Dashboard Account

