Towards assessing reliability of next‐generation Internet of Things dashboard for anxiety risk classification
<p dir="ltr">The ubiquitous Internet of Things (IoT) and sensing technologies provide an interestingopportunity of remote health monitoring and disease risk categorisation of populations.An end‐to‐end architecture is proposed to facilitate real‐time digital dashboards tovisualise gen...
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2024
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| author | Shama Siddiqui (10753719) |
| author2 | Anwar Ahmed Khan (10753753) Farid Nait Abdesselam (20469569) Shamsul Arfeen Qasmi (20469572) Adnan Akhunzada (20151648) Indrakshi Dey (8483415) |
| author2_role | author author author author author |
| author_facet | Shama Siddiqui (10753719) Anwar Ahmed Khan (10753753) Farid Nait Abdesselam (20469569) Shamsul Arfeen Qasmi (20469572) Adnan Akhunzada (20151648) Indrakshi Dey (8483415) |
| author_role | author |
| dc.creator.none.fl_str_mv | Shama Siddiqui (10753719) Anwar Ahmed Khan (10753753) Farid Nait Abdesselam (20469569) Shamsul Arfeen Qasmi (20469572) Adnan Akhunzada (20151648) Indrakshi Dey (8483415) |
| dc.date.none.fl_str_mv | 2024-12-01T00:00:00Z |
| dc.identifier.none.fl_str_mv | 10.1049/wss2.12090 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/journal_contribution/Towards_assessing_reliability_of_next_generation_Internet_of_Things_dashboard_for_anxiety_risk_classification/28087175 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Engineering Biomedical engineering Electronics, sensors and digital hardware Health sciences Health services and systems Information and computing sciences Data management and data science Distributed computing and systems software Internet of Things Real‐time systems Sensors |
| dc.title.none.fl_str_mv | Towards assessing reliability of next‐generation Internet of Things dashboard for anxiety risk classification |
| dc.type.none.fl_str_mv | Text Journal contribution info:eu-repo/semantics/publishedVersion text contribution to journal |
| description | <p dir="ltr">The ubiquitous Internet of Things (IoT) and sensing technologies provide an interestingopportunity of remote health monitoring and disease risk categorisation of populations.An end‐to‐end architecture is proposed to facilitate real‐time digital dashboards tovisualise general anxiety risks of patients, especially during a pandemic, such as COVID‐19. To collect physiological data related to anxiety (heart rate, blood pressure, andsaturation of peripheral oxygen [SPO<sub>2</sub>]) and communicate them to a centralised dash-board, dubbed ‘X‐DASH’, a hardware prototype of the proposed architecture wasdeveloped using Node‐MCU and diverse sensors. The dashboard presents a smart cat-egorisation of users' data, assessing their anxiety risks, to provide medical professionalsand state authorities a clear visualisation of health risks in populations belonging todifferent regions. We categorised the risk levels as Normal, Mild, Moderate, Elevated,Severe, and Extreme, based on the collected physiological data and pre‐defined thresholdvalues. The developed hardware prototype in this work was used to collect data fromabout 500 patients present at cardiac clinic of a leading general hospital in Karachi(Pakistan) and the anxiety risk levels were assigned based on pre‐defined threshold values.To validate the reliability of the X‐DASH, the personal physician of each patient wasconsulted and was requested to identify each of their anxiety risk levels. It was found thatthe risk levels suggested by X‐DASH, (based on data of heart rate, blood pressure, and SPO<sub>2</sub> were more than 90% accurate when compared with diagnoses of physicians.Subsequently, packet loss, delay and network overhead for the platform was comparedwhen using MQTT, CoAP and Modbus. Although MQTT has shown higher delays, but itis still recommended due to having a higher reliability.</p><h2>Other Information</h2><p dir="ltr">Published in: IET Wireless Sensor Systems<br>License: <a href="http://creativecommons.org/licenses/by/4.0/" target="_blank">http://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://doi.org/10.1049/wss2.12090" target="_blank">https://doi.org/10.1049/wss2.12090</a></p> |
| eu_rights_str_mv | openAccess |
| id | Manara2_de60df7dfc9d68af8ee55e7bd41b20fa |
| identifier_str_mv | 10.1049/wss2.12090 |
| network_acronym_str | Manara2 |
| network_name_str | Manara2 |
| oai_identifier_str | oai:figshare.com:article/28087175 |
