranchi which was recorded using a 128-channels whereas major previous studies used fewer channels; the details of the study participants are summarized and a model is developed for statistical analysis using N-way ANOVA; artifacts are removed by high and low pass filtering of epoch data followed by re-referencing and independent component analysis for noise removal; linear features, namely, band power and interhemispheric asymmetry and non-linear features, namely, relative wavelet energy, wavelet entropy, Approximate entropy, sample entropy, detrended fluctuation analysis and correlation dimension are extracted; this model utilizes Epoch (213,072) for 5 s EEG data, which allows the model to train for longer, thereby increasing the efficiency of classifiers. Features scaling is done using a standard scalar rather than normalization because it helps increase the accuracy of the models (especially for deep learning algorithms) while PCA is used for feature reduction; the linear, non-linear and combination of both features are taken for extensive analysis in conjunction with ML and DL classifiers for the classification of depression. The combination of linear and non-linear features (only for those whose accuracy is highest) is used for the best detection results.

Details

Aslib Journal of Information Management, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 2050-3806

Keywords

Article
Publication date: 23 November 2023

Yanan Wang, Lee Yen Chaw, Choi-Meng Leong, Yet Mee Lim and Abdulkadir Barut

This study intends to investigate the determinants of learners' continuance intention to use massive open online courses (MOOCs) for personal or professional development.

Abstract

Purpose

This study intends to investigate the determinants of learners' continuance intention to use massive open online courses (MOOCs) for personal or professional development.

Design/methodology/approach

This study employed quantitative research design. The respondents were individual learners from six selected universities in China who used MOOCs for continuous learning. A purposive sampling technique was employed to obtain 270 valid samples. Data were analyzed and analytical outputs were produced using the techniques of Partial Least Squares Structural Equation Modeling and Importance-Performance Matrix.

Findings

Expectation confirmation was found to have a positive relationship with perceived usefulness, flow experience, learning self-efficacy and satisfaction with MOOCs. Perceived usefulness, flow experience and leaning self-efficacy were also found to have a positive relationship with MOOC satisfaction. In addition, perceived usefulness, flow experience, learning self-efficacy and MOOC satisfaction had a positive impact on continuance usage intention.

Originality/value

The outcomes of the study can serve as a practical reference for MOOC providers and decision-makers to develop relevant strategies to increase the course completion rates.

Details

International Journal of Educational Management, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 0951-354X

Keywords

Article
Publication date: 21 December 2023

Freya Rumball, Rachel Parker, Ailbhe Elizabeth Madigan, Francesca Happe and Debbie Spain

Autistic individuals are at increased risk of trauma exposure and post-traumatic stress disorder (PTSD). Diagnostic overshadowing, however, often results in PTSD symptoms being…

Abstract

Purpose

Autistic individuals are at increased risk of trauma exposure and post-traumatic stress disorder (PTSD). Diagnostic overshadowing, however, often results in PTSD symptoms being mislabelled as autistic traits. This study aims to develop professional consensus on the identification and assessment of co-occurring PTSD in autistic adults.

Design/methodology/approach

An online modified Delphi design was used to gather professionals’ perspectives on key aspects of the identification and assessment of PTSD in autistic adults. Data were gathered qualitatively in Round 1 and then synthesised using content analysis into a list of statements that were rated in Round 2. Statements reaching 60–79% consensus and additional suggestions were sent out for rating in Round 3. Consensus for the final statement list was set at 80% agreement.

Findings

Overall, 108 statements reached consensus. These form the basis of professional-informed recommendations to facilitate the identification and assessment of PTSD symptoms in autistic adults.

Practical implications

The final Delphi statements provide a framework to assist with the assessment and recognition of traumatic stress reactions in autistic adults presenting to mental health, diagnostic or social services.

Originality/value

To the best of the authors’ knowledge, this is the first study to explore the presentation and identification of PTSD in autistic adults (with and without intellectual disability), using a bottom-up approach informed by professional consensus.

