# Sleep Data # Dashboard Account This refers to an SleepTrack dashboard account. Users who have conducted sleep tracking using the API Key issued from this account can log in to the dashboard to retrieve their data. Each account provides two environments: Live and Test, and separate API Keys can be issued for each. Data tracked with an API Key from a specific environment can only be accessed using the corresponding API Key. Additionally, the statistics provided by the dashboard are separated by environment. # User A user refers to the individual measuring sleep using the client app. You can generate a User ID through the SDK or API for sleep tracking. This allows you to track sleep sessions and manage sleep data on a per-user basis. # Session A sleep session refers to a single sleep period (from starting the tracking at night to pressing the stop button in the morning). You can create a sleep session for a specific user, upload the data for analysis, and then close the session once the tracking is complete. A session can have three states: **OPEN**, **CLOSED**, and **COMPLETE**. * **OPEN**: The session is created, and sound data can be uploaded for analysis. * **CLOSED**: The session is closed, meaning no more sound data can be uploaded, and it is waiting for AI server analysis to complete. * **COMPLETE**: All analyses have been completed. # Sleep Data Metrics When a sleep session is in the OPEN state, only real-time analyzed sleep data is available. Once the session is closed and the AI server completes the analysis, transitioning the session state to COMPLETE, you can access various sleep metrics and generate a sleep report for the user. The available sleep metrics depend on the Enterprise contract plan. Check the [Data Plan](plan-data) to see which metrics are provided under each plan. ## Basic Sleep Information Data collection related to the start and end of sleep tracking, 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 Measurement Time** (`time_in_bed`) and **Sleep Period Time** (`time_in_sleep_period`) serve as the standard for different data metrics, so be careful to distinguish between the two data sets. | No. | Data Name | Type | Definition | Example \[Units] (= Actual Value) | | :-- | :--------------------- | :------- | :------------------------------------------------------------------------------------------------------------------ | :-------------------------------------------------------------------------------- | | 1 | `start_time` | Time | Exact time at which the tracking started. | **2023-11-22T00:00:10+09:00** \[YYYY-MM-DDThh:mm:ss±hh:mm] (=2023-11-22 00:00:10) | | 2 | `end_time` | Time | Exact time at which the tracking ended. | **2023-11-22T06:30:38+09:00** \[YYYY-MM-DDThh:mm:ss±hh:mm] (=2023-11-22 06:30:38) | | 3 | `time_in_bed` | Duration | The time duration between the start and the end of tracking. | **23428** \[s] (=6h 30m 28s) | | 4 | `sleep_time` | Time | Exact time at which ‘Sleep’ was first detected since the tracking started. | **2023-11-22T00:53:10+09:00** \[YYYY-MM-DDThh:mm:ss±hh:mm] (=2023-11-22 00:53:10) | | 5 | `wake_time` | Time | Exact time at which the last 'Wake' period was detected during tracking. No sleep will be detected after wake\_time. | **2023-11-22T06:30:08+09:00** \[YYYY-MM-DDThh:mm:ss±hh:mm] (= 2023-11-22 06:30:08) | | 6 | `time_in_sleep_period` | Duration | The time duration between the onset of ‘Sleep’ and the start of the last ‘Wake’ during tracking. | **20190** \[s] (= 5h 36m 30s) | | 7 | `sleep_latency` | Duration | The time duration between the start of tracking and the detection of first 'Sleep'. | **3180** \[s] (=53m 0s) | | 8 | `wakeup_latency` | Duration | The time duration between the start of the last 'Wake' and the end of tracking. | **30** \[s] (= 0m 30s) | | 9 | `sleep_index` | Score | The metric that comprehensively represents sleep quality, defined by learning from the distribution of sleep data. | **97** (range: 50 ~ 100) | ### Sleep Index The `sleep_index` is defined by three indicators: total sleep time (`time_in_sleep`), which measures the quantity of sleep; sleep efficiency (`sleep_efficiency`), which measures the quality of sleep; and the number of awakenings (`waso_count`). The `sleep_index` ranges from 50 to 100 points and is designed to have the highest correlation with users’ subjective sleep satisfaction, based on medical definitions of sleep quality and sleep big data. Awakenings during sleep are categorized into long awakenings that a person can perceive and short awakenings lasting 30 seconds to a few minutes, which are difficult to notice. Short awakenings that are distributed throughout sleep are reflected in the score through `waso_count`, while long awakenings that a person can perceive are reflected through `sleep_efficiency`. ## Sleep Efficiency Data related to **Sleep Efficiency** (`sleep_efficiency`), one of the key indicators representing the quality of sleep. * By figuring out the **Total 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 **Sleep Period Time** (`time_in_sleep_period`), please note that **Sleep Efficiency** (`sleep_efficiency`) data is based on the **Total Measurement Time** (`time_in_bed`) | No. | Data Name | Type | Definition | Example \[Units] (= Actual Value) | | :-- | :----------------- | :------- | :--------------------------------------------------------------------------------------------------------------------- | :------------------------------- | | 1 | `time_in_sleep` | Duration | The actual time spent sleeping. It is the time duration calculated after subtracting all ‘Wake’ time from time\_in\_bed. | **19380** \[s] (= 5h 23m 0s) | | 2 | `sleep_efficiency` | Ratio | The ratio of time\_in\_sleep to time\_in\_bed | **0.83** \[x.xx] (=83%) | ## 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
No. Data Name Type Definition Example \[Units] ( \= Actual Value)
1 `sleep_stages` Number List A list of numbers, indicating sleep stages during time\_in\_bed\ (0: Wake / 1: Light / 2: Deep / 3: REM) * *\[0,0,1,……,2,2,3,3,1,1,0,0,0]*\* \[list] (= wake, wake, light, .....)
