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Selected Papers From the 1st International Conference on AI Sensors (AIS)

A special issue of Sensors (ISSN 1424-8220).

Deadline for manuscript submissions: closed (31 December 2024) | Viewed by 3104

Special Issue Editors

Prof. Dr. Chengkuo Lee

grade E-Mail Website
Guest Editor
Department of Electrical and Computer Engineering, National University of Singapore, Singapore 117576, Singapore
Interests: MEMS; NEMS; nanophotonics; Si photonics; metamaterials; energy harvesting; wearable sensors; flexible electronics; Artificial Intelligence of Things (AIoT); Internet of Things (IoT); electroceuticals and biomedical applications
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Po-Liang Liu

E-Mail Website
Guest Editor
Graduate Institute of Precision Engineering, National Chung Hsing University, Taichung 402, Taiwan
Interests: density functional theory and its application to the computational simulation and modeling of optical; vibrational, electronic, and thermoelastic properties of materials
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues, 

This Special Issue was created in collaboration with the 1st International Conference on AI Sensors and the 10th International Symposium on Sensor Science (AIS-I3S 2024), which will be held in University Town, National University of Singapore, Singapore from 1 August to 4 August 2024. It comprises five symposia. The conference participants are cordially invited to contribute a full manuscript to this Special Issue and receive a 20% discount on the Article Processing Charge.

AIS Symposia
S1: Wearable AI Sensors
S2: Energy Harvesting Technology for Self-Sustained AIoT System
S3: Enabling Technologies for Neuromorphic Computing and Photonics Neural Networks
S4: Haptic Technology for Future Metaverse Applications
S5: Advances in Intelligent Sensors and Robots for AI-Enhanced Applications - Industry 5.0, Digital Twin, Smart Homes, Healthcare, and Rehabilitation

Prof. Dr. Chengkuo Lee
Prof. Dr. Po-Liang Liu
Guest 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 submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue 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 2600 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

  • AI sensor
  • energy harvesting technology
  • wearables
  • intelligent sensor
  • industry 5.0
  • digital twin
  • smart homes
  • healthcare
  • rehabilitation

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Published Papers (2 papers)

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Research

22 pages, 11689 KiB  
Article
Predicting Restroom Dirtiness Based on Water Droplet Volume Using the LightGBM Algorithm
by Sumio Kurose, Hironori Moriwaki, Tadao Matsunaga and Sang-Seok Lee
Sensors 2025, 25(7), 2186; https://doi.org/10.3390/s25072186 - 30 Mar 2025
Viewed by 91
Abstract
This study examines restroom cleanliness in public facilities, department stores, supermarkets, and schools by using water droplet volumes around washbowls as an indicator of usage. Rising cleaning costs due to labour shortages necessitate more efficient restroom maintenance. Quantifying water droplet accumulation and predicting [...] Read more.
This study examines restroom cleanliness in public facilities, department stores, supermarkets, and schools by using water droplet volumes around washbowls as an indicator of usage. Rising cleaning costs due to labour shortages necessitate more efficient restroom maintenance. Quantifying water droplet accumulation and predicting cleaning schedules can help optimise cleaning frequency. To achieve this, water droplet volumes were measured at specific time intervals, with significant variations indicating increased restroom usage and potential dirt buildup. For real-world assessment, acrylic plates were placed on both sides of washbowls in public restrooms. These plates were collected every hour over five days and analysed using near-infrared photography to track changes in water droplet areas. The collected data informed the development of a prediction system based on the decision tree method, implemented via the LightGBM framework. This paper presents the developed prediction system, which utilises in situ water droplet volume measurements, and evaluates its accuracy in forecasting restroom cleaning needs. Full article
(This article belongs to the Special Issue Selected Papers From the 1st International Conference on AI Sensors (AIS))
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19 pages, 5834 KiB  
Article
Data Reconstruction Methods in Multi-Feature Fusion CNN Model for Enhanced Human Activity Recognition
by Jae Eun Ko, SeungHui Kim, Jae Ho Sul and Sung Min Kim
Sensors 2025, 25(4), 1184; https://doi.org/10.3390/s25041184 - 14 Feb 2025
Viewed by 629
Abstract
Background: Human activity recognition (HAR) plays a pivotal role in digital healthcare, enabling applications such as exercise monitoring and elderly care. However, traditional HAR methods relying on accelerometer data often require complex preprocessing steps, including noise reduction and manual feature extraction. Deep learning-based [...] Read more.
Background: Human activity recognition (HAR) plays a pivotal role in digital healthcare, enabling applications such as exercise monitoring and elderly care. However, traditional HAR methods relying on accelerometer data often require complex preprocessing steps, including noise reduction and manual feature extraction. Deep learning-based human activity recognition (HAR) using one-dimensional accelerometer data often suffers from noise and limited feature extraction. Transforming time-series signals into two-dimensional representations has shown potential for enhancing feature extraction and reducing noise. However, existing methods relying on single-feature inputs or extensive preprocessing face limitations in robustness and accuracy. Methods: This study proposes a multi-input, two-dimensional CNN architecture using three distinct data reconstruction methods. By fusing features from reconstructed images, the model enhances feature extraction capabilities. This method was validated on a custom HAR dataset without requiring complex preprocessing steps. Results: The proposed method outperformed models using single-reconstruction methods or raw one-dimensional data. Compared to a one-dimensional baseline, it achieved 16.64%, 13.53%, and 16.3% improvements in accuracy, precision, and recall, respectively. We tested across various levels of noise, and the proposed model consistently demonstrated greater robustness than the time-series-based approach. Fusing features from three inputs effectively captured latent patterns and variations in accelerometer data. Conclusions: This study demonstrates that HAR can be effectively improved using a multi-input CNN approach with reconstructed data. This method offers a practical and efficient solution, streamlining feature extraction and enhancing performance, making it suitable for real-world applications. Full article
(This article belongs to the Special Issue Selected Papers From the 1st International Conference on AI Sensors (AIS))
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