Smart Pervasive Technologies Utilizing Non-Verbal Information

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Artificial Intelligence".

Deadline for manuscript submissions: 15 September 2026 | Viewed by 995

Special Issue Editors

Center of Mathematical and Data Science, Kobe University, 1-1 Rokkodai-cho, Nada-ku, Kobe 657-8501, Japan
Interests: smart healthcare; AIoT; data science; digital learning; multimodal AI; software engineering
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Guest Editor
Department of Computer Science and Engineering, Waseda University, Tokyo 169-8050, Japan
Interests: self-adaptive system; software engineering; human-computer interaction
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Utilizing non-verbal information to advance the development of intelligent pervasive technologies for daily human interactions has emerged as a new focal point. In the domain of non-verbal communication tailored to specific demographics or purposes, research and development activities are burgeoning around physical gestures, facial expressions, eye movements, and other motor behaviors, as well as bodily characteristics (e.g., weight, body odor, hair, and skin features, and physique), tactile interactions (e.g., greetings, hugs, and touches), paralanguage (e.g., tone, intensity, and rhythm of speech), spatial behaviors (e.g., interpersonal distances, defensive perimeters, territories), and artifacts (e.g., eyeglasses and clothing).

However, challenges persist in implementing cloud and fog computing due to concerns over user privacy and security, with current mainstream approaches relying heavily on edge computing. Elements such as intrusiveness in daily life, accuracy, availability, affordability, long-term maintenance complexity, individuality, and even ethical considerations must all be addressed through practical research.

Recently, AI-based pre-trained models for image and sound recognition have emerged, begetting the question of whether traditional data collection and training processes can be entirely streamlined. Furthermore, diverse techniques such as ensemble learning, transfer learning, and model fine-tuning offer promising avenues for exploration. Moreover, hardware integration, including smart robots, has yet to fully converge with these technologies in the current market. Against this backdrop, what are the greatest difficulties and challenges in achieving such integration? Addressing this issue demands continuous reflection and exploration from scholars.

For this Special Issue, original research articles and reviews are welcome. Research areas may include (but are not limited to) the following:

  • Smart pervasive services with physical gestures;
  • Facial expression estimation for smart pervasive services;
  • Eye movement measurement for smart pervasive services;
  • Smart pervasive services based on tactile interactions;
  • Smart pervasive services using paralanguage.

Dr. Sinan Chen
Dr. Jialong Li
Guest Editors

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Keywords

  • smart pervasive technologies
  • non-verbal information
  • smart pervasive services with edge computing
  • AI-based pre-trained models
  • ensemble learning
  • transfer learning
  • model fine-tuning for smart pervasive systems
  • affordable hardware for smart pervasive services

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Published Papers (1 paper)

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Research

21 pages, 669 KB  
Article
Non-Invasive Showering Estimation Utilizing Household-Adaptive Models and Washing Time Data
by Takuya Nakata, Jiro Hashizume, Akihiro Yanada and Masahide Nakamura
Electronics 2025, 14(21), 4336; https://doi.org/10.3390/electronics14214336 - 5 Nov 2025
Viewed by 617
Abstract
This study introduces a dual-proxy framework for household-adaptive, non-invasive shower detection using standard water-heater logs. The framework leverages proxy at two complementary levels: a feature-level proxy (washing_seconds) that captures washing duration, and a scheme-level proxy (proxy-driven training) that enables learning in periods without [...] Read more.
This study introduces a dual-proxy framework for household-adaptive, non-invasive shower detection using standard water-heater logs. The framework leverages proxy at two complementary levels: a feature-level proxy (washing_seconds) that captures washing duration, and a scheme-level proxy (proxy-driven training) that enables learning in periods without direct shower labels. The proxy feature (washing_seconds) serves as an indirect descriptor of washing behavior, enabling effective inference even under label scarcity. We investigated three research questions: (RQ1) the effectiveness of proxy features in improving shower detection, (RQ2) how proxy-driven evaluation identifies compact yet reliable feature subsets, and (RQ3) the robustness of these subsets in long-term, real-world scenarios. Experiments on two households showed that washing_seconds consistently improved discrimination (raising summer PR-AUC, lowering non-summer false alarms), and that compact subsets of only two or three features, anchored by the proxy feature, achieved stable performance across households. The evaluation represents an illustrative example based on two cooperating households, providing practical evidence of the framework’s real-world applicability. Evaluation in real-world conditions confirmed robustness: representative subsets maintained micro PR-AUC 0.724–0.728, micro F1 0.66–0.69 (macro F1 0.55–0.58), and summer PR-AUC near 0.87, with generalization gaps within ±0.01 for discrimination and small positive shifts for F1 (+0.02–+0.05). These results demonstrate that proxy can function both as a feature and as a methodological principle, and that the proposed framework is model-agnostic and transferable to other learning architectures. It provides a foundation for adaptive, privacy-preserving smart home applications that can scale to broader household and healthcare contexts. Full article
(This article belongs to the Special Issue Smart Pervasive Technologies Utilizing Non-Verbal Information)
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