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Open AccessEditorial

Statement of Peer Review

by Po-Liang Liu

Eng. Proc. 2024, 78(1), 13; https://doi.org/10.3390/engproc2024078013 - 9 Jun 2025

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Abstract

In submitting conference proceedings to Engineering Proceedings, the volume editors of the proceedings certify to the publisher that all papers published in this volume have been subjected to peer review administered by the volume editors [...] Full article

(This article belongs to the Proceedings of The 1st International Conference on AI Sensors & the 10th International Symposium on Sensor Science)

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6 pages, 2045 KiB

Open AccessProceeding Paper

Chip-Sized Microscopy for Continuous Monitoring: Application in White Wine Fermentation and Yeast Cell Counting via Deep Learning

by Ángel Diéguez, Sergio Moreno, Sofía Moncada-Madrazo, Oriol Caravaca, Joel Diéguez, Joan Canals, Ismael Benito-Altamirano, Juan Daniel Prades and Anna Vilà

Eng. Proc. 2024, 78(1), 1; https://doi.org/10.3390/engproc2024078001 - 8 Oct 2024

Cited by 1 | Viewed by 897

Abstract

Nowadays, continuous monitoring is a difficult issue in microscopy. A chip-sized microscope was developed, composed only of microelectronic components, with high optical resolution and a wide field of view. Due to its miniaturized size, it can be placed on or attached to the [...] Read more.

Nowadays, continuous monitoring is a difficult issue in microscopy. A chip-sized microscope was developed, composed only of microelectronic components, with high optical resolution and a wide field of view. Due to its miniaturized size, it can be placed on or attached to the sample for continuous monitoring in the sample environment. An example of an application of this microscope for the food and beverage industry is described, referring to the study of the fermentation process of white wine. The comparison of the images acquired with conventional optical microscopy reveals similar results. To automatically count yeast cells, the traditional image postprocessing is compared with deep learning. Neural networks achieve similar cell recognition characteristics but with an ~100× speed improvement, by directly processing the obtained holograms. Full article

(This article belongs to the Proceedings of The 1st International Conference on AI Sensors & the 10th International Symposium on Sensor Science)

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10 pages, 12472 KiB

Open AccessProceeding Paper

Deep Transfer Learning Approach in Smartwatch-Based Fall Detection Systems

by Alessandro Leone, Andrea Manni, Gabriele Rescio, Pietro Siciliano and Andrea Caroppo

Eng. Proc. 2024, 78(1), 2; https://doi.org/10.3390/engproc2024078002 - 18 Nov 2024

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Abstract

This study introduces a fall detection system utilizing an affordable consumer smartwatch and smartphone with edge computing capabilities for implementing AI algorithms. Due to the widespread use of these devices, the system as a whole is extremely accepted, easy to use, requires no [...] Read more.

This study introduces a fall detection system utilizing an affordable consumer smartwatch and smartphone with edge computing capabilities for implementing AI algorithms. Due to the widespread use of these devices, the system as a whole is extremely accepted, easy to use, requires no tuning of any kind, and guarantees extended functioning for a long period. From a technical standpoint, falls are identified using AI techniques to analyze 3D raw data acquired by the smartwatch’s built-in accelerometer. However, existing AI models for fall detection are often trained on simulated falls involving young people, which may not accurately represent the falls of elderly in unhealthy conditions, such as arthritis or Parkinson’s disease, leading to limitations in detecting falls in this population. Additionally, variations in hardware features among different smartwatches can result in inconsistencies in accelerometer data measurements across X, Y, and Z orientations, further complicating accurate fall detection. To address the challenge of limited and device-specific datasets and to enhance model generalization across various devices, a Deep Transfer Learning approach is proposed. This method proves effective when data are poor. Specifically, the Continuous Wavelet Transform (CWT) is applied to raw accelerometer signals to convert them into 2D images, enabling the use of deep architectures for Transfer Learning. By employing CWT on 5 s time windowed raw accelerometer signals, heat maps (scalograms) are generated. Real-time accelerations sampled at 50 Hz are collected using a smartwatch application, transmitted via Bluetooth to a smartphone app, and converted into scalograms. These serve as input for pre-trained Deep Learning models to estimate fall probabilities. Preliminary tests on the Wrist Early Daily Activity and Fall Dataset (WEDA-FALL) show promising results with an accuracy of approximately 98%, underscoring the efficacy of utilizing wrist-worn wearable devices for processing raw accelerometer data. Full article

