Scholarlink

<p>With the development of smart technologies and smart buildings, more multimodal big data are being generated in buildings. The availability of more detailed and real-time buildings data on well-being and other aspects is, however, still inadequate. The collection of such data should preferably be low-cost. Our innovative Multimodal Property Video Neuroanalytics (MOVE) can provide such data. The research aims to develop the MOVE designed to examine the property, its context, and potential investors’ emotional, affective, and physiological states (MAPS) by combining the circumplex model of affect, the somatic marker hypothesis, regression and multiple criteria analysis, and neuromarketing and recommender methods. The link between the built environment and emotions can be deep and multifaceted. We developed the MOVE by integrating text, biometrics, audio, and image analysis technologies, regression and multi-criteria analysis methods. The MOVE contains multimodal analysis, fusion, regression, property decision support, digital evidence-based recommendations, and value analysis subsystems. Using the proposed MOVE, the perceived investment value is determined and evidence-based recommendations are provided on how to increase the property value. To improve green building ratings, stakeholders should use potential investors’ and users’ MAPS data, which adequately reflect the quality of green buildings. We suggest including a new emotional dimension in green building rating systems.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Multimodal property video neuroanalytics

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年10月29日

查看全文
  • Arturas Kaklauskas,
  • Valeria Minucciani,
  • Gianluca D’Agostino,
  • Kestutis Dauksys,
  • Romualdas Kliukas,
  • Simona Kildiene,
  • Raimonda Bubliene,
  • Vitalijus Gurcinas,
  • Virginijus Milevicius

摘要

With the development of smart technologies and smart buildings, more multimodal big data are being generated in buildings. The availability of more detailed and real-time buildings data on well-being and other aspects is, however, still inadequate. The collection of such data should preferably be low-cost. Our innovative Multimodal Property Video Neuroanalytics (MOVE) can provide such data. The research aims to develop the MOVE designed to examine the property, its context, and potential investors’ emotional, affective, and physiological states (MAPS) by combining the circumplex model of affect, the somatic marker hypothesis, regression and multiple criteria analysis, and neuromarketing and recommender methods. The link between the built environment and emotions can be deep and multifaceted. We developed the MOVE by integrating text, biometrics, audio, and image analysis technologies, regression and multi-criteria analysis methods. The MOVE contains multimodal analysis, fusion, regression, property decision support, digital evidence-based recommendations, and value analysis subsystems. Using the proposed MOVE, the perceived investment value is determined and evidence-based recommendations are provided on how to increase the property value. To improve green building ratings, stakeholders should use potential investors’ and users’ MAPS data, which adequately reflect the quality of green buildings. We suggest including a new emotional dimension in green building rating systems.