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

Deadline for manuscript submissions: closed (28 October 2022) | Viewed by 17270
Please contact the Guest Editor or the Section Managing Editor at ([email protected]) for any queries.

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Special Issue Editors

Prof. Dr. Martin Hitz

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Guest Editor

Interactive Systems, Department of Informatics Systems, Alpen-Adria-Universität Klagenfurt, Austria
Interests: HCI, usability, and non-standard user interfaces, generally taking into account also mobile systems and pervasive interfaces.

Prof. Dr. Gerhard Leitner

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Guest Editor

Interactive Systems, Department of Informatics Systems, Alpen-Adria-Universität Klagenfurt, Austria
Interests: Human Computer Interaction (HCI). The main focus of his current research are current developments in HCI, such as mobile devices, the internet of things (IoT) and intelligent environments, specifically smart homes. The research addresses the relevance of new forms of human machine interaction to support specific needs (e.g. comfort, saving, caregiving), interaction modalities (gesture, speech, peripheral (“calm”)) and specific user groups (e.g. elderly, children).

Special Issue Information

Dear Colleagues,

Smart homes have been “sold” for decades as successful models of the future. The predicted success has, so far, materialized on one level – the technology: There are hundreds of different smart home systems in different areas, such as industrial buildings, public buildings, and private households. However, the naïve smart home user is increasingly being confronted with the difficulties in keeping track of the functionality and operation of the available systems; this could be considered as a new form of “not being able to see the forest for the trees”. This Special Issue deals with the problems, challenges, and potentials of the field of smart homes from the perspective of human–computer interaction and spans the spectrum from social science questions of acceptance and value systems to classical questions of system operation (usability) and the application of artificial intelligence (configuration, recommendation systems).

Martin Hitz
Gerhard Leitner
Guest Editors

Manuscript Submission Information

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Keywords

Possible topics for submitted papers include (but are not limited to):

User Experience in Smart Homes
Technology Acceptance of Smart Home Systems: Acceptance models tailored to smart homes; Empirical studies
Societal Impact of Smart Home Systems: Critical aspects such as danger of surveillance, danger of dependency; Sustainability aspects: Saving resources with smart home systems (e.g., energy consumption monitoring and control) vs. wasting energy by operating smart home systems; Safety and security in smart homes
Control Interface Issues: Conversational interfaces; Gesture-based interfaces; GUI/mobile device-based interfaces; Usability of the control interface
Static Configuration Interface Issues: Flexibility of configuration; Self explanation features; Usability of the configuration environment
Dynamic Configuration Interface Issues (Rule Definition): Expressive power/logical structure of definition language; Usability of the configuration environment; Self-configuration ability (adaptive smart home/ritual-based interaction)

Published Papers (5 papers)

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Research

52 pages, 7700 KiB

Open AccessArticle

The PBC Model: Supporting Positive Behaviours in Smart Environments

by Oluwande Adewoyin, Janet Wesson and Dieter Vogts

Sensors 2022, 22(24), 9626; https://0-doi-org.brum.beds.ac.uk/10.3390/s22249626 - 08 Dec 2022

Cited by 1 | Viewed by 1685

Abstract

Several behavioural problems exist in office environments, including resource use, sedentary behaviour, cognitive/multitasking, and social media. These behavioural problems have been solved through subjective or objective techniques. Within objective techniques, behavioural modelling in smart environments (SEs) can allow the adequate provision of services to users of SEs with inputs from user modelling. The effectiveness of current behavioural models relative to user-specific preferences is unclear. This study introduces a new approach to behavioural modelling in smart environments by illustrating how human behaviours can be effectively modelled from user models in SEs. To achieve this aim, a new behavioural model, the Positive Behaviour Change (PBC) Model, was developed and evaluated based on the guidelines from the Design Science Research Methodology. The PBC Model emphasises the importance of using user-specific information within the user model for behavioural modelling. The PBC model comprised the SE, the user model, the behaviour model, classification, and intervention components. The model was evaluated using a naturalistic-summative evaluation through experimentation using office workers. The study contributed to the knowledge base of behavioural modelling by providing a new dimension to behavioural modelling by incorporating the user model. The results from the experiment revealed that behavioural patterns could be extracted from user models, behaviours can be classified and quantified, and changes can be detected in behaviours, which will aid the proper identification of the intervention to provide for users with or without behavioural problems in smart environments. Full article