(This article belongs to the Proceedings of The 1st International Conference on AI Sensors & the 10th International Symposium on Sensor Science)

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8 pages, 805 KiB

Open AccessProceeding Paper

Microcontroller-Based EdgeML: Health Monitoring for Stress and Sleep via HRV

by Priyanshu Srivastava, Namita Shah and Kavita Jaiswal

Eng. Proc. 2024, 78(1), 3; https://doi.org/10.3390/engproc2024078003 - 4 Dec 2024

Cited by 1 | Viewed by 1285

Abstract

The healthcare sector is undergoing a transformation with the integration of cutting-edge technologies such as machine learning (ML), the Internet-of-Things (IoT), and Cyber–Physical Systems (CPS). However, traditional ML systems often face challenges in real-time processing and resource efficiency, limiting their application in life-critical [...] Read more.

The healthcare sector is undergoing a transformation with the integration of cutting-edge technologies such as machine learning (ML), the Internet-of-Things (IoT), and Cyber–Physical Systems (CPS). However, traditional ML systems often face challenges in real-time processing and resource efficiency, limiting their application in life-critical scenarios. This research explores the potential of edge ML, particularly TinyML with TensorFlow Lite, implemented on microcontroller-based AI sensors for real-time health monitoring. By leveraging model quantization, the system analyzes heart rate variability (HRV) data to deliver continuous and personalized insights into stress levels and sleep quality. Trained on SWELL and ISRUC datasets, the system is highly energy-efficient, consuming 33 mW in idle mode, 66 mW during data collection, and 99 mW during real-time inference, making it suitable for resource-constrained environments. Performance analysis reveals significant demographic variations: younger individuals (18–25) achieved 90% accuracy due to higher HRV and lower baseline stress, while middle-aged (26–50) and older adults (50+) demonstrated declining HRV, reducing accuracy to 82% for the latter. Gender differences were also observed, with males exhibiting greater stress response sensitivity and better accuracy (89%) compared to females. This study underscores the transformative potential of TinyML for real-time, energy-efficient health monitoring and emphasizes the need for demographic-specific optimizations to enhance system reliability and accessibility. Full article

(This article belongs to the Proceedings of The 1st International Conference on AI Sensors & the 10th International Symposium on Sensor Science)

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11 pages, 2981 KiB

Open AccessProceeding Paper

Enhancing Wildfire Risk Management Through Sensor-Based AI Integration in Social IoT Frameworks

by Martina Putzu, Daniele Loru, Francesco Carta, Angelo Ledda, Alessio Chirigu, Mariella Sole, Matteo Anedda and Daniele Giusto

Eng. Proc. 2024, 78(1), 4; https://doi.org/10.3390/engproc2024078004 - 11 Dec 2024

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Abstract

The search for solutions aimed at environmental protection is still an open and increasingly topical challenge. Information and communication technology is playing an increasing role in the research and development of innovative solutions. In this paper, a widespread and scalable solution for forest [...] Read more.

