The study is posted on arXiv and submitted for IEEE Transactions on Biomedical Engineering. It is in its second round of review in the journal.

A machine learning algorithm used data from a medical wristband to reduce the sleep patterns of 25 study participants who were intentionally exposed to flu stress. For seven of the eight participants who came down with the flu and also had usable data, evidence of disrupted sleep appeared 24 hours before the participants shed the virus.

Although early studies focused on the flu, this method may be sufficient to spot the onset of other infections in general – possibly including.

“As we obtain more data from the population wearing smart watches through this epidemic, our forecasting model will be refined. We imagine that these sophisticated models can be used to generate an early warning signal and even enable prediction of asymptomatic spread without potentially testing, ”corresponding author Alfred Hero, John h. Professor of Electrical Engineering and Computer Science of Holland Distinguished University and R. Jameson and Betty Williams of Engineering.

While this approach will not be able to diagnose in any way, it may still be able to provide useful guidance when people should self-isolate as a precaution. In the current pandemic, those experiencing cold and flu symptoms are asked to self-isolate, who have tested positive for new, did not experience the most common symptoms of fever, dry cough, and shortness of breath. is. If the algorithm can operate with less complete data of a smartwatch or fitness tracker, critical workers may receive an early warning that they are getting sick and self-segregating.

We develop an unpublished transfer learning algorithm based on the multivariate hidden Markov model and Fisher’s linear differential analysis, which optimally adjusts the sleep pattern shift by training on the dynamics of sleep / wake states.

The proposed algorithm leverages a taper window mechanism to establish sleep patterns in an incremental fashion by establishing an initial training set with a hidden Markov model, without the need for a priori information about true sleep / wake states. is.

Our domain-optimization algorithm is applied to a dataset collected in a human viral challenge study, which identifies the sleep / wake periods of both uninfected and infected participants.

The algorithm successfully detects sleep / wake sessions in subjects whose sleep pattern is interrupted by a respiratory infection (H3N2 flu virus). Pre-symptomatic features based on known periods are found to be strongly predictive of both infection status (AUC = 0.844) and infection onset time (AUC = 0.885), indicating the effectiveness and utility of the algorithm.

Using integrated multisensor signal processing and adaptive training schemes, our algorithm is able to capture major sleep patterns in ambulatory monitoring, leading to better automated sleep assessment and prediction.

The study is posted on arXiv and submitted for IEEE Transactions on Biomedical Engineering. It is in its second round of review in the journal.

A machine learning algorithm used data from a medical wristband to reduce the sleep patterns of 25 study participants who were intentionally exposed to flu stress. For seven of the eight participants who came down with the flu and also had usable data, evidence of disrupted sleep appeared 24 hours before the participants shed the virus.

Although early studies focused on the flu, this method may be sufficient to spot the onset of other infections in general – possibly including.

“As we obtain more data from the population wearing smart watches through this epidemic, our forecasting model will be refined.

We imagine that these sophisticated models can be used to generate an early warning signal and even enable prediction of asymptomatic spread without potentially testing, ”corresponding author Alfred Hero, John h. Professor of Electrical Engineering and Computer Science of Holland Distinguished University and R. Jameson and Betty Williams of Engineering.

While this approach will not be able to diagnose in any way, it may still be able to provide useful guidance when people should self-isolate as a precaution. In the current pandemic, those experiencing cold and flu symptoms are asked to self-isolate, who have tested positive for new, did not experience the most common symptoms of fever, dry cough, and shortness of breath. is.

If the algorithm can operate with less complete data of a smartwatch or fitness tracker, critical workers may receive an early warning that they are getting sick and self-segregating.

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