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Prof. Dr. Rui Zhang

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School of Instrumentation Science and Engineering, Harbin Institute of Technology, Harbin 150000, China
Interests: theory and method for testing relaxation type ferroelectric single crystals; design and fabrication of relaxation type ferroelectric single crystal functional devices; ultrasonic transducers and surface acoustic waves

Special Issue Information

Dear Colleagues,

As an important instrument that can convert a sound signal into electrical signal, acoustic sensors are widely used in various fields such as healthcare, geophysics, and agriculture. Based on different theories, there are two kinds of acoustic sensitivity, namely, piezoelectric acoustic sensors and capacitive acoustic sensors. In addition, fiber-based distributed acoustic sensors as powerful instruments are becoming an interesting research issue in acoustic field analyzing. Different acoustic sensors are sensitive in different frequency ranges. Ultrasound, whose frequency is over 20 kHz, is a common spectrum in research, allowing us to perform activities such as health monitoring and non-destructive material testing. The signal from sensors can be handled through an advanced intelligent algorithm.

This Special Issue shall present articles as an overview across advanced acoustic sensing technology, such as acoustic sensitivity, piezoelectric transducer, capacitive acoustic sensors, and distributed acoustic sensors, in recent years.

Submission of both review articles and original research papers relating to piezoelectric transducer on health monitors will be much appreciated.

Prof. Dr. Rui Zhang
Guest Editor

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Keywords

acoustic sensitivity
piezoelectric transducer
capacitive acoustic sensors
distributed acoustic sensors
wearable
health monitor
non-destructive material testing
intelligence
algorithm

Published Papers (1 paper)

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Research

17 pages, 4342 KiB

Open AccessArticle

Identification of Myofascial Trigger Point Using the Combination of Texture Analysis in B-Mode Ultrasound with Machine Learning Classifiers

by Fatemeh Shomal Zadeh, Ryan G. L. Koh, Banu Dilek, Kei Masani and Dinesh Kumbhare

Sensors 2023, 23(24), 9873; https://doi.org/10.3390/s23249873 - 16 Dec 2023

Viewed by 1042

Abstract

Myofascial pain syndrome is a chronic pain disorder characterized by myofascial trigger points (MTrPs). Quantitative ultrasound (US) techniques can be used to discriminate MTrPs from healthy muscle. In this study, 90 B-mode US images of upper trapezius muscles were collected from 63 participants (left and/or right side(s)). Four texture feature approaches (individually and a combination of them) were employed that focused on identifying spots, and edges were used to explore the discrimination between the three groups: active MTrPs (n = 30), latent MTrPs (n = 30), and healthy muscle (n = 30). Machine learning (ML) and one-way analysis of variance were used to investigate the discrimination ability of the different approaches. Statistically significant results were seen in almost all examined features for each texture feature approach, but, in contrast, ML techniques struggled to produce robust discrimination. The ML techniques showed that two texture features (i.e., correlation and mean) within the combination of texture features were most important in classifying the three groups. This discrepancy between traditional statistical analysis and ML techniques prompts the need for further investigation of texture-based approaches in US for the discrimination of MTrPs. Full article

(This article belongs to the Special Issue Advanced Acoustic Sensing Technology)

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