The search for solutions aimed at environmental protection is still an open and increasingly topical challenge. Information and communication technology is playing an increasing role in the research and development of innovative solutions. In this paper, a widespread and scalable solution for forest fire detection based on LoRa technology, a wireless sensor network (WSN), and the development and training of a feed-forward neural network is proposed. Data analysis and alert management are handled through the Social Internet of Things (SIoT) paradigm. The proposed method is validated on a real forest scenario and provides a validated configuration for the early detection of forest fires. Full article

(This article belongs to the Proceedings of The 1st International Conference on AI Sensors & the 10th International Symposium on Sensor Science)

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5 pages, 1712 KiB

Open AccessProceeding Paper

Evaluation of a Continuous Blood Glucose Sensor’s Performance for Hospitalized Patients

by Ruiqi Lim, James Ven Wee Yap, Siti Rafeah Mohamed Rafei and Ming-Yuan Cheng

Eng. Proc. 2024, 78(1), 5; https://doi.org/10.3390/engproc2024078005 - 20 Dec 2024

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Abstract

Frequent blood glucose monitoring is crucial for managing blood glucose levels in critically ill hospitalized patients experiencing hyperglycemia or hypoglycemia. Existing blood glucose monitoring methods are often cumbersome, painful, and impractical for hourly testing. This work provides a solution for frequent glucose monitoring [...] Read more.

Frequent blood glucose monitoring is crucial for managing blood glucose levels in critically ill hospitalized patients experiencing hyperglycemia or hypoglycemia. Existing blood glucose monitoring methods are often cumbersome, painful, and impractical for hourly testing. This work provides a solution for frequent glucose monitoring (less than 1 h per test) for hospitalized patients, ensuring accuracy while minimizing discomfort from blood collection. The glucose sensor demonstrated accuracy within 10% across a range of 0–20 mM over 96 testing cycles. This meets the need for hourly monitoring during an average 2-day hospital stay. Full article

(This article belongs to the Proceedings of The 1st International Conference on AI Sensors & the 10th International Symposium on Sensor Science)

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8 pages, 1425 KiB

Open AccessProceeding Paper

Enhanced Skin Lesion Classification Using Deep Learning, Integrating with Sequential Data Analysis: A Multiclass Approach

by Azmath Mubeen and Uma N. Dulhare

Eng. Proc. 2024, 78(1), 6; https://doi.org/10.3390/engproc2024078006 - 7 Jan 2025

Cited by 2 | Viewed by 996

Abstract

In dermatological research, accurately identifying different types of skin lesions, such as nodules, is essential for early diagnosis and effective treatment. This study introduces a novel method for classifying skin lesions, including nodules, by combining a unified attention (UA) network with deep convolutional [...] Read more.

In dermatological research, accurately identifying different types of skin lesions, such as nodules, is essential for early diagnosis and effective treatment. This study introduces a novel method for classifying skin lesions, including nodules, by combining a unified attention (UA) network with deep convolutional neural networks (DCNNs) for feature extraction. The UA network processes sequential data, such as patient histories, while long short-term memory (LSTM) networks track nodule progression. Additionally, Markov random fields (MRFs) enhance pattern recognition. The integrated system classifies lesions and evaluates whether they are responding to treatment or worsening, achieving 93% accuracy in distinguishing nodules, melanoma, and basal cell carcinoma. This system outperforms existing methods in precision and sensitivity, offering advancements in dermatological diagnostics. Full article

(This article belongs to the Proceedings of The 1st International Conference on AI Sensors & the 10th International Symposium on Sensor Science)

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8 pages, 2712 KiB

Open AccessProceeding Paper

CareTaker.ai—A Smart Health-Monitoring and Caretaker-Assistant System for Elder Healthcare

by Ankur Gupta, Sahil Sawhney and Suhaib Ahmed

Eng. Proc. 2024, 78(1), 7; https://doi.org/10.3390/engproc2024078007 - 8 Jan 2025

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Abstract

There are several systems for patient care, including elderly healthcare, which rely on sensor data acquisition and analysis. These sensors are typical vital-monitoring sensors and are coupled with Artificial Intelligence (AI) models to quickly analyze emergency situations or even predict them. These systems [...] Read more.

