Name
Abstract | ||
|---|---|---|
So far, approximately 30 kinds of sleep disorders have been raised, and irregular sleep rhythm caused by disturbance of sleep induction and nocturnal awakening is one important factor in lifestyle-related diseases. Therefore, evaluating quality of sleep on a daily basis has a great significance for healthcare. On the other hand, in late years smart phone applications came to be able to record life activity log, mostly physical activity monitoring, and sleep length. However, the method used are mainly based on motion analysis, such sleep length cannot be measured accurately enough. This paper presents a newly proposed method to detect accurately falling asleep and awakening times, towards its application for a new smart clock alarm. Proposed method relies on using a wearable heart rate sensor to extract activity indices of autonomic nervous system calculated from heart rate variability in addition to body motion, and on algorithms that dynamically mine into the sequences of heterogeneous data to identify accurately sleep start and end times. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1145/3004010.3004014 | MobiQuitous (Adjunct Proceedings) |
Keywords | Field | DocType |
Wearable sensor,Sleep,Falling asleep,Awakening | Circadian rhythm,Heart rate variability,Wearable computer,ALARM,Simulation,Computer science,Sleep induction,Real-time computing,Motion analysis,Heart rate,Smart phone | Conference |
Citations | PageRank | References |
1 | 0.35 | 4 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Takayuki Okamura | 1 | 21 | 3.21 |
| Naoya Isoyama | 2 | 8 | 9.90 |
| Guillaume Lopez | 3 | 3 | 1.80 |