Details

Advances in Autism, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 2056-3868

Keywords

Open Access
Article
Publication date: 22 August 2023

Mahesh Babu Purushothaman and Kasun Moolika Gedara

This pragmatic research paper aims to unravel the smart vision-based method (SVBM), an AI program to correlate the computer vision (recorded and live videos using mobile and…

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Abstract

Purpose

This pragmatic research paper aims to unravel the smart vision-based method (SVBM), an AI program to correlate the computer vision (recorded and live videos using mobile and embedded cameras) that aids in manual lifting human pose deduction, analysis and training in the construction sector.

Design/methodology/approach

Using a pragmatic approach combined with the literature review, this study discusses the SVBM. The research method includes a literature review followed by a pragmatic approach and lab validation of the acquired data. Adopting the practical approach, the authors of this article developed an SVBM, an AI program to correlate computer vision (recorded and live videos using mobile and embedded cameras).

Findings

Results show that SVBM observes the relevant events without additional attachments to the human body and compares them with the standard axis to identify abnormal postures using mobile and other cameras. Angles of critical nodal points are projected through human pose detection and calculating body part movement angles using a novel software program and mobile application. The SVBM demonstrates its ability to data capture and analysis in real-time and offline using videos recorded earlier and is validated for program coding and results repeatability.

Research limitations/implications

Literature review methodology limitations include not keeping in phase with the most updated field knowledge. This limitation is offset by choosing the range for literature review within the last two decades. This literature review may not have captured all published articles because the restriction of database access and search was based only on English. Also, the authors may have omitted fruitful articles hiding in a less popular journal. These limitations are acknowledged. The critical limitation is that the trust, privacy and psychological issues are not addressed in SVBM, which is recognised. However, the benefits of SVBM naturally offset this limitation to being adopted practically.

Practical implications

The theoretical and practical implications include customised and individualistic prediction and preventing most posture-related hazardous behaviours before a critical injury happens. The theoretical implications include mimicking the human pose and lab-based analysis without attaching sensors that naturally alter the working poses. SVBM would help researchers develop more accurate data and theoretical models close to actuals.

Social implications

By using SVBM, the possibility of early deduction and prevention of musculoskeletal disorders is high; the social implications include the benefits of being a healthier society and health concerned construction sector.

Originality/value

Human pose detection, especially joint angle calculation in a work environment, is crucial to early deduction of muscoloskeletal disorders. Conventional digital technology-based methods to detect pose flaws focus on location information from wearables and laboratory-controlled motion sensors. For the first time, this paper presents novel computer vision (recorded and live videos using mobile and embedded cameras) and digital image-related deep learning methods without attachment to the human body for manual handling pose deduction and analysis of angles, neckline and torso line in an actual construction work environment.

Details

Smart and Sustainable Built Environment, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 2046-6099

Keywords

Article
Publication date: 8 May 2023

Yumeng Hou, Fadel Mamar Seydou and Sarah Kenderdine

Despite being an authentic carrier of various cultural practices, the human body is often underutilised to access the knowledge of human body. Digital inventions today have…

Abstract

Purpose

Despite being an authentic carrier of various cultural practices, the human body is often underutilised to access the knowledge of human body. Digital inventions today have created new avenues to open up cultural data resources, yet mainly as apparatuses for well-annotated and object-based collections. Hence, there is a pressing need for empowering the representation of intangible expressions, particularly embodied knowledge within its cultural context. To address this issue, the authors propose to inspect the potential of machine learning methods to enhance archival knowledge interaction with intangible cultural heritage (ICH) materials.

Design/methodology/approach

This research adopts a novel approach by combining movement computing with knowledge-specific modelling to support retrieving through embodied cues, which is applied to a multimodal archive documenting the cultural heritage (CH) of Southern Chinese martial arts.

Findings

Through experimenting with a retrieval engine implemented using the Hong Kong Martial Arts Living Archive (HKMALA) datasets, this work validated the effectiveness of the developed approach in multimodal content retrieval and highlighted the potential for the multimodal's application in facilitating archival exploration and knowledge discoverability.

