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Topical Collection "Intelligent Sensors and Sensing Spaces for Smart Home and Environment"

A topical collection in Sensors (ISSN 1424-8220). This collection belongs to the section "Intelligent Sensors".

Editors

Prof. Dr. Mi Jeong Kim
E-Mail Website
Collection Editor
School of Architecture, Hanyang University, Seoul 04763, Korea
Interests: sensing architecture; design computing and cognition; human-computer interaction
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Han Jong Jun
E-Mail Website
Collection Editor
School of Architecture, Hanyang University, Seoul 04763, Republic of Korea
Interests: Affective computing; building information modeling; sensing architecture
Special Issues, Collections and Topics in MDPI journals

Topical Collection Information

Dear Colleagues,

Considerable research has been conducted on smart homes and environments in the domains of architecture, engineering and construction. Many studies on smart homes and environments have dealt with monitoring occupants’ behaviors and health to provide smart services allowing a comfortable life and independence of the occupants. Through combining intelligent sensors as part of the environment components, our living spaces have become interactive sensing spaces, affecting the experience of occupants.

We are currently facing a big challenge due to COVID-19 pandemic. Many experts are predicting that overall lifestyle and spatial experience will be changed, calling for new working, living styles and sustainable environments. Smart home equipped with sensors could be a center of urban activities and the sensing spaces would be one of the important research areas towards the realization of smart environments from a cognitive perspective.

The aim of this Special Issue is to contribute to the state-of-the-art, and to introduce the current developments focusing on the sensor technologies and sensing spaces contributing to smart homes and environments. We encourage submissions that apply various methodological approaches in either original research, present systematic reviews, theoretical models or new developments. We also welcome experimental works and envision of possible scenarios for smart environments on a strong theoretical foundation.

Prof. Dr. Mi Jeong Kim
Prof. Dr. Han Jong Jun
Collection 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 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. 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 2200 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

  • smart home
  • smart environment
  • intelligent sensor
  • sensing architecture
  • affective computing
  • human–computer interaction
  • building information modeling

Published Papers (10 papers)

