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Eng. Proc., 2024, AIS & I3S 2024

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6 pages, 2045 KiB  
Proceeding 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
Viewed by 372
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
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10 pages, 12472 KiB  
Proceeding 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
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