Originality/value

This work takes a knowledge-specific approach to invent an intelligent encoding approach through a deep-learning workflow. This article underlines that the convergence of algorithmic reckoning and content-centred design holds promise for transforming the paradigm of archival interaction, thereby augmenting knowledge transmission via more accessible CH materials.

Details

Journal of Documentation, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 0022-0418

Keywords

Article
Publication date: 31 May 2022

Samridhi Garg, Monica Puri Sikka and Vinay Kumar Midha

Perspiration and heat are produced by the body and must be eliminated to maintain a stable body temperature. Sweat, heat and air must pass through the fabric to be comfortable…

Abstract

Purpose

Perspiration and heat are produced by the body and must be eliminated to maintain a stable body temperature. Sweat, heat and air must pass through the fabric to be comfortable. The cloth absorbs sweat and then releases it, allowing the body to chill down. By capillary action, moisture is driven away from fabric pores or sucked out of yarns. Convectional air movement improves sweat drainage, which may aid in body temperature reduction. Clothing reduces the skin's ability to transport heat and moisture to the outside. Excessive moisture makes clothing stick to the skin, whereas excessive heat induces heat stress, making the user uncomfortable. Wet heat loss is significantly more difficult to understand than dry heat loss. The purpose of this study is to provided a good compilation of complete information on wet thermal comfort of textile and technological elements to be consider while constructing protective apparel.

Design/methodology/approach

This paper aims to critically review studies on the thermal comfort of textiles in wet conditions and assess the results to guide future research.

Findings

Several recent studies focused on wet textiles' impact on comfort. Moisture reduces the fabric's thermal insulation value while also altering its moisture characteristics. Moisture and heat conductivity were linked. Sweat and other factors impact fabric comfort. So, while evaluating a fabric's comfort, consider both external and inside moisture.

Originality/value

The systematic literature review in this research focuses on wet thermal comfort and technological elements to consider while constructing protective apparel.

Details

Research Journal of Textile and Apparel, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 1560-6074

Keywords

Article
Publication date: 20 March 2024

Mark Yi-Cheon Yim, Eunice (Eun-Sil) Kim and Hongmin Ahn

In keeping with recent body image social trends, consumer demand for the adoption of plus-size models is increasing, although the use of thin models remains prevalent. The current…

Abstract

Purpose

In keeping with recent body image social trends, consumer demand for the adoption of plus-size models is increasing, although the use of thin models remains prevalent. The current study explores how consumers process information about fashion products displayed on different sizes of models in advertisements, focusing on model and consumer body sizes and both genders. As an underlying mechanism explaining how the relationship between model and consumer body sizes shapes consumer purchase intention, this study explores the role of guilt, shame and mental imagery.

Design/methodology/approach

The current study uses a text analytics technique to identify female consumers' general opinions of thin models in advertising. Employing a 3 (consumer body size: normal, overweight, obese) × 2 (model body size: thin, plus-size) × 2 (gender: male, female) between-subjects online experiment (n = 718), the main study comparatively analyzes the influences of plus-size and thin models on consumer responses.

Findings

The results reveal that, despite body positivity movements, thin models still generate negative emotions among female consumers. For obese female consumers, advertisements featuring plus-size models produce fewer negative emotions but not more mental imagery than advertisements featuring thin models. Conversely, for obese male consumers, advertisements featuring plus-size models generate more mental imagery but not more negative emotions than advertisements featuring thin models. The results also reveal that the relationship between consumer body size and guilt is moderated by perceived model size, which is also moderated by gender in generating mental imagery. While guilt plays a mediating role in enhancing mental imagery, resulting in purchase intention, shame does not take on this role.

Originality/value

This study is the first to present an integrated model that elucidates how consumers with varying body sizes respond to different sizes of models in advertising and how these responses impact purchase intentions.

