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Sensor Fusion for Recognition of Activities of Daily Living
Activity of daily living (ADL) is a significant predictor of the independence and functional capabilities of an individual. Measurements of ADLs help to indicate one’s health status and capabilities of quality living. Recently, the most common ways to capture ADL data are far from automation, includ...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2018
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6263431/ https://www.ncbi.nlm.nih.gov/pubmed/30463199 http://dx.doi.org/10.3390/s18114029 |
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author | Wu, Jiaxuan Feng, Yunfei Sun, Peng |
author_facet | Wu, Jiaxuan Feng, Yunfei Sun, Peng |
author_sort | Wu, Jiaxuan |
collection | PubMed |
description | Activity of daily living (ADL) is a significant predictor of the independence and functional capabilities of an individual. Measurements of ADLs help to indicate one’s health status and capabilities of quality living. Recently, the most common ways to capture ADL data are far from automation, including a costly 24/7 observation by a designated caregiver, self-reporting by the user laboriously, or filling out a written ADL survey. Fortunately, ubiquitous sensors exist in our surroundings and on electronic devices in the Internet of Things (IoT) era. We proposed the ADL Recognition System that utilizes the sensor data from a single point of contact, such as smartphones, and conducts time-series sensor fusion processing. Raw data is collected from the ADL Recorder App constantly running on a user’s smartphone with multiple embedded sensors, including the microphone, Wi-Fi scan module, heading orientation of the device, light proximity, step detector, accelerometer, gyroscope, magnetometer, etc. Key technologies in this research cover audio processing, Wi-Fi indoor positioning, proximity sensing localization, and time-series sensor data fusion. By merging the information of multiple sensors, with a time-series error correction technique, the ADL Recognition System is able to accurately profile a person’s ADLs and discover his life patterns. This paper is particularly concerned with the care for the older adults who live independently. |
format | Online Article Text |
id | pubmed-6263431 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-62634312018-12-12 Sensor Fusion for Recognition of Activities of Daily Living Wu, Jiaxuan Feng, Yunfei Sun, Peng Sensors (Basel) Article Activity of daily living (ADL) is a significant predictor of the independence and functional capabilities of an individual. Measurements of ADLs help to indicate one’s health status and capabilities of quality living. Recently, the most common ways to capture ADL data are far from automation, including a costly 24/7 observation by a designated caregiver, self-reporting by the user laboriously, or filling out a written ADL survey. Fortunately, ubiquitous sensors exist in our surroundings and on electronic devices in the Internet of Things (IoT) era. We proposed the ADL Recognition System that utilizes the sensor data from a single point of contact, such as smartphones, and conducts time-series sensor fusion processing. Raw data is collected from the ADL Recorder App constantly running on a user’s smartphone with multiple embedded sensors, including the microphone, Wi-Fi scan module, heading orientation of the device, light proximity, step detector, accelerometer, gyroscope, magnetometer, etc. Key technologies in this research cover audio processing, Wi-Fi indoor positioning, proximity sensing localization, and time-series sensor data fusion. By merging the information of multiple sensors, with a time-series error correction technique, the ADL Recognition System is able to accurately profile a person’s ADLs and discover his life patterns. This paper is particularly concerned with the care for the older adults who live independently. MDPI 2018-11-19 /pmc/articles/PMC6263431/ /pubmed/30463199 http://dx.doi.org/10.3390/s18114029 Text en © 2018 by the authors. 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 (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Wu, Jiaxuan Feng, Yunfei Sun, Peng Sensor Fusion for Recognition of Activities of Daily Living |
title | Sensor Fusion for Recognition of Activities of Daily Living |
title_full | Sensor Fusion for Recognition of Activities of Daily Living |
title_fullStr | Sensor Fusion for Recognition of Activities of Daily Living |
title_full_unstemmed | Sensor Fusion for Recognition of Activities of Daily Living |
title_short | Sensor Fusion for Recognition of Activities of Daily Living |
title_sort | sensor fusion for recognition of activities of daily living |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6263431/ https://www.ncbi.nlm.nih.gov/pubmed/30463199 http://dx.doi.org/10.3390/s18114029 |
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