Cargando…
Ambient and Wearable Sensor Technologies for Energy Expenditure Quantification of Ageing Adults
COVID-19 has affected daily life in unprecedented ways, with dramatic changes in mental health, sleep time and level of physical activity. These changes have been especially relevant in the elderly population, with important health-related consequences. In this work, two different sensor technologie...
Autores principales: | , , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9269397/ https://www.ncbi.nlm.nih.gov/pubmed/35808387 http://dx.doi.org/10.3390/s22134893 |
_version_ | 1784744226351742976 |
---|---|
author | Leone, Alessandro Rescio, Gabriele Diraco, Giovanni Manni, Andrea Siciliano, Pietro Caroppo, Andrea |
author_facet | Leone, Alessandro Rescio, Gabriele Diraco, Giovanni Manni, Andrea Siciliano, Pietro Caroppo, Andrea |
author_sort | Leone, Alessandro |
collection | PubMed |
description | COVID-19 has affected daily life in unprecedented ways, with dramatic changes in mental health, sleep time and level of physical activity. These changes have been especially relevant in the elderly population, with important health-related consequences. In this work, two different sensor technologies were used to quantify the energy expenditure of ageing adults. To this end, a technological platform based on Raspberry Pi 4, as an elaboration unit, was designed and implemented. It integrates an ambient sensor node, a wearable sensor node and a coordinator node that uses the information provided by the two sensor technologies in a combined manner. Ambient and wearable sensors are used for the real-time recognition of four human postures (standing, sitting, bending and lying down), walking activity and for energy expenditure quantification. An important first aim of this work was to realize a platform with a high level of user acceptability. In fact, through the use of two unobtrusive sensors and a low-cost processing unit, the solution is easily accessible and usable in the domestic environment; moreover, it is versatile since it can be used by end-users who accept being monitored by a specific sensor. Another added value of the platform is the ability to abstract from sensing technologies, as the use of human posture and walking activity for energy expenditure quantification enables the integration of a wide set of devices, provided that they can reproduce the same set of features. The obtained results showed the ability of the proposed platform to automatically quantify energy expenditure, both with each sensing technology and with the combined version. Specifically, for posture and walking activity classification, an average accuracy of 93.8% and 93.3% was obtained, respectively, with the wearable and ambient sensor, whereas an improvement of approximately 4% was reached using data fusion. Consequently, the estimated energy expenditure quantification always had a relative error of less than 3.2% for each end-user involved in the experimentation stage, classifying the high level information (postures and walking activities) with the combined version of the platform, justifying the proposed overall architecture from a hardware and software point of view. |
format | Online Article Text |
id | pubmed-9269397 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-92693972022-07-09 Ambient and Wearable Sensor Technologies for Energy Expenditure Quantification of Ageing Adults Leone, Alessandro Rescio, Gabriele Diraco, Giovanni Manni, Andrea Siciliano, Pietro Caroppo, Andrea Sensors (Basel) Article COVID-19 has affected daily life in unprecedented ways, with dramatic changes in mental health, sleep time and level of physical activity. These changes have been especially relevant in the elderly population, with important health-related consequences. In this work, two different sensor technologies were used to quantify the energy expenditure of ageing adults. To this end, a technological platform based on Raspberry Pi 4, as an elaboration unit, was designed and implemented. It integrates an ambient sensor node, a wearable sensor node and a coordinator node that uses the information provided by the two sensor technologies in a combined manner. Ambient and wearable sensors are used for the real-time recognition of four human postures (standing, sitting, bending and lying down), walking activity and for energy expenditure quantification. An important first aim of this work was to realize a platform with a high level of user acceptability. In fact, through the use of two unobtrusive sensors and a low-cost processing unit, the solution is easily accessible and usable in the domestic environment; moreover, it is versatile since it can be used by end-users who accept being monitored by a specific sensor. Another added value of the platform is the ability to abstract from sensing technologies, as the use of human posture and walking activity for energy expenditure quantification enables the integration of a wide set of devices, provided that they can reproduce the same set of features. The obtained results showed the ability of the proposed platform to automatically quantify energy expenditure, both with each sensing technology and with the combined version. Specifically, for posture and walking activity classification, an average accuracy of 93.8% and 93.3% was obtained, respectively, with the wearable and ambient sensor, whereas an improvement of approximately 4% was reached using data fusion. Consequently, the estimated energy expenditure quantification always had a relative error of less than 3.2% for each end-user involved in the experimentation stage, classifying the high level information (postures and walking activities) with the combined version of the platform, justifying the proposed overall architecture from a hardware and software point of view. MDPI 2022-06-29 /pmc/articles/PMC9269397/ /pubmed/35808387 http://dx.doi.org/10.3390/s22134893 Text en © 2022 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 Leone, Alessandro Rescio, Gabriele Diraco, Giovanni Manni, Andrea Siciliano, Pietro Caroppo, Andrea Ambient and Wearable Sensor Technologies for Energy Expenditure Quantification of Ageing Adults |
title | Ambient and Wearable Sensor Technologies for Energy Expenditure Quantification of Ageing Adults |
title_full | Ambient and Wearable Sensor Technologies for Energy Expenditure Quantification of Ageing Adults |
title_fullStr | Ambient and Wearable Sensor Technologies for Energy Expenditure Quantification of Ageing Adults |
title_full_unstemmed | Ambient and Wearable Sensor Technologies for Energy Expenditure Quantification of Ageing Adults |
title_short | Ambient and Wearable Sensor Technologies for Energy Expenditure Quantification of Ageing Adults |
title_sort | ambient and wearable sensor technologies for energy expenditure quantification of ageing adults |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9269397/ https://www.ncbi.nlm.nih.gov/pubmed/35808387 http://dx.doi.org/10.3390/s22134893 |
work_keys_str_mv | AT leonealessandro ambientandwearablesensortechnologiesforenergyexpenditurequantificationofageingadults AT resciogabriele ambientandwearablesensortechnologiesforenergyexpenditurequantificationofageingadults AT diracogiovanni ambientandwearablesensortechnologiesforenergyexpenditurequantificationofageingadults AT manniandrea ambientandwearablesensortechnologiesforenergyexpenditurequantificationofageingadults AT sicilianopietro ambientandwearablesensortechnologiesforenergyexpenditurequantificationofageingadults AT caroppoandrea ambientandwearablesensortechnologiesforenergyexpenditurequantificationofageingadults |