Research limitations/implications

Our findings only apply to contexts where consumers purchase fashion clothing in response to advertisements featuring thin versus plus-size models.

Practical implications

Exposing normal-size consumers to plus-size models generates less mental imagery, and thus, practitioners should seek to match the body sizes of the models featured in advertising to the body sizes of their target audience or ad campaigns that include both plus-size and thin models may help improve message persuasiveness in fashion advertising. Moreover, guilt-appeal advertising campaigns using thin models would appeal more to thin consumers of both genders than shame-appeal advertising.

Details

Journal of Fashion Marketing and Management: An International Journal, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 1361-2026

Keywords

Article
Publication date: 9 January 2024

Niloofar Solhjoo, Maja Krtalić and Anne Goulding

While exploring the information experience within multispecies families, the subjective nature of humans and non-human entities, living beings and non-living objects becomes…

Abstract

Purpose

While exploring the information experience within multispecies families, the subjective nature of humans and non-human entities, living beings and non-living objects becomes evident. This paper aims to reveal the underlying significance of information within socio-physical living environments shared among humans, cats and dogs as companions.

Design/methodology/approach

Gaining inspiration from the information experience approach and posthumanism, this is a phenomenological paper. Empirical material related to lived experiences of participating families were gathered through multispecies ethnography methods, followed by phenomenological reflections. The paper has been written based on excerpt-commentary-units and the inclusion of videos and images as an approach to convey the richness of the lived experiences and multiple perspectives.

Findings

Findings are organised into three main sections, each capturing lived experiences of information and its utilization from various frames. The paper shows how living beings, both human and animal, use their physical, sensual and moving bodies to acquire and convey information to and from each other. Moving beyond the living beings, the study discusses how non-living objects in the physical environment of a multispecies family also shape information. Material objects, spatial locations and even plants became sources of information for the family members. Lastly, the paper delves into the social environment of the family, where all members, human and animal, are actively shaped by information within their social interactions and companionship.

Originality/value

Considering information distributed across species and material objects in a shared, more-than-human environment, the article suggests implications for an information experience approach. It emphasizes how information shapes the in-between humans, animals and their environment, highlighting their reliance on each other for understanding and living a good shared life. There is a need for future research to explore the information experience within the internal subjective minds of members of multispecies families, bridging the gap in the understanding of these external information and their internal information processes.

Details

Journal of Documentation, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 0022-0418

Keywords

Article
Publication date: 9 February 2024

Xiaoqing Zhang, Genliang Xiong, Peng Yin, Yanfeng Gao and Yan Feng

To ensure the motion attitude and stable contact force of massage robot working on unknown human tissue environment, this study aims to propose a robotic system for autonomous…

Abstract

Purpose

To ensure the motion attitude and stable contact force of massage robot working on unknown human tissue environment, this study aims to propose a robotic system for autonomous massage path planning and stable interaction control.

Design/methodology/approach

First, back region extraction and acupoint recognition based on deep learning is proposed, which provides a basis for determining the working area and path points of the robot. Second, to realize the standard approach and movement trajectory of the expert massage, 3D reconstruction and path planning of the massage area are performed, and normal vectors are calculated to control the normal orientation of robot-end. Finally, to cope with the soft and hard changes of human tissue state and body movement, an adaptive force tracking control strategy is presented to compensate the uncertainty of environmental position and tissue hardness online.

Findings

Improved network model can accomplish the acupoint recognition task with a large accuracy and integrate the point cloud to generate massage trajectories adapted to the shape of the human body. Experimental results show that the adaptive force tracking control can obtain a relatively smooth force, and the error is basically within ± 0.2 N during the online experiment.

Originality/value

This paper incorporates deep learning, 3D reconstruction and impedance control, the robot can understand the shape features of the massage area and adapt its planning massage path to carry out a stable and safe force tracking control during dynamic robot–human contact.

Details

Industrial Robot: the international journal of robotics research and application, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 0143-991X

Keywords

1 – 10 of over 1000