In the latest paper titled Learning from Large-Scale Wearable Device Data for Predicting Epidemics Trend of COVID-19 published by the special issue[1] from an scientific journal[2], Huami demonstrated the health management capabilities of wearable devices' and its essential role in early alerting of epidemic outbreaks and public health, providing new clues for establishing a large-scale epidemic surveillance system, and helping improve the efficiency of public health monitoring and prediction[3].
Huami: COVID-19 Epidemic Trend Prediction Model
This study[4] was supported by the Huami Corporation, a prediction model was established by using big data and artificial intelligence algorithms, which provides a new method for predicting epidemic trends for COVID-19.
Under the Huami Privacy Policy and data protections, researchers collected heart rate, physical activity, sleep, and other physiological data related to the above symptoms based on smart wearable devices. De-identified sensor data of about 1.3 million users who wore Huami devices from July 1, 2017, to April 8, 2020 were obtained according to appropriate security control. All the users were notified that their de-identified data could potentially be used for academic research[5].
Research found that, for every 1?C increase in human body temperature, heart rate increases by about 8.5 bpm[6]. Based on this, the increase in heart rate caused by fevers related to COVID-19 or influenza-like diseases can be used as a starting point for a method to detect physiological abnormalities.
Huami researchers considered an individual's resting heart rate at 1.5 standard deviations higher than the personal average for 5 consecutive days, and sleep duration not less than 0.5 standard deviations from the personal average as the criterion to determine an abnormality.
The prediction model's analysis results show that in the listed cities of Wuhan, Beijing, Shenzhen, Hefei, and Nanjing, there was a clear outbreak period in the infection rate prediction curve for each city which corresponded to the epidemic's outbreak in each city.
Taking Wuhan as an example, the infection rate predicted by the model peaked on January 28th, while the newly confirmed cases in Wuhan peaked at nearly 2,000 people on February 7. The predicted infection rate peak was 10 days earlier than the officially reported peak time.
Given the lag between COVID-19 infection and the emergence of symptoms and diagnosis, the model-derived results are also consistent with the results of a retrospective study on COVID-19 conducted by the Chinese Center for Disease Control[7].
More Efforts Towards COVID-19 and Health Management
Besides the academic research from Huami, Huami continued the efforts of Connect Health with Technology. The company has donated medical supplies and devices worth 11.5 million RMB during the coronavirus outbreak.
Amazfit, a self-brand of Huami, started to working on a transparent N95 face mask called Amazfit AERI to contribute more to the global health management and epidemic prevention. The product in concept features a transparent anti-fog cover and a translucent frame. Wearers' facial expressions can be seen even if they wear masks, easing the social distancing and allowing wearers to unlock their phones with Face ID. The Innovative Amazfit AERI can clean itself and last for several weeks. In April, the avant-garde Amazfit X Smartwatch with curved AMOLED screen and button free design went on sale by crowdfunding, which brings the upgraded experience for the users as well.
For combating COVID-19, Huami also partnered with China National Clinical Research Center of Respiratory Disease (NCRCRD) and Guangdong Nanshan Medical Innovation Institute which led by Dr. Nanshan Zhong to build up a smart wearable joint laboratory. Based on Huami smart wearable technology and powerful computing algorithms, the lab aims to help COVID-19 recovered patients follow-up care and management through the NCRCRD big data platform.
[1] Cognitive Modeling of Multimodal Data Intensive Systems for Applications in Nature and Society (COMDICS)
[2] Discrete Dynamics in Nature and Society https://www.hindawi.com/journals/ddns/2020/6152041
[3] Huami wearable devices are not a medical device and is not intended for use in the diagnosis or monitoring of any medical condition.
[4] Data Availability: The concerned sensor data cannot be shared due to user privacy. For academic purposes, de-identified region-level statistics can be shared under agreement.
Conflicts of Interest: The authors declare that there are no conflicts of interest regarding the publication of this paper.
[5] Quoted from https://www.hindawi.com/journals/ddns/2020/6152041
[6] According to the study on fever and cardiac rhythm, L. Faust, K. Feldman, S. M. Mattingly et al., "Deviations from normal bedtimes are associated with short-term increases in resting heart rate," Npj Digital Medicine, vol. 3, no. 1, pp. 1–9, 2020.
[7] The New England Journal of Medicine (NEJM): https://www.nejm.org/doi/full/10.1056/NEJMoa2001316
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