(This article belongs to the Special Issue Human–Smarthome Interaction)

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20 pages, 2620 KiB

Open AccessArticle

The Investigation of Adoption of Voice-User Interface (VUI) in Smart Home Systems among Chinese Older Adults

by Yao Song, Yanpu Yang and Peiyao Cheng

Sensors 2022, 22(4), 1614; https://0-doi-org.brum.beds.ac.uk/10.3390/s22041614 - 18 Feb 2022

Cited by 25 | Viewed by 4641

Abstract

Driven by advanced voice interaction technology, the voice-user interface (VUI) has gained popularity in recent years. VUI has been integrated into various devices in the context of the smart home system. In comparison with traditional interaction methods, VUI provides multiple benefits. VUI allows for hands-free and eyes-free interaction. It also enables users to perform multiple tasks while interacting. Moreover, as VUI is highly similar to a natural conversation in daily lives, it is intuitive to learn. The advantages provided by VUI are particularly beneficial to older adults, who suffer from decreases in physical and cognitive abilities, which hinder their interaction with electronic devices through traditional methods. However, the factors that influence older adults’ adoption of VUI remain unknown. This study addresses this research gap by proposing a conceptual model. On the basis of the technology adoption model (TAM) and the senior technology adoption model (STAM), this study considers the characteristic of VUI and the characteristic of older adults through incorporating the construct of trust and aging-related characteristics (i.e., perceived physical conditions, mobile self-efficacy, technology anxiety, self-actualization). A survey was designed and conducted. A total of 420 Chinese older adults participated in this survey, and they were current or potential users of VUI. Through structural equation modeling, data were analyzed. Results showed a good fit with the proposed conceptual model. Path analysis revealed that three factors determine Chinese older adults’ adoption of VUI: perceived usefulness, perceived ease of use, and trust. Aging-related characteristics also influence older adults’ adoption of VUI, but they are mediated by perceived usefulness, perceived ease of use, and trust. Specifically, mobile self-efficacy is demonstrated to positively influence trust and perceived ease of use but negatively influence perceived usefulness. Self-actualization exhibits positive influences on perceived usefulness and perceived ease of use. Technology anxiety only exerts influence on perceived ease of use in a marginal way. No significant influences of perceived physical conditions were found. This study extends the TAM and STAM by incorporating additional variables to explain Chinese older adults’ adoption of VUI. These results also provide valuable implications for developing suitable VUI for older adults as well as planning actionable communication strategies for promoting VUI among Chinese older adults. Full article

(This article belongs to the Special Issue Human–Smarthome Interaction)

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20 pages, 694 KiB

Open AccessArticle

Ageing Safely in the Digital Era: A New Unobtrusive Activity Monitoring Framework Leveraging on Daily Interactions with Hand-Operated Appliances

by Hafsa Bousbiat, Gerhard Leitner and Wilfried Elmenreich

Sensors 2022, 22(4), 1322; https://0-doi-org.brum.beds.ac.uk/10.3390/s22041322 - 09 Feb 2022

Cited by 6 | Viewed by 1853

Abstract

Supporting the elderly to maintain their independence, safety, and well-being through Active Assisted Living (AAL) technologies, is gaining increasing momentum. Recently, Non-intrusive Load Monitoring (NILM) approaches have become the focus of these technologies due to their non-intrusiveness and reduced price. Whilst some research has been carried out in this respect; it still is challenging to design systems considering the heterogeneity and complexity of daily routines. Furthermore, scholars gave little attention to evaluating recent deep NILM models in AAL applications. We suggest a new interactive framework for activity monitoring based on custom user-profiles and deep NILM models to address these gaps. During evaluation, we consider four different deep NILM models. The proposed contribution is further assessed on two households from the REFIT dataset for a period of one year, including the influence of NILM on activity monitoring. To the best of our knowledge, the current study is the first to quantify the error propagated by a NILM model on the performance of an AAL solution. The results achieved are promising, particularly when considering the UNET-NILM model, a multi-task convolutional neural network for load disaggregation, that revealed a deterioration of only 10% in the f1-measure of the framework’s overall performance. Full article

