Wearable Devices, Smartphones, and Interpretable Artificial Intelligence in Combating COVID-19
Abstract
:1. Introduction
2. Related Work
- Different prediction techniques were proposed, namely two independent prediction models: one for the HRV measures and the other is for analyzing the daily textual status of users as reported by them using NLP techniques.
- A model interpretation based on the LIME framework was introduced to better understand each feature’s contribution to the final decision.
- An accuracy of 83.34 ± 1.68% with 0.91, 0.88, and 0.89 precision, recall, and F-score, respectively, were obtained in predicting the infection two days before the onset of the symptoms.
- The decision fusion technique between the biometric model decision and the non-biometric model decision (i.e., feelings and reported status) improved the accuracy and the precision of the obtained results.
3. Participants, Dataset, and Methods
3.1. Participants
3.2. Dataset Description
3.3. Methods
- The data acquisition: The physiological signals (i.e., HRV) were collected from participants through the Welltory application over a continuous period. The collected data included textual logs as a part of ‘participants’ daily reports on the application. The text’s tags comprised short words like tired, fever, fatigue, back to active life, and other short messages.
- The preprocessing: This step included cleaning the data that do not conform with the required standards, such as having data before and after the onset, having correct onset dates, and having sufficient daily logs. The second step was to normalize the data due to the variable nature of HRV among participants by using the direct max-min normalization as appears in the following formula:
- Exploratory data analysis (EDA): This aims to visualize and test the data distributions and patterns before introducing them to the AI models.
- Feature extraction and selection: In this stage, domain knowledge and data-driven approaches are utilized. In the domain knowledge, we selected the HRV measures among other vital signs mentioned in Table 1 due to many reasons: (a) the well-established connections in the literature between HRV features and pathological changes including inflammatory onsets [40,41], (b) the timely manner response of some HRV features such as the standard deviation of NN intervals (SDNN) and the root mean square of successive differences between normal heartbeats (RMSSD) [42]. Nonetheless, HRV is still nonspecific to certain inflammatory infections like the COVID-19. Thus, we fused the model with non-physiological complementary data like the textual information tweeted by participants. From the initial screening of the textual tags posted on the application, we noticed a recurrent pattern of words expressed among those who started to feel unwell due to the COVID-19 infection (before the actual onset of symptoms). This additional source of information would be useless with asymptomatic patients as they would not report any significant feelings. Thus, HRV features remain the primary source in our work. Examples of HRV time-domain and frequency-domain are listed below:
- (a)
- The time-domain features:
- Beat per minute (BPM).
- Meanrr: The mean between two RR intervals.
- Mxdmnn: The difference between the maximum and minimum RR intervals.
- SDNN: The standard deviation of all the normal-to-normal RR intervals.
- RMSDD: The root mean square of successive differences between each heartbeat.
- pNN50: The mean number of times the changes in the normal-to-normal intervals exceed 50 ms.
- (b)
- The frequency-domain features:
- HF: The high frequency of the heart rate represents the activity in the 0.15–0.40 Hz range.
- LF: The low frequency of the heart rate represents the activity in the 0.04–0.15 Hz range.
- LF/HF: The ratio between the low and high frequencies.
4. Results and Discussion
4.1. Features Interpretation
4.2. Heart Signals and Feelings Classification Results and Their Interpretations
4.3. Daily Textual Logs Classification Results
5. Threats to Validity
6. Conclusions and Future Work
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Source | Data | Details |
---|---|---|
Welltory Mobile Application | HRV | Daily readings of beats per minute and HRV features such as SDNN, RMSSD, pNN50, COVID-19 onset date. Moreover, textual tags were provided by patients about their status daily. |
Blood pressure | Diastolic and systolic readings, functional change index. | |
Heart Rate | Beats per minute readings, and a binary answer (whether heart rate was measured at rest). | |
Surveys | COVID symptoms such as cough assessment, fever, breath shortness, fatigue, etc. | |
Wearables | Physiological metrics and fitness data | Resting heart rate, heart rate, oxygen saturation, steps count, walking distances. |
Sleep data | Sleep begins and ends, sleep duration, light, and deep sleep information. |
Modality | Model | Accuracy | Precision | Recall | F1-Score |
---|---|---|---|---|---|
HRV features + feeling assessment | SVM | 83.34 ± 1.68% | 0.91 | 0.88 | 0.89 |
KNN | 83.06 ± 1.99% | 0.80 | 0.80 | 0.80 | |
Decision Tree | 74.28 ± 0.613% | 0.80 | 0.79 | 0.79 | |
Logistic Regression | 78.93 ± 2.34% | 0.80 | 0.80 | 0.79 | |
HRV features only | SVM | 78.85 ± 3.04% | 0.79 | 0.81 | 0.78 |
KNN | 80.17 ± 0.28% | 0.75 | 0.75 | 0.75 | |
Decision Tree | 76.30 ± 0.98% | 0.71 | 0.71 | 0.71 | |
Logistic Regression | 79.75 ± 4.31% | 0.79 | 0.79 | 0.79 | |
Feeling assessment only | SVM | 65.38 ± 8.21% | 0.66 | 0.67 | 0.65 |
KNN | 41.74 ± 9.68% | 0.46 | 0.47 | 0.41 | |
Decision Tree | 50.58 ± 7.03% | 0.57 | 0.55 | 0.53 | |
Logistic Regression | 58.68 ± 11.28% | 0.27 | 0.50 | 0.35 |
Epoch | Train Loss | Valid Loss | Accuracy |
---|---|---|---|
0 | 0.362183 | 0.264590 | 0.925373 |
1 | 0.355476 | 1.288716 | 0.686567 |
2 | 0.461005 | 0.675994 | 0.791045 |
3 | 0.447026 | 0.774474 | 0.805970 |
4 | 0.420781 | 0.499611 | 0.805970 |
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Hijazi, H.; Abu Talib, M.; Hasasneh, A.; Bou Nassif, A.; Ahmed, N.; Nasir, Q. Wearable Devices, Smartphones, and Interpretable Artificial Intelligence in Combating COVID-19. Sensors 2021, 21, 8424. https://doi.org/10.3390/s21248424
Hijazi H, Abu Talib M, Hasasneh A, Bou Nassif A, Ahmed N, Nasir Q. Wearable Devices, Smartphones, and Interpretable Artificial Intelligence in Combating COVID-19. Sensors. 2021; 21(24):8424. https://doi.org/10.3390/s21248424
Chicago/Turabian StyleHijazi, Haytham, Manar Abu Talib, Ahmad Hasasneh, Ali Bou Nassif, Nafisa Ahmed, and Qassim Nasir. 2021. "Wearable Devices, Smartphones, and Interpretable Artificial Intelligence in Combating COVID-19" Sensors 21, no. 24: 8424. https://doi.org/10.3390/s21248424
APA StyleHijazi, H., Abu Talib, M., Hasasneh, A., Bou Nassif, A., Ahmed, N., & Nasir, Q. (2021). Wearable Devices, Smartphones, and Interpretable Artificial Intelligence in Combating COVID-19. Sensors, 21(24), 8424. https://doi.org/10.3390/s21248424