LEARNING SOUND WAVES' EFFECTS ON EMG SIGNALS USING ARTIFICIAL INTELLIGENCE

Sound is an energy (Raghu , 2018; Choi, Jung, & Kang, 2019) or a vibration that can be propagated into human bodies as sound waves and these sound waves have either positive or negative effects on both physical and mental well-being of people (Bass & Clark, 2003; World Health Organization, 2018; Reybrouck , Podlipniak, & Welch, 2019). These positive or negative sound wave effects can culminate into therapeutic or pathologic effects hence our aim of creating an artificial intelligence-based model for detection and prediction of therapeutic sound frequencies and amplitudes based on individual health needs. Our research aims to design an artificial intelligence-based (AI) model for detecting human muscle response to different sound frequencies and amplitudes. Using artificial intelligence, we were able to find the difference between the EMG signals recorded from the volunteers during different sound waves applications. This means that human muscle responds to different sound signals and react differently.

Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2025-03-25
DOI
https://doi.org/10.5281/zenodo.15085566
Primary Topic
Phonocardiography and Auscultation Techniques
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

LEARNING SOUND WAVES' EFFECTS ON EMG SIGNALS USING ARTIFICIAL INTELLIGENCE

Zenodo (CERN European Organization for Nuclear Research)
Phonocardiography and Auscultation Techniques
article

LEARNING SOUND WAVES' EFFECTS ON EMG SIGNALS USING ARTIFICIAL INTELLIGENCE

article en

Abstract

Sound is an energy (Raghu , 2018; Choi, Jung, & Kang, 2019) or a vibration that can be propagated into human bodies as sound waves and these sound waves have either positive or negative effects on both physical and mental well-being of people (Bass & Clark, 2003; World Health Organization, 2018; Reybrouck , Podlipniak, & Welch, 2019). These positive or negative sound wave effects can culminate into therapeutic or pathologic effects hence our aim of creating an artificial intelligence-based model for detection and prediction of therapeutic sound frequencies and amplitudes based on individual health needs. Our research aims to design an artificial intelligence-based (AI) model for detecting human muscle response to different sound frequencies and amplitudes. Using artificial intelligence, we were able to find the difference between the EMG signals recorded from the volunteers during different sound waves applications. This means that human muscle responds to different sound signals and react differently.

Zenodo (CERN European Organization for Nuclear Research)
Openalex Percentile: Top 36%
Phonocardiography and Auscultation Techniques
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.

LEARNING SOUND WAVES' EFFECTS ON EMG SIGNALS USING ARTIFICIAL INTELLIGENCE · Zenodo (CERN European Organization for Nuclear Research) (2025) | TGRS Research Map | TGRS