Publication date: Oct 19, 2024
The COVID-19 pandemic has challenged the current paradigm of clinical and community-based disease detection. We present a multimodal wearable sensor system paired with a two-minute, movement-based activity sequence that successfully captures a snapshot of physiological data (including cardiac, respiratory, temperature, and percent oxygen saturation). We conducted a large, multi-site trial of this technology across India from June 2021 to April 2022 amidst the COVID-19 pandemic (Clinical trial registry name: International Validation of Wearable Sensor to Monitor COVID-19 Like Signs and Symptoms; NCT05334680; initial release: 04/15/2022). An Extreme Gradient Boosting algorithm was trained to discriminate between COVID-19 infected individuals (n = 295) and COVID-19 negative healthy controls (n = 172) and achieved an F1-Score of 0. 80 (95% CI = [0. 79, 0. 81]). SHAP values were mapped to visualize feature importance and directionality, yielding engineered features from core temperature, cough, and lung sounds as highly important. The results demonstrated potential for data-driven wearable sensor technology for remote preliminary screening, highlighting a fundamental pivot from continuous to snapshot monitoring of cardiorespiratory illnesses.
Open Access PDF
| Concepts | Keywords |
|---|---|
| April | Based |
| Cardiorespiratory | Cardiorespiratory |
| F1 | Clinical |
| Healthy | Covid |
| Detect | |
| Feasibility | |
| India | |
| Pandemic | |
| Sensor | |
| Sensors | |
| Snapshot | |
| Temperature | |
| Testing | |
| Trial | |
| Wearable |