2021

Article
Smart Tree: An Architectural, Greening and ICT Multidisciplinary Approach to Smart Campus Environments
Sensors 2021, 21(21), 7202; https://doi.org/10.3390/s21217202 - 29 Oct 2021
Viewed by 378
Abstract
At present, climate change, pollution, and uncontrolled urbanism threaten not only natural ecosystems, but also the urban environment. Approaches to mitigate these challenges and able to provide an alternative for the use of the space are deemed to be multidisciplinary, combining architecture, vegetation [...] Read more.
At present, climate change, pollution, and uncontrolled urbanism threaten not only natural ecosystems, but also the urban environment. Approaches to mitigate these challenges and able to provide an alternative for the use of the space are deemed to be multidisciplinary, combining architecture, vegetation integration, circular economy and information and communications technologies (ICT). University campuses are a key scenario to evaluate such solutions as their student and research community is intrinsically willing to support these experiences and provide a wide knowledge on the fields necessary for their design and implementation. However, the creation of areas combining usability and sustainability is commonly lacking a multidisciplinary approach combining all these different perspectives. Hence, the present work aims to overcome this limitation by the development of a novel integrated approach for campus spaces for co-working and leisure, namely a “Smart Tree”, where novel architecture, furniture design, flora integration, environmental sensoring and communications join together. To this end, a survey of the literature is provided, covering related approaches as well as general principles behind them. From this, the general requirements and constraints for the development of the Smart Tree area are identified, establishing the main interactions between the architecture, greening and ICT perspectives. Such requirements guide the proposed system design and implementation, whose impact on the environment is analyzed. Finally, the research challenges and lessons learned for their development are identified in order to support future works. Full article
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Article
Schizophrenia Detection Using Machine Learning Approach from Social Media Content
Sensors 2021, 21(17), 5924; https://doi.org/10.3390/s21175924 - 03 Sep 2021
Viewed by 552
Abstract
Schizophrenia is a severe mental disorder that ranks among the leading causes of disability worldwide. However, many cases of schizophrenia remain untreated due to failure to diagnose, self-denial, and social stigma. With the advent of social media, individuals suffering from schizophrenia share their [...] Read more.
Schizophrenia is a severe mental disorder that ranks among the leading causes of disability worldwide. However, many cases of schizophrenia remain untreated due to failure to diagnose, self-denial, and social stigma. With the advent of social media, individuals suffering from schizophrenia share their mental health problems and seek support and treatment options. Machine learning approaches are increasingly used for detecting schizophrenia from social media posts. This study aims to determine whether machine learning could be effectively used to detect signs of schizophrenia in social media users by analyzing their social media texts. To this end, we collected posts from the social media platform Reddit focusing on schizophrenia, along with non-mental health related posts (fitness, jokes, meditation, parenting, relationships, and teaching) for the control group. We extracted linguistic features and content topics from the posts. Using supervised machine learning, we classified posts belonging to schizophrenia and interpreted important features to identify linguistic markers of schizophrenia. We applied unsupervised clustering to the features to uncover a coherent semantic representation of words in schizophrenia. We identified significant differences in linguistic features and topics including increased use of third person plural pronouns and negative emotion words and symptom-related topics. We distinguished schizophrenic from control posts with an accuracy of 96%. Finally, we found that coherent semantic groups of words were the key to detecting schizophrenia. Our findings suggest that machine learning approaches could help us understand the linguistic characteristics of schizophrenia and identify schizophrenia or otherwise at-risk individuals using social media texts. Full article
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Review
Generative Design in Building Information Modelling (BIM): Approaches and Requirements
Sensors 2021, 21(16), 5439; https://doi.org/10.3390/s21165439 - 12 Aug 2021
Cited by 1 | Viewed by 721
Abstract
The integration of generative design (GD) and building information modelling (BIM), as a new technology consolidation, can facilitate the constructability of GD’s automatic design solutions, while improving BIM’s capability in the early design phase. Thus, there has been an increasing interest to study [...] Read more.
The integration of generative design (GD) and building information modelling (BIM), as a new technology consolidation, can facilitate the constructability of GD’s automatic design solutions, while improving BIM’s capability in the early design phase. Thus, there has been an increasing interest to study GD-BIM, with current focuses mainly on exploring applications and investigating tools. However, there are a lack of studies regarding methodological relationships and skill requirement based on different development objectives or GD properties; thus, the threshold of developing GD-BIM still seems high. This study conducts a critical review of current approaches for developing GD in BIM, and analyses methodological relationships, skill requirements, and improvement of GD-BIM development. Accordingly, novel perspectives of objective-oriented, GD component-based, and skill-driven GD-BIM development as well as reference guides are proposed. Finally, future research directions, challenges, and potential solutions are discussed. This research aims to guide designers in the building industry to properly determine approaches for developing GD-BIM and inspire researchers’ future studies. Full article
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Article
A Preference-Driven Smart Home Service for the Elderly’s Biophilic Experience
Sensors 2021, 21(15), 5108; https://doi.org/10.3390/s21155108 - 28 Jul 2021
Viewed by 527
Abstract
Smart home services (SHS) should support the positive experiences of the elderly in homes with a focus on getting closer to nature. The study identified the services preferred by the elderly through a survey on the biophilic experience-based SHS, and to discuss the [...] Read more.
