🙋‍♀️ User

User is defined as a specific person you’d like to record sleep. You can use the SDK or API to generate a user uuid that you want to record your sleep. This allows you to manage sleep sessions on a per-user basis.

🛌 Sleep Session

A sleep session is a concept that corresponds to one’s sleep(lying in bed until waking up). You can create a sleep session for a specific user, upload the data you want to analyze, and end the session.

The session has a total of three states: OPEN, CLOSED, and COMPLETE.

• OPEN: Session has been created and audio data can be uploaded for analysis
• CLOSED: No more audio can be uploaded at the end of the session, waiting for the analysis to complete
• COMPLETE: All analysis is completed

📇 Mel Spectrogram

Audio data measured on a smartphone or smart device is converted to the form of a Mel spectrogram through the SDK's preprocessing algorithm and sent to the server.

📊 Data Metrics

When sleep concludes and all analyses are completed, the data available for confirmation through the analysis results is as follows: Depending on the plan, users can monitor in real-time the analyzed sleep stages and respiratory stability stages up to the present moment in sessions that are in the 'Open' state when utilizing the real-time analysis feature.

[ Explore Data Offerings by Plan ↗️ ]

1. Basic Sleep Information

Data collection related to the start and end of sleep measurement, as well as sleep onset and wake-up times.

• Sleep latency (sleep_latency) and Wakeup latency (wakeup_latency) can be used as data to infer the user's habits (e.g., difficulty falling asleep, lying in bed for a long time after waking up).

• Total time in bed (time_in_bed) and Total sleep time (time_in_sleep_period) serve as the standard for different data metrics, so be careful to distinguish between the two data sets.

2. Sleep Efficiency

Data related to Sleep Efficiency (sleep_efficiency), one of the key indicators representing the quality of sleep.

• By figuring out the Actual Sleep Time (time_in_sleep) during the Total Measurement Time (time_in_bed), you can calculate Sleep Efficiency (sleep_efficiency).
• While there is no one-size-fits-all answer for sleep, having a high Sleep Efficiency suggests securing an optimal amount of sleep in relation to the time spent in bed.
• While the Sleep Stage Ratio (sleep_ratio) data is based on the Total Sleep Time (time_in_sleep_period), please note that Sleep Efficiency (sleep_efficiency) data is based on the Total Measurement Time (time_in_bed)

3. Sleep Stage

Data collection related to the four sleep stages: Wake, Light Sleep, Deep Sleep, and REM Sleep.

• During the Total Measurement Time (time_in_bed), the user's sleep is recorded every 30 seconds as one of the four stages. You can check this collection of sleep stages through the Sleep Stage List (sleep_stages)
• The frequency and duration of awakenings during sleep can be crucial indicators affecting the subjective satisfaction of sleep, making them important metrics during result analysis. Relevant data, such as Wake After Sleep Onset Count (waso_count), Time in Wakeful Stage (time_in_wake), and Longest Wake After Sleep Onset (longest_waso), can be utilized for this purpose

4. Sleep Stage Ratio

Data collection related to the ratio of awakenings and each sleep stage during the Total Sleep Time (time_in_sleep_period)

• The Sleep Stage Ratio (sleep_ratio) data represents the proportion of time recorded in Light, Deep, and REM stages during the Total Sleep Time (time_in_sleep_period), excluding wakefulness. To compare the ratio of awakening to sleep in the user's total sleep time, you can utilize both Sleep Stage Ratio (sleep_ratio) and Wake Ratio (wake_ratio).
• To gain an overview of the user's overall sleep structure, you can compare and utilize the following four data: Wake Ratio (wake_ratio), Light Sleep Ratio (light_ratio), Deep Sleep Ratio (deep_ratio), and REM Sleep Ratio (rem_ratio).

5. Sleep Stage-specific Latency

Data collection related to the time it takes for each sleep stage (Light Sleep, Deep Sleep, REM Sleep) to first appear from the onset of sleep.

• Generally, as the Sleep Latency (sleep_latency) increases, the length of the REM Sleep Latency (rem_latency) also increases.
• Since the first detected sleep after the user initiates sleep tracking is always Light Sleep, please note that, barring any errors, the value for Light Sleep Latency (light_latency) data is always 0.

6. Sleep Period

Data collection related to the sleep cycles that periodically occur within the Total Sleep Time (time_in_sleep_period).

• Typical sleep involves a pattern of alternating between Non-REM (NREM) and REM sleep. A bundle of repeating NREM and REM sleep is referred to as a sleep cycle, and the start and end times of each sleep cycle can be confirmed through the Sleep Cycle Transition Time List (sleep_cycle_time) data.
• A healthy adult typically experiences 4-6 cycles of sleep per night, each lasting 70-110 minutes, when obtaining around 8 hours of sleep. To comprehend the user's sleep cycle structure, you can utilize the Sleep Cycle Count (sleep_cycle_count) and Average Sleep Cycle Duration (sleep_cycle) data.

7. Unstable Breath

Data collection related to understanding the user's respiratory stability and instability during sleep.

• Similar to the Sleep Stage List (sleep_stages) data, the respiratory stages are recorded as 0 (Stable) and 1 (Unstable) during the Total Measurement Time (time_in_bed). This collection of respiratory stages can be confirmed in the Breath Stage List (breath_stages) data after the measurement is concluded.
• The user's irregular breathing is converted into Unstable breathing index (breathing_index) data, and the severity of the Unstable breathing pattern (breathing_pattern) is determined by its value.

8. Snoring