Multi-Night at-Home Evaluation of Improved Sleep Detection and Classification with a Memory-Enhanced Consumer Sleep Tracker.
Shohreh GhorbaniHosein Aghayan GolkashaniNicholas Yong Nian CheeTeck Boon TeoAndrew Roshan DicomGizem YilmazRuth L F LeongJu Lynn OngMichael Wei-Liang CheePublished in: Nature and science of sleep (2022)
These results affirm the benefits of applying machine learning and a diverse training dataset to improve sleep measurement of a consumer wearable device. Importantly, collecting raw data with appropriate hardware allows for future advancements in algorithm development or sleep physiology to be retrospectively applied to enhance the value of longitudinal sleep studies.