Pre-Emption of Affliction Severity Using HRV Measurements from a Smart Wearable; Case-Study on SARS-Cov-2 Symptoms
Smart wristbands and watches have become an important accessory to fitness, but their application to healthcare is still in a fledgling state. Their long-term wear facilitates extensive data collection and evolving sensitivity of smart wristbands allows them to read various body vitals. In this pape...
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-12-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/20/24/7068 |
| _version_ | 1797545211805564928 |
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| author | Gatha Varma Ritu Chauhan Madhusudan Singh Dhananjay Singh |
| author_facet | Gatha Varma Ritu Chauhan Madhusudan Singh Dhananjay Singh |
| author_sort | Gatha Varma |
| collection | DOAJ |
| description | Smart wristbands and watches have become an important accessory to fitness, but their application to healthcare is still in a fledgling state. Their long-term wear facilitates extensive data collection and evolving sensitivity of smart wristbands allows them to read various body vitals. In this paper, we hypothesized the use of heart rate variability (HRV) measurements to drive an algorithm that can pre-empt the onset or worsening of an affliction. Due to its significance during the time of the study, SARS-Cov-2 was taken as the case study, and a hidden Markov model (HMM) was trained over its observed symptoms. The data used for the analysis was the outcome of a study hosted by Welltory. It involved the collection of SAR-Cov-2 symptoms and reading of body vitals using Apple Watch, Fitbit, and Garmin smart bands. The internal states of the HMM were made up of the absence and presence of a consistent decline in standard deviation of NN intervals (SSDN), the root mean square of the successive differences (rMSSD) in R-R intervals, and low frequency (LF), high frequency (HF), and very low frequency (VLF) components of the HRV measurements. The emission probabilities of the trained HMM instance confirmed that the onset or worsening of the symptoms had a higher probability if the HRV components displayed a consistent decline state. The results were further confirmed through the generation of probable hidden states sequences using the Viterbi algorithm. The ability to pre-empt the exigent state of an affliction would not only lower the chances of complications and mortality but may also help in curbing its spread through intelligence-backed decisions. |
| first_indexed | 2024-03-10T14:12:16Z |
| format | Article |
| id | doaj.art-fc5a219f98c041beb498a8bf8a49db84 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-10T14:12:16Z |
| publishDate | 2020-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-fc5a219f98c041beb498a8bf8a49db842023-11-21T00:09:26ZengMDPI AGSensors1424-82202020-12-012024706810.3390/s20247068Pre-Emption of Affliction Severity Using HRV Measurements from a Smart Wearable; Case-Study on SARS-Cov-2 SymptomsGatha Varma0Ritu Chauhan1Madhusudan Singh2Dhananjay Singh3Amity Institute of Information Technology, Amity University, Noida 201313, IndiaCenter for Computational Biology and Bioinformatics, Amity University, Noida 201313, IndiaEndicott College of International Studies, Woosong University, Daejeon 34606, KoreaDepartment of Electronics Engineering, Hankuk University of Foreign Studies Seoul, Yongin 17035, KoreaSmart wristbands and watches have become an important accessory to fitness, but their application to healthcare is still in a fledgling state. Their long-term wear facilitates extensive data collection and evolving sensitivity of smart wristbands allows them to read various body vitals. In this paper, we hypothesized the use of heart rate variability (HRV) measurements to drive an algorithm that can pre-empt the onset or worsening of an affliction. Due to its significance during the time of the study, SARS-Cov-2 was taken as the case study, and a hidden Markov model (HMM) was trained over its observed symptoms. The data used for the analysis was the outcome of a study hosted by Welltory. It involved the collection of SAR-Cov-2 symptoms and reading of body vitals using Apple Watch, Fitbit, and Garmin smart bands. The internal states of the HMM were made up of the absence and presence of a consistent decline in standard deviation of NN intervals (SSDN), the root mean square of the successive differences (rMSSD) in R-R intervals, and low frequency (LF), high frequency (HF), and very low frequency (VLF) components of the HRV measurements. The emission probabilities of the trained HMM instance confirmed that the onset or worsening of the symptoms had a higher probability if the HRV components displayed a consistent decline state. The results were further confirmed through the generation of probable hidden states sequences using the Viterbi algorithm. The ability to pre-empt the exigent state of an affliction would not only lower the chances of complications and mortality but may also help in curbing its spread through intelligence-backed decisions.https://www.mdpi.com/1424-8220/20/24/7068smart wearablesmart healthheart rate variabilityonset detectionhidden markov modelSARS-Cov-2 |
| spellingShingle | Gatha Varma Ritu Chauhan Madhusudan Singh Dhananjay Singh Pre-Emption of Affliction Severity Using HRV Measurements from a Smart Wearable; Case-Study on SARS-Cov-2 Symptoms Sensors smart wearable smart health heart rate variability onset detection hidden markov model SARS-Cov-2 |
| title | Pre-Emption of Affliction Severity Using HRV Measurements from a Smart Wearable; Case-Study on SARS-Cov-2 Symptoms |
| title_full | Pre-Emption of Affliction Severity Using HRV Measurements from a Smart Wearable; Case-Study on SARS-Cov-2 Symptoms |
| title_fullStr | Pre-Emption of Affliction Severity Using HRV Measurements from a Smart Wearable; Case-Study on SARS-Cov-2 Symptoms |
| title_full_unstemmed | Pre-Emption of Affliction Severity Using HRV Measurements from a Smart Wearable; Case-Study on SARS-Cov-2 Symptoms |
| title_short | Pre-Emption of Affliction Severity Using HRV Measurements from a Smart Wearable; Case-Study on SARS-Cov-2 Symptoms |
| title_sort | pre emption of affliction severity using hrv measurements from a smart wearable case study on sars cov 2 symptoms |
| topic | smart wearable smart health heart rate variability onset detection hidden markov model SARS-Cov-2 |
| url | https://www.mdpi.com/1424-8220/20/24/7068 |
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