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.

COVID-19 大流行挑战了当前基于临床和社区的疾病检测范式。我们提出了一个多模式可穿戴传感器系统，该系统与两分钟的基于运动的活动序列配对，可成功捕获生理数据（包括心脏、呼吸、体温和血氧饱和度百分比）的快照。在 COVID-19 大流行期间，我们于 2021 年 6 月至 2022 年 4 月在印度对这项技术进行了大型多中心试验（临床试验注册名称：可穿戴传感器监测 COVID-19 样体征和症状的国际验证;NCT05334680;初始版本：2022 年 4 月 15 日）。训练极端梯度提升算法来区分 COVID-19 感染个体 （n = 295） 和 COVID-19 阴性健康对照 （n = 172），并取得了 0.80 的 F1 评分 （95% CI = [0.79， 0.81]）。对 SHAP 值进行映射以可视化特征重要性和方向性，从而产生来自核心温度、咳嗽和肺音的工程特征非常重要。结果显示了数据驱动的可穿戴传感器技术在远程初步筛查中的潜力，突出了从心肺疾病连续监测到快照监测的基本转变。