2 `waso_count` Count The number of ‘Wake’ detected during time\_in\_sleep\_period * *19*\* \[#] (= 19 times)
3 `longest_waso` Duration The longest time duration of ‘Wake’ during time\_in\_sleep\_period * *90*\* \[s] (=1m 30s)
4 `time_in_wake` Duration The sum of all ‘Wake’ seconds detected during time\_in\_sleep\_period * *810*\* \[s] (=13m 30s)
5 `time_in_light` Duration The sum of all ‘Light’ seconds detected during time\_in\_sleep\_period * *8610*\* \[s] (=2h 23m 30s)
6 `time_in_deep` Duration The sum of all ‘Deep’ seconds detected during time\_in\_sleep\_period * *5700*\* \[s] (=1h 35m 0s)
7 `time_in_rem` Duration The sum of all ‘REM’ seconds detected during time\_in\_sleep\_period * *5070*\* \[s] (=1h 24m 30s)
## Sleep Stage Ratio Data collection related to the ratio of awakenings and each sleep stage during the **Sleep Period 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 **Sleep Period Time** (`time_in_sleep_period`), excluding wakefulness. To compare the ratio of awakening to sleep in the user's sleep period 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`). | No. | Data Name | Type | Definition | Example \[Units] (= Actual Value) | | :-- | :------------ | :---- | :--------------------------------------------- | :------------------------------- | | 1 | `sleep_ratio` | Ratio | Ratio of time\_in\_sleep to time\_in\_sleep\_period | **0.96** \[x.xx] (=96%) | | 2 | `wake_ratio` | Ratio | Ratio of time\_in\_wake to time\_in\_sleep\_period | **0.04** \[x.xx] (=4%) | | 3 | `light_ratio` | Ratio | Ratio of time\_in\_light to time\_in\_sleep\_period | **0.43** \[x.xx] (=43%) | | 4 | `deep_ratio` | Ratio | Ratio of time\_in\_deep to time\_in\_sleep\_period | **0.28** \[x.xx] (=28%) | | 5 | `rem_ratio` | Ratio | Ratio of time\_in\_rem to time\_in\_sleep\_period | **0.25** \[x.xx] (=25%) | ## 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.
No. Data Name Type Definition Example \[Units] ( \= Actual Value)
1 `light_latency` Duration The time duration between sleep\_time to the first detection of ‘Light’\ (Normally, '0' as light sleep is the first sleep stage to be detected) * *0*\* \[s] (=0m 0s)
2 `deep_latency` Duration The time duration between sleep\_time to the first detection of ‘Deep’ * *540*\* \[s] (=9m 0s)
3 `rem_latency` Duration The time duration between sleep\_time to the first detection of ‘REM’ * *4710*\* \[s] (=1h 18m 30s)
## Sleep Period Data collection related to the sleep cycles that periodically occur within the **Sleep Period 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. | No. | Data Name | Type | Definition | Example \[Units] (= Actual Value) | | :-- | :------------------ | :-------- | :---------------------------------------------------------------------------------------------------------- | :---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | | 1 | `sleep_cycle` | Duration | The average time duration of sleep cycles. Each sleep cycle is consisted of both 'REM' and 'Non-REM' sleep. | **6520** \[s] (=1h 38m 40s) | | 2 | `sleep_cycle_count` | Count | The number of sleep cycles detected during the time\_in\_sleep\_period | **3** \[#] (=3 times) | | 3 | `sleep_cycle_time` | Time List | A list of the exact start and end times of each sleep cycle | **\[2023-11-22T00:53:10+09:00,2023-11-22T02:26:10+09:00,2023-11-22T04:54:10+09:00,2023-11-22T06:19:10+09:00]** \[list] (= 2023-11-22 00:53:10, 2023-11-22 02:26:10, 2023-11-22 04:54:10, 2023-11-22 06:19:10) | ## Snoring SleepTrack can detect not only sleep stages but also various sleep events, such as snoring. Snoring data includes the occurrence time, frequency, and ratio, providing more detailed sleep analysis.
No. Data Name Type Definition Example \[Units] ( \= Actual Value)
1 `snoring_stages` Number List A list of snoring stages during the time\_in\_bed\ (0: no snoring / 1: snoring) * *\[0,0,0,1,1,……,1,0,0,0,0]*\* \[list] (= no snoring, no snoring, no snoring, snoring, snoring .......)
2 `time_in_snoring` Duration The time duration, of which snoring is detected during time\_in\_sleep\_period * *23400*\* \[s] (=6h 30m 00s)
3 `time_in_no_snoring` Duration The time duration, of which no snoring is detected during time\_in\_sleep\_period * *5400*\* \[s] (=90m 0s)
4 `snoring_ratio` Ratio The ratio of time\_in\_snoring to time\_in\_sleep\_period * *0.72*\* \[x.xx] (=72%)
5 `no_snoring_ratio` Ratio The ratio of time\_in\_no\_snoring to time\_in\_sleep\_period * *0.28*\* \[x.xx] (=28%)
6 `snoring_count` Count The number of times snoring is detected during the time\_in\_sleep\_period * *20*\* \[#] (=20 times)