| publishDate | 2024 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | Towards assessing reliability of next‐generation Internet of Things dashboard for anxiety risk classificationShama Siddiqui (10753719)Anwar Ahmed Khan (10753753)Farid Nait Abdesselam (20469569)Shamsul Arfeen Qasmi (20469572)Adnan Akhunzada (20151648)Indrakshi Dey (8483415)EngineeringBiomedical engineeringElectronics, sensors and digital hardwareHealth sciencesHealth services and systemsInformation and computing sciencesData management and data scienceDistributed computing and systems softwareInternet of ThingsReal‐time systemsSensors<p dir="ltr">The ubiquitous Internet of Things (IoT) and sensing technologies provide an interestingopportunity of remote health monitoring and disease risk categorisation of populations.An end‐to‐end architecture is proposed to facilitate real‐time digital dashboards tovisualise general anxiety risks of patients, especially during a pandemic, such as COVID‐19. To collect physiological data related to anxiety (heart rate, blood pressure, andsaturation of peripheral oxygen [SPO<sub>2</sub>]) and communicate them to a centralised dash-board, dubbed ‘X‐DASH’, a hardware prototype of the proposed architecture wasdeveloped using Node‐MCU and diverse sensors. The dashboard presents a smart cat-egorisation of users' data, assessing their anxiety risks, to provide medical professionalsand state authorities a clear visualisation of health risks in populations belonging todifferent regions. We categorised the risk levels as Normal, Mild, Moderate, Elevated,Severe, and Extreme, based on the collected physiological data and pre‐defined thresholdvalues. The developed hardware prototype in this work was used to collect data fromabout 500 patients present at cardiac clinic of a leading general hospital in Karachi(Pakistan) and the anxiety risk levels were assigned based on pre‐defined threshold values.To validate the reliability of the X‐DASH, the personal physician of each patient wasconsulted and was requested to identify each of their anxiety risk levels. It was found thatthe risk levels suggested by X‐DASH, (based on data of heart rate, blood pressure, and SPO<sub>2</sub> were more than 90% accurate when compared with diagnoses of physicians.Subsequently, packet loss, delay and network overhead for the platform was comparedwhen using MQTT, CoAP and Modbus. Although MQTT has shown higher delays, but itis still recommended due to having a higher reliability.</p><h2>Other Information</h2><p dir="ltr">Published in: IET Wireless Sensor Systems<br>License: <a href="http://creativecommons.org/licenses/by/4.0/" target="_blank">http://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://doi.org/10.1049/wss2.12090" target="_blank">https://doi.org/10.1049/wss2.12090</a></p>2024-12-01T00:00:00ZTextJournal contributioninfo:eu-repo/semantics/publishedVersiontextcontribution to journal10.1049/wss2.12090https://figshare.com/articles/journal_contribution/Towards_assessing_reliability_of_next_generation_Internet_of_Things_dashboard_for_anxiety_risk_classification/28087175CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/280871752024-12-01T00:00:00Z |
| spellingShingle | Towards assessing reliability of next‐generation Internet of Things dashboard for anxiety risk classification Shama Siddiqui (10753719) Engineering Biomedical engineering Electronics, sensors and digital hardware Health sciences Health services and systems Information and computing sciences Data management and data science Distributed computing and systems software Internet of Things Real‐time systems Sensors |
| status_str | publishedVersion |
| title | Towards assessing reliability of next‐generation Internet of Things dashboard for anxiety risk classification |
| title_full | Towards assessing reliability of next‐generation Internet of Things dashboard for anxiety risk classification |
| title_fullStr | Towards assessing reliability of next‐generation Internet of Things dashboard for anxiety risk classification |
| title_full_unstemmed | Towards assessing reliability of next‐generation Internet of Things dashboard for anxiety risk classification |
| title_short | Towards assessing reliability of next‐generation Internet of Things dashboard for anxiety risk classification |
| title_sort | Towards assessing reliability of next‐generation Internet of Things dashboard for anxiety risk classification |
| topic | Engineering Biomedical engineering Electronics, sensors and digital hardware Health sciences Health services and systems Information and computing sciences Data management and data science Distributed computing and systems software Internet of Things Real‐time systems Sensors |