There are several systems for patient care, including elderly healthcare, which rely on sensor data acquisition and analysis. These sensors are typical vital-monitoring sensors and are coupled with Artificial Intelligence (AI) models to quickly analyze emergency situations or even predict them. These systems are deployed in hospitals and require expensive monitoring and analysis equipment. Eldercare specifically encompasses monitoring, smart analysis, and even the emotional aspects of care. Existing systems do not provide a portable, easy-to-use system for at-home eldercare. Further, existing systems do not address advanced analysis capabilities around mood/sentiment/mental state/mental disorder analysis or the analysis of issues around sleep disorders, apnea, etc., based on sound capture and analysis. Also, existing systems disregard the emotional needs of elderly patients, which are a critical aspect of patient wellbeing. A low-cost and effective solution is therefore required for extended use in eldercare. In this paper, the CareTaker.ai system is proposed to address the shortcomings of the existing systems and build a comprehensive caretaker assistant using sensors, audio, video, and AI. It consists of smart bed sheets, pillow covers with embedded sensors, and a processing unit with GPUs, conversational AI, and generative AI capabilities, with associated functional modules. Compared to existing systems, the proposed system has advanced monitoring and analysis capabilities with potential for low-cost mass manufacturing and a widespread commercial application. Full article

(This article belongs to the Proceedings of The 1st International Conference on AI Sensors & the 10th International Symposium on Sensor Science)

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7 pages, 4149 KiB

Open AccessProceeding Paper

Empowering Smart Surfaces: Optimizing Dielectric Inks for In-Mold Electronics

by Priscilla Hong, Gibson Soo Chin Yuan, Yeow Meng Tan and Kebao Wan

Eng. Proc. 2024, 78(1), 8; https://doi.org/10.3390/engproc2024078008 - 6 Feb 2025

Viewed by 468

Abstract

Dielectric materials have gained traction for their energy-storage capacitive and electrically insulating properties as sensors and in smart surface technologies such as in In-Mold Electronics (IME). IME is a disruptive technology that involves environmentally protected electronics in plastic thermoformed and molded structures. The [...] Read more.

Dielectric materials have gained traction for their energy-storage capacitive and electrically insulating properties as sensors and in smart surface technologies such as in In-Mold Electronics (IME). IME is a disruptive technology that involves environmentally protected electronics in plastic thermoformed and molded structures. The use of IME in a human–machine interface (HMI) provides a favorable experience to the users and helps reduce production costs due to a smaller list of parts and lower material costs. A few functional components that are compatible with one another are crucial to the final product’s properties in the IME structure. Of these components, the dielectric layers are an important component in the smart surface industry, providing insulation for the prevention of leakage currents in multilayered printed structures and capacitance sensing on the surface of specially designed shapes in IME. Advanced dielectric materials are non-conductive materials that impend and polarize electron movements within the material, store electrical energy, and reduce the flow of electric current with exceptional thermal stability. The selection of a suitable dielectric ink is an integral stage in the planning of the IME smart touch surface. The ink medium, solvent, and surface tension determine the printability, adhesion, print quality, and the respective reaction with the bottom and top conductive traces. The sequence in which the components are deposited and the heating processes in subsequent thermoforming and injection molding are other critical factors. In this study, various commercially available dielectric layers were each printed in two to four consecutive layers with a mesh thickness of 50–60 µm or 110–120 µm, acting as an insulator between conductive silver traces overlaid onto a polycarbonate substrate. Elemental mapping and optical analysis on the cross-section were conducted to determine the compatibility and the adhesion of the dielectric layers on the conductive traces and polycarbonate substrate. The final selection was based on the functionality, reliability, repeatability, time-stability, thickness, total processing time, appearance, and cross-sectional analysis results. The chosen candidate was then placed through the final product design, circuitry design, and plastic thermoforming process. In summary, this study will provide a general guideline to optimize the selection of dielectric inks for in-mold electronics applications. Full article

