Sleep Detection

Effortlessly document your sleeping, waking, and key sleep stages.

What is it?

Effortlessly record when you go to bed, when you fall asleep, and when you wake up. Meaningful sleep stages (REM, light, deep) are detected along the way. Informative on a daily basis, when examined over time this documented record of your sleep becomes a powerful resource for awareness and enhanced personal responsibility. Larger trends that are easy to miss from one day to the next come into focus. This information also lays the necessary groundwork for additional analysis, interpretation, and ultimately a path to better sleep if that’s what you need.

Key Benefits:

–  Effortlessly record your sleep time and structure.
–  Create a resource for personal accountability.
–  Provides a basis for analysis, interpretation and coaching.

How it works:

Firstbeat’s Sleep Detection analysis uses a combination of factors to recognize and document your sleep. These include heart rate, heart rate variability (HRV), respiration rate derived from heartbeat data, wrist or body movement captured with an accelerometer, and time of day. Information you provide about yourself (i.e., age, height, weight and gender) provides additional context for analysis and interpretation.

An ability to recognize the difference between lying in bed and being asleep is essential to the detection analysis and for interpretation down the road. The length of time between when you get into bed and when you fall asleep is called sleep latency, or sometimes sleep onset latency (SOL).

Once you fall asleep, REM, light and deep sleep stages are analytically detected. Restlessness is recorded along with waking moments during the night, particular attention is paid to times when you are awake for longer than 5 minutes at a time. As a result, the structure of your sleep over the course of the night is revealed. You can see important sleep cycles and how various stages contributed to the whole.

Firstbeat’s experience developing neural network-based physiological analytic tools delivers a significant advantage to wearable devices. Minimal processing and power requirements mean that Sleep Detection can run directly on the device itself without transferring the data to a paired smartphone or other external device. It also means that results can be provided practically without delay and without the need to capture significant amounts of data after waking up before providing insight into sleep.