(This article belongs to the Special Issue Human–Smarthome Interaction)

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13 pages, 1011 KiB

Open AccessArticle

Who Is to Blame? The Appearance of Virtual Agents and the Attribution of Perceived Responsibility

by Tetsuya Matsui and Atsushi Koike

Sensors 2021, 21(8), 2646; https://0-doi-org.brum.beds.ac.uk/10.3390/s21082646 - 09 Apr 2021

Cited by 3 | Viewed by 2241

Abstract

Virtual agents have been widely used in human-agent collaboration work. One important problem with human-agent collaboration is the attribution of responsibility as perceived by users. We focused on the relationship between the appearance of a virtual agent and the attribution of perceived responsibility. We conducted an experiment with five agents: an agent without an appearance, a human-like agent, a robot-like agent, a dog-like agent, and an angel-like agent. We measured the perceived agency and experience for each agent, and we conducted an experiment involving a sound-guessing game. In the game, participants listened to a sound and guessed what the sound was with an agent. At the end of the game, the game finished with failure, and the participants did not know who made the mistake, the participant or the agent. After the game, we asked the participants how they perceived the agents’ trustworthiness and to whom they attributed responsibility. As a result, participants attributed less responsibility to themselves when interacting with a robot-like agent than interacting with an angel-like robot. Furthermore, participants perceived the least trustworthiness toward the robot-like agent among all conditions. In addition, the agents’ perceived experience had a correlation with the attribution of perceived responsibility. Furthermore, the agents that made the participants feel their attribution of responsibility to be less were not trusted. These results suggest the relationship between agents’ appearance and perceived attribution of responsibility and new methods for designs in the creation of virtual agents for collaboration work. Full article

(This article belongs to the Special Issue Human–Smarthome Interaction)

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22 pages, 830 KiB

Open AccessArticle

What Influences the Perceived Trust of a Voice-Enabled Smart Home System: An Empirical Study

by Yuqi Liu, Yan Gan, Yao Song and Jing Liu

Sensors 2021, 21(6), 2037; https://0-doi-org.brum.beds.ac.uk/10.3390/s21062037 - 13 Mar 2021

Cited by 20 | Viewed by 5395

Abstract

Contemporarily, almost all the global IT giants have aimed at the smart home industry and made an active strategic business layout. As the early-stage and entry-level product of the voice-enabled smart home industry, the smart speakers have been going through rapid development and rising fierce market competition globally in recent years. China, one of the most populous and largest markets in the world, has tremendous business potential in the smart home industry. The market sales of smart speakers in China have gone through rapid growth in the past three years. However, the market penetration rate of related smart home devices and equipment still stays extremely low and far from mass adoption. Moreover, the market sales of smart speakers have also entered a significant slowdown and adjustment period since 2020. Chinese consumers have moved from early impulsive consumption to a rational consumption phase about this early-stage smart home product. Trust in the marketing field is considered an indispensable component of all business transactions, which plays a crucial role in adopting new technologies. This study explores the influencing factors of Chinese users’ perceived trust in the voice-enabled smart home systems, uses structural equation modeling (SEM) to analyze the interaction mechanism between different variables, and establishes a perceived trust model through 475 valid samples. The model includes six variables: system quality, familiarity, subjective norm, technology optimism, perceived enjoyment, and perceived trust. The result shows that system quality is the essential influence factor that impacts all other variables and could significantly affect the perceived trust. Perceived enjoyment is the most direct influence variable affected by system quality, subjective norm, and technology optimism, and it positively affects the perceived trust in the end. The subjective norm is one of the most distinguishing variables for Chinese users, since China has a collectivist consumption culture. People always expect their behavior to meet social expectations and standards to avoid criticism and acquire social integration. Therefore, policy guidance, authoritative opinions, and people with important reference roles will significantly affect consumers’ perceived trust and purchase intention. Familiarity and technology optimism are important influential factors that will have an indirect impact on the perceived trust. The related results of this study can help designers, practitioners, and researchers of the smart home industry produce products and services with higher perceived trust to improve consumers’ adoption and acceptance so that the market penetration rate of related products and enterprises could be increased, and the maturity and development of the voice-enabled smart home industry could be promoted. Full article

(This article belongs to the Special Issue Human–Smarthome Interaction)

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