(This article belongs to the Proceedings of The 1st International Conference on AI Sensors & the 10th International Symposium on Sensor Science)

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9 pages, 2051 KiB

Open AccessProceeding Paper

Secure Internet of Things Device with Single-Channel Communication for Resource-Constrained Applications

by Ankur Gupta, Sahil Sawhney and Suhaib Ahmed

Eng. Proc. 2024, 78(1), 9; https://doi.org/10.3390/engproc2024078009 - 11 Feb 2025

Viewed by 395

Abstract

Internet of Things (IoT) devices are being utilized in large numbers in various applications, ranging from healthcare, manufacturing, and home automation to manufacturing, etc. This rapid increase in the number of devices has also led to a significant increase in cybersecurity concerns for [...] Read more.

Internet of Things (IoT) devices are being utilized in large numbers in various applications, ranging from healthcare, manufacturing, and home automation to manufacturing, etc. This rapid increase in the number of devices has also led to a significant increase in cybersecurity concerns for these devices. Malicious nodes exploit vulnerabilities in conventional IoT architectures, leading to high risks to data integrity, privacy, and system reliability. Hence, there is a need for innovative solutions to improve the security of IoT devices and networks. Considering this, in this paper, a novel approach towards securing IoT devices against any malicious attack is proposed. A new IoT node is proposed, that has a single communication channel, which sends information only to a specific endpoint. Unlike a conventional IoT system with multiple communication channels, the proposed design limits communication to a single dedicated device, thereby drastically reducing the attack probability. The proposed device architecture integrates a robust cryptographic protocol to establish a secure and authenticated communication link between the IoT device and its designated endpoint. By employing a state-of-the-art encryption technique and secure access controls, the proposed solution can mitigate common attacks such as eavesdropping, data tampering, and unauthorized access. The proposed system also improves resource efficiency, lowers the device’s power consumption due to single-communication-channel data transmission, and simplifies network management, thus making it suitable for resource-constrained applications, such as military surveillance, home automation, etc. Full article

(This article belongs to the Proceedings of The 1st International Conference on AI Sensors & the 10th International Symposium on Sensor Science)

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10 pages, 5680 KiB

Open AccessProceeding Paper

ClassRoom-Crowd: A Comprehensive Dataset for Classroom Crowd Counting and Cross-Domain Baseline Analysis

by Wenqian Jiang, Xiaohua Huang, Qun Zhao and Sheng Liu

Eng. Proc. 2024, 78(1), 10; https://doi.org/10.3390/engproc2024078010 - 11 Feb 2025

Viewed by 627

Abstract

In recent years, with the rise of smart education, crowd counting technology has garnered increasing attention for its applications in educational environments. During epidemic outbreaks, ensuring the accurate monitoring of student density in classrooms and other public educational spaces has become crucial. Additionally, [...] Read more.

In recent years, with the rise of smart education, crowd counting technology has garnered increasing attention for its applications in educational environments. During epidemic outbreaks, ensuring the accurate monitoring of student density in classrooms and other public educational spaces has become crucial. Additionally, in day-to-day educational management, tasks such as optimizing the allocation of teaching resources and planning educational spaces heavily depend on precise crowd counting techniques. Current methods predominantly employ convolutional neural networks, which require large datasets for training. However, there is a lack of specialized datasets tailored to specific educational scenarios and extreme conditions. Furthermore, to enhance crowd movement predictions in real-world applications, these methods must demonstrate temporal coherence. To address these gaps, this paper introduces the ClassRoom-Crowd dataset, specifically designed for crowd counting in educational settings with extreme conditions and temporal continuity. The dataset consists of 7571 images with 172,898 annotated objects. Additionally, baseline results using state-of-the-art crowd counting algorithms under a cross-condition protocol are provided. The experimental findings reveal that domain shifts significantly impact model performance, underscoring the need for domain adaptation methods in crowd counting research, particularly within the smart education context. Full article