Smart home services (SHS) should support the positive experiences of the elderly in homes with a focus on getting closer to nature. The study identified the services preferred by the elderly through a survey on the biophilic experience-based SHS, and to discuss the configuration of the sensors and devices required to provide the service. We reorganized the biophilic experience-based SHS and related sensors and devices, focusing on our previous study, and developed a survey instrument. A preference survey was conducted on 250 adults aged 20 and older, and the SPSS program was used for a factor analysis and independent two-sample T-test. We derived six factors for biophilic experience-based SHS. Compared to other age groups, the elderly preferred services that were mainly attributed to factors such as ‘Immersion and interaction with nature’ (A), ‘Management of well-being and indoor environmental quality (IEQ)’ (B), and ‘Natural process and systems’ (F). We proposed 15 prioritized services, along with their sensor and device configurations, in consideration of service provision regarding the elderly’s preferences and universality. This study contributes to new developments in elderly-friendly smart home research by converting bio-friendly ideas into the market in the development of medical services and SHS for the elderly. Full article
Article
Psychophysiological Response According to the Greenness Index of Subway Station Space
Sensors 2021, 21(13), 4360; https://doi.org/10.3390/s21134360 - 25 Jun 2021
Viewed by 506
Abstract
This study proposed a plan for implementing a pleasant and healthy indoor landscape in subway station space. To this end, it established a 3D landscape model of the subway interior by reviewing previous studies on indoor landscape and the greenness index of indoor [...] Read more.
This study proposed a plan for implementing a pleasant and healthy indoor landscape in subway station space. To this end, it established a 3D landscape model of the subway interior by reviewing previous studies on indoor landscape and the greenness index of indoor spaces. Moreover, it investigated and analyzed psychophysiological responses of users to environmental indoor landscape design in subway station space. Subway stations were classified as underground subway stations and ground subway stations according to the presence of natural light inflow. The greenness index of indoor spaces was also divided into four types of 0%, 10%, 15%, and 20%. Through this process, eight 3D landscape models of the subway interior were implemented. In addition, this study investigated psychophysiological responses of 60 male and female adults in their 20 s and 30 s using the models implemented. The investigation result was analyzed based on a frequency analysis, the χ2 test, T-test, one-way analysis of variance, and multidimensional scaling, which were performed in SPSS Statistics 25. The results of this study can be summarized as follows. First, physiological responses of research subjects were analyzed based on their prefrontal α wave asymmetric values. The analytic result showed that the environment where interior landscape was adopted produced more positive effects than the environment where interior landscape was not adopted. Second, psychological responses of research subjects were examined based on their greenness index preference, awareness of interior landscape area, attention restoration effect, and space images. The analytic result indicated that, among eight 3D landscape models of the subway interior, they preferred the model with the greenness index of 15% for underground subway stations. In addition, they preferred the model with the greenness index of 10% the most for ground subway stations. Full article
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Review
Characterizing Smart Environments as Interactive and Collective Platforms: A Review of the Key Behaviors of Responsive Architecture
Sensors 2021, 21(10), 3417; https://doi.org/10.3390/s21103417 - 14 May 2021
Viewed by 650
Abstract
Since architect Nicholas Negroponte first proposed a vision of responsive architecture smart environments have been widely investigated, especially in the fields of computer science and engineering. Despite growing interest in the topic, a comprehensive review of research about smart environments from the architectural [...] Read more.
Since architect Nicholas Negroponte first proposed a vision of responsive architecture smart environments have been widely investigated, especially in the fields of computer science and engineering. Despite growing interest in the topic, a comprehensive review of research about smart environments from the architectural perspective is largely missing. In order to provide a formal understanding of smart environments in architecture, this paper conducts a systematic literature review of scholarly sources over the last decade, focusing on four related subjects: (1) responsive architecture, (2) kinetic architecture, (3) adaptive architecture and (4) intelligent buildings. Through this review, the paper identifies and examines interactive and collective behaviors in smart environments, thereby contributing to defining the properties of creative, smart spaces in the contemporary digital ecosystem. In addition, this research offers a means of systematically characterizing and constructing smart environments as interactive and collective platforms, enabling occupants to sense, experience and understand smart spaces. Full article
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Article
Developing Sidewalk Inventory Data Using Street View Images
Sensors 2021, 21(9), 3300; https://doi.org/10.3390/s21093300 - 10 May 2021
Cited by 1 | Viewed by 771
Abstract
(1) Background: Public sidewalk GIS data are essential for smart city development. We developed an automated street-level sidewalk detection method with image-processing Google Street View data. (2) Methods: Street view images were processed to produce graph-based segmentations. Image segment regions were manually labeled [...] Read more.
(1) Background: Public sidewalk GIS data are essential for smart city development. We developed an automated street-level sidewalk detection method with image-processing Google Street View data. (2) Methods: Street view images were processed to produce graph-based segmentations. Image segment regions were manually labeled and a random forest classifier was established. We used multiple aggregation steps to determine street-level sidewalk presence. (3) Results: In total, 2438 GSV street images and 78,255 segmented image regions were examined. The image-level sidewalk classifier had an 87% accuracy rate. The street-level sidewalk classifier performed with nearly 95% accuracy in most streets in the study area. (4) Conclusions: Highly accurate street-level sidewalk GIS data can be successfully developed using street view images. Full article
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Article
The Influence of Users’ Spatial Familiarity on Their Emotional Perception of Space and Wayfinding Movement Patterns
