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Sound of Daily Living Identification Based on Hierarchical Situation Audition
One of the key objectives in developing IoT applications is to automatically detect and identify human activities of daily living (ADLs). Mobile phone users are becoming more accepting of sharing data captured by various built-in sensors. Sounds detected by smartphones are processed in this work. We...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10098573/ https://www.ncbi.nlm.nih.gov/pubmed/37050786 http://dx.doi.org/10.3390/s23073726 |
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author | Wu, Jiaxuan Feng, Yunfei Chang, Carl K. |
author_facet | Wu, Jiaxuan Feng, Yunfei Chang, Carl K. |
author_sort | Wu, Jiaxuan |
collection | PubMed |
description | One of the key objectives in developing IoT applications is to automatically detect and identify human activities of daily living (ADLs). Mobile phone users are becoming more accepting of sharing data captured by various built-in sensors. Sounds detected by smartphones are processed in this work. We present a hierarchical identification system to recognize ADLs by detecting and identifying certain sounds taking place in a complex audio situation (AS). Three major categories of sound are discriminated in terms of signal duration. These are persistent background noise (PBN), non-impulsive long sounds (NILS), and impulsive sound (IS). We first analyze audio signals in a situation-aware manner and then map the sounds of daily living (SDLs) to ADLs. A new hierarchical audible event (AE) recognition approach is proposed that classifies atomic audible actions (AAs), then computes pre-classified portions of atomic AAs energy in one AE session, and finally marks the maximum-likelihood ADL label as the outcome. Our experiments demonstrate that the proposed hierarchical methodology is effective in recognizing SDLs and, thus, also in detecting ADLs with a remarkable performance for other known baseline systems. |
format | Online Article Text |
id | pubmed-10098573 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-100985732023-04-14 Sound of Daily Living Identification Based on Hierarchical Situation Audition Wu, Jiaxuan Feng, Yunfei Chang, Carl K. Sensors (Basel) Article One of the key objectives in developing IoT applications is to automatically detect and identify human activities of daily living (ADLs). Mobile phone users are becoming more accepting of sharing data captured by various built-in sensors. Sounds detected by smartphones are processed in this work. We present a hierarchical identification system to recognize ADLs by detecting and identifying certain sounds taking place in a complex audio situation (AS). Three major categories of sound are discriminated in terms of signal duration. These are persistent background noise (PBN), non-impulsive long sounds (NILS), and impulsive sound (IS). We first analyze audio signals in a situation-aware manner and then map the sounds of daily living (SDLs) to ADLs. A new hierarchical audible event (AE) recognition approach is proposed that classifies atomic audible actions (AAs), then computes pre-classified portions of atomic AAs energy in one AE session, and finally marks the maximum-likelihood ADL label as the outcome. Our experiments demonstrate that the proposed hierarchical methodology is effective in recognizing SDLs and, thus, also in detecting ADLs with a remarkable performance for other known baseline systems. MDPI 2023-04-04 /pmc/articles/PMC10098573/ /pubmed/37050786 http://dx.doi.org/10.3390/s23073726 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Wu, Jiaxuan Feng, Yunfei Chang, Carl K. Sound of Daily Living Identification Based on Hierarchical Situation Audition |
title | Sound of Daily Living Identification Based on Hierarchical Situation Audition |
title_full | Sound of Daily Living Identification Based on Hierarchical Situation Audition |
title_fullStr | Sound of Daily Living Identification Based on Hierarchical Situation Audition |
title_full_unstemmed | Sound of Daily Living Identification Based on Hierarchical Situation Audition |
title_short | Sound of Daily Living Identification Based on Hierarchical Situation Audition |
title_sort | sound of daily living identification based on hierarchical situation audition |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10098573/ https://www.ncbi.nlm.nih.gov/pubmed/37050786 http://dx.doi.org/10.3390/s23073726 |
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