(This article belongs to the Proceedings of The 1st International Conference on AI Sensors & the 10th International Symposium on Sensor Science)

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9 pages, 2950 KiB

Open AccessProceeding Paper

Cost-Effective Triboelectric-Assisted Sensory Actuator Designed for Intelligent Robot and Exoskeleton

by Haowen Liu, Yusong Chu, Yudong Zhao, Guanyu Zhu, Xuan Li, Minglu Zhu and Tao Chen

Eng. Proc. 2024, 78(1), 11; https://doi.org/10.3390/engproc2024078011 - 18 Apr 2025

Viewed by 2346

Abstract

Joint actuators are the key components in the innovation and iterative optimization of the robots, with a significant impact on both the performances of robots and manufacturing costs. Conventional industrial collaborative robots often use high-precision position and torque sensors, which are not cost-effective [...] Read more.

Joint actuators are the key components in the innovation and iterative optimization of the robots, with a significant impact on both the performances of robots and manufacturing costs. Conventional industrial collaborative robots often use high-precision position and torque sensors, which are not cost-effective or energy-efficient in specific applications like assistive exoskeletons, legged robots, or wheeled robots. Alternatively, we propose a triboelectric-assisted sensory actuator that balances lightweight design, performance, and affordability for large-scale applications. The actuator is composed of a high-power density motor, a low reduction gearbox, and integrated with a rotational triboelectric sensor, which leads to high dynamic performances and low power consumption. The feasibility of the prototype is initially verified by characterizing the angular positioning accuracy and the back drivability. Experiments indicate that the rotational triboelectric sensor is able to accurately detect the angular displacement of the actuator with the self-generated signals. Overall, a highly integrated actuator module with the actuation and sensing circuit is fabricated as a compact design ready for assembling a complete intelligent robot. This actuator holds great potential as a cost-effective, energy-efficient, and versatile solution for modern robotics, crucial for advancing this field and improving human convenience. Full article

(This article belongs to the Proceedings of The 1st International Conference on AI Sensors & the 10th International Symposium on Sensor Science)

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13 pages, 1763 KiB

Open AccessProceeding Paper

Transforming Petrochemical Safety Using a Multimodal AI Visual Analyzer

by Uzair Bhatti, Qamar Jaleel, Umair Aslam, Ahrad bin Riaz, Najam Saeed and Khurram Kamal

Eng. Proc. 2024, 78(1), 12; https://doi.org/10.3390/engproc2024078012 - 29 May 2025

Viewed by 300

Abstract

The petrochemical industry faces significant safety challenges, necessitating stringent protocols and advanced monitoring systems. Traditional methods rely on manual inspections and fixed sensors, often reacting to hazards only after they occur. Multimodal AI, integrating visual, sensor, and textual data, offers a transformative solution [...] Read more.

The petrochemical industry faces significant safety challenges, necessitating stringent protocols and advanced monitoring systems. Traditional methods rely on manual inspections and fixed sensors, often reacting to hazards only after they occur. Multimodal AI, integrating visual, sensor, and textual data, offers a transformative solution for real-time, proactive safety management. This paper evaluates AI models—Gemini 1.5 Pro, OPENAI GPT-4, and Copilot—in detecting workplace hazards, ensuring compliance with Process Safety Management (PSM) and DuPont safety frameworks. The study highlights the models’ potential in improving safety outcomes, reducing human error, and supporting continuous, data-driven risk management in petrochemical plants. This paper is the first of its kind to use the latest multimodal tech to identify the safety hazard; a similar model could be deployed in other manufacturing industries, especially the oil and gas (both upstream and downstream) industry, fertilizer industries, and production facilities. Full article

(This article belongs to the Proceedings of The 1st International Conference on AI Sensors & the 10th International Symposium on Sensor Science)

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