Sensors 2021, 21(8), 2583; https://doi.org/10.3390/s21082583 - 07 Apr 2021
Viewed by 500
Abstract
In order to evaluate the sensory perceptions of users who visited a train station, this study aimed to conduct an evaluation of their spatial emotions and identify the distance and type of transfer. For evaluation and verification, emotional recognition and wayfinding types were [...] Read more.
In order to evaluate the sensory perceptions of users who visited a train station, this study aimed to conduct an evaluation of their spatial emotions and identify the distance and type of transfer. For evaluation and verification, emotional recognition and wayfinding types were analyzed according to types in the groups (gender, age, and spatial familiarity) of experimental participants. There were two research questions: “Will the length of movement patterns in the experiment environment vary depending on the types of the participant group?” and “Is there any moderating effect in the interaction between spatial familiarity and the types of the participant groups?” A total of 28 participants were recruited with consideration of gender, age, and familiarity with spatial experience, which were used to analyze the participant groups. The experiment was conducted at a train station, and a vignette was presented to the participants to record the route and pattern of their wayfinding, followed by providing a questionnaire to record their spatial perception. SPSS was used to conduct a T-test, factor analysis, and multidimensional scaling (MDS). The differences in spatial perception were arranged in visual positioning based on emotional vocabulary, and average movement distances in the participant groups were compared in accordance with the type of wayfinding and interaction effect by ANOVA. The results showed that there was a difference in spatial perception depending on the negative emotional vocabulary and type of participant. An emotional positioning map for average comparison was prepared for each participant group (gender, age, and spatial familiarity) by using the factors extracted in the factor analysis (emotional factor, management factor, and aesthetic factor). Female and unfamiliar groups displayed negative results in the emotional factor (F = 7.202, p < 0.05). In addition, male and familiar groups displayed negative results in the management factor (F = 3.058, p < 0.10). In wayfinding, there was an interaction between gender and the resident group based on the status of their spatial familiarity. Through this, it was possible to extract negative emotional evaluations according to the type of participant and the interaction factors for the type and length of the wayfinding. Full article
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Article
Self-Organized Crowd Dynamics: Research on Earthquake Emergency Response Patterns of Drill-Trained Individuals Based on GIS and Multi-Agent Systems Methodology
Sensors 2021, 21(4), 1353; https://doi.org/10.3390/s21041353 - 14 Feb 2021
Cited by 1 | Viewed by 833
Abstract
Predicting evacuation patterns is useful in emergency management situations such as an earthquake. To find out how pre-trained individuals interact with one another to achieve their own goal to reach the exit as fast as possible firstly, we investigated urban people’s evacuation behavior [...] Read more.
Predicting evacuation patterns is useful in emergency management situations such as an earthquake. To find out how pre-trained individuals interact with one another to achieve their own goal to reach the exit as fast as possible firstly, we investigated urban people’s evacuation behavior under earthquake disaster coditions, established crowd response rules in emergencies, and described the drill strategy and exit familiarity quantitatively through a cellular automata model. By setting different exit familiarity ratios, simulation experiments under different strategies were conducted to predict people’s reactions before an emergency. The corresponding simulation results indicated that the evacuees’ training level could affect a multi-exit zone’s evacuation pattern and clearance time. Their exit choice preferences may disrupt the exit options’ balance, leading to congestion in some of the exits. Secondly, due to people’s rejection of long distances, congestion, and unfamiliar exits, some people would hesitant about the evacuation direction during the evacuation process. This hesitation would also significantly reduce the overall evacuation efficiency. Finally, taking a community in Zhuhai City, China, as an example, put forward the best urban evacuation drill strategy. The quantitative relation between exit familiar level and evacuation efficiency was obtained. The final results showed that the optimized evacuation plan could improve evacuation’s overall efficiency through the self-organization effect. These studies may have some impact on predicting crowd behavior during evacuation and designing the evacuation plan. Full article
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Article
Elderly Perception on the Internet of Things-Based Integrated Smart-Home System
Sensors 2021, 21(4), 1284; https://doi.org/10.3390/s21041284 - 11 Feb 2021
Cited by 3 | Viewed by 1316
Abstract
An integrated smart home system (ISHS) is an effective way to improve the quality of life of the elderly. The elderly’s willingness is essential to adopt an ISHS; to the best of our knowledge, no study has investigated the elderly’s perception of ISHS. [...] Read more.
An integrated smart home system (ISHS) is an effective way to improve the quality of life of the elderly. The elderly’s willingness is essential to adopt an ISHS; to the best of our knowledge, no study has investigated the elderly’s perception of ISHS. Consequently, this study aims to investigate the elderly’s perception of the ISHS by comprehensively evaluating its possible benefits and negative responses. A set of sensors required for an ISHS was determined, and interviews were designed based on four factors: perceived comfort, perceived usability, perceived privacy, and perceived benefit. Subsequently, technological trials of the sensor-set followed by two focus group interviews were conducted on nine independently living elderly participants at a senior welfare center in South Korea. Consistent with previous studies, the results of this investigation indicate that elderly participants elicited negative responses regarding usability complexity, and discomfort to daily activities. Despite such negative responses, after acquiring enough awareness about the ISHS’s benefits, the elderly acknowledged its necessity and showed a high level of willingness. Furthermore, these results indicate that for a better adoption of an ISHS, sufficient awareness regarding its benefits and development of elderly-friendly smart home sensors that minimize negative responses are required. Full article
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