Towards Human Stress and Activity Recognition: A Review and a First Approach Based on Low-Cost Wearables
Detecting stress when performing physical activities is an interesting field that has received relatively little research interest to date. In this paper, we took a first step towards redressing this, through a comprehensive review and the design of a low-cost body area network (BAN) made of a set o...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-01-01
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| Series: | Electronics |
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| Online Access: | https://www.mdpi.com/2079-9292/11/1/155 |
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| author | Juan Antonio Castro-García Alberto Jesús Molina-Cantero Isabel María Gómez-González Sergio Lafuente-Arroyo Manuel Merino-Monge |
| author_facet | Juan Antonio Castro-García Alberto Jesús Molina-Cantero Isabel María Gómez-González Sergio Lafuente-Arroyo Manuel Merino-Monge |
| author_sort | Juan Antonio Castro-García |
| collection | DOAJ |
| description | Detecting stress when performing physical activities is an interesting field that has received relatively little research interest to date. In this paper, we took a first step towards redressing this, through a comprehensive review and the design of a low-cost body area network (BAN) made of a set of wearables that allow physiological signals and human movements to be captured simultaneously. We used four different wearables: OpenBCI and three other open-hardware custom-made designs that communicate via bluetooth low energy (BLE) to an external computer—following the edge-computingconcept—hosting applications for data synchronization and storage. We obtained a large number of physiological signals (electroencephalography (EEG), electrocardiography (ECG), breathing rate (BR), electrodermal activity (EDA), and skin temperature (ST)) with which we analyzed internal states in general, but with a focus on stress. The findings show the reliability and feasibility of the proposed body area network (BAN) according to battery lifetime (greater than 15 h), packet loss rate (0% for our custom-made designs), and signal quality (signal-noise ratio (SNR) of 9.8 dB for the ECG circuit, and 61.6 dB for the EDA). Moreover, we conducted a preliminary experiment to gauge the main ECG features for stress detection during rest. |
| first_indexed | 2024-03-10T03:44:14Z |
| format | Article |
| id | doaj.art-37df1966f01445a5ab30f7ba1235453e |
| institution | Directory Open Access Journal |
| issn | 2079-9292 |
| language | English |
| last_indexed | 2024-03-10T03:44:14Z |
| publishDate | 2022-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Electronics |
| spelling | doaj.art-37df1966f01445a5ab30f7ba1235453e2023-11-23T11:23:43ZengMDPI AGElectronics2079-92922022-01-0111115510.3390/electronics11010155Towards Human Stress and Activity Recognition: A Review and a First Approach Based on Low-Cost WearablesJuan Antonio Castro-García0Alberto Jesús Molina-Cantero1Isabel María Gómez-González2Sergio Lafuente-Arroyo3Manuel Merino-Monge4Departamento de Tecnología Electrónica, E.T.S.I. Informática, Universidad de Sevilla, 41012 Sevilla, SpainDepartamento de Tecnología Electrónica, E.T.S.I. Informática, Universidad de Sevilla, 41012 Sevilla, SpainDepartamento de Tecnología Electrónica, E.T.S.I. Informática, Universidad de Sevilla, 41012 Sevilla, SpainDepartamento de Teoría de la Señal y Comunicaciones, Escuela Politécnica Superior, Universidad de Alcalá de Henares, 28801 Alcalá de Henares, SpainDepartamento de Tecnología Electrónica, E.T.S.I. Informática, Universidad de Sevilla, 41012 Sevilla, SpainDetecting stress when performing physical activities is an interesting field that has received relatively little research interest to date. In this paper, we took a first step towards redressing this, through a comprehensive review and the design of a low-cost body area network (BAN) made of a set of wearables that allow physiological signals and human movements to be captured simultaneously. We used four different wearables: OpenBCI and three other open-hardware custom-made designs that communicate via bluetooth low energy (BLE) to an external computer—following the edge-computingconcept—hosting applications for data synchronization and storage. We obtained a large number of physiological signals (electroencephalography (EEG), electrocardiography (ECG), breathing rate (BR), electrodermal activity (EDA), and skin temperature (ST)) with which we analyzed internal states in general, but with a focus on stress. The findings show the reliability and feasibility of the proposed body area network (BAN) according to battery lifetime (greater than 15 h), packet loss rate (0% for our custom-made designs), and signal quality (signal-noise ratio (SNR) of 9.8 dB for the ECG circuit, and 61.6 dB for the EDA). Moreover, we conducted a preliminary experiment to gauge the main ECG features for stress detection during rest.https://www.mdpi.com/2079-9292/11/1/155wearableemotionstresshuman activity recognitionEDAECG |
| spellingShingle | Juan Antonio Castro-García Alberto Jesús Molina-Cantero Isabel María Gómez-González Sergio Lafuente-Arroyo Manuel Merino-Monge Towards Human Stress and Activity Recognition: A Review and a First Approach Based on Low-Cost Wearables Electronics wearable emotion stress human activity recognition EDA ECG |
| title | Towards Human Stress and Activity Recognition: A Review and a First Approach Based on Low-Cost Wearables |
| title_full | Towards Human Stress and Activity Recognition: A Review and a First Approach Based on Low-Cost Wearables |
| title_fullStr | Towards Human Stress and Activity Recognition: A Review and a First Approach Based on Low-Cost Wearables |
| title_full_unstemmed | Towards Human Stress and Activity Recognition: A Review and a First Approach Based on Low-Cost Wearables |
| title_short | Towards Human Stress and Activity Recognition: A Review and a First Approach Based on Low-Cost Wearables |
| title_sort | towards human stress and activity recognition a review and a first approach based on low cost wearables |
| topic | wearable emotion stress human activity recognition EDA ECG |
| url | https://www.mdpi.com/2079-9292/11/1/155 |
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