| Project/Area Number |
16H07469
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| Research Category |
Grant-in-Aid for Research Activity Start-up
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| Allocation Type | Single-year Grants |
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| Research Field |
Applied health science
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| Research Institution | The University of Tokyo |
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Principal Investigator |
LIANG ZILU 東京大学, 大学院工学系研究科(工学部), 特任助教 (10782807)
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| Project Period (FY) |
2016-08-26 – 2018-03-31
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| Project Status |
Completed (Fiscal Year 2017)
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| Budget Amount *help |
¥2,990,000 (Direct Cost: ¥2,300,000、Indirect Cost: ¥690,000)
Fiscal Year 2017: ¥1,430,000 (Direct Cost: ¥1,100,000、Indirect Cost: ¥330,000)
Fiscal Year 2016: ¥1,560,000 (Direct Cost: ¥1,200,000、Indirect Cost: ¥360,000)
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| Keywords | Sleep / Personal informatics / Wearable computing / Quantified Self / Health informatics / HCI / Fitbit / EEG / sleep / wearable / personal informatics / quantified self / 睡眠 / wearables / health / wearable fitness tracker |
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| Outline of Final Research Achievements |
This study aimed to develop accurate home sleep tracking technology. Our main findings are listed below. (1) Consumer sleep trackers agreed well to medical devices in measuring total sleep duration and sleep efficiency, but classifying sleep stages remains challenging for both devices. (2) In terms of user experience with sleep tracking devices, people had incomplete and faulty mental models of sleep, and there are two blind spots in people’s mental models of sleep tracking devices. In addition, they encountered challenges in reconciling device differences for assessing their sleep quality. (3) We developed a new sleep staging algorithm based on Generalized Linear Mixed Model to count in fixed and random effects during sleep tracking. Compared to the proprietary algorithm of Fitbit, our new algorithm achieved similar specificity, better sensitivity and thus higher balanced accuracy. This study provided rich implications to the design of accurate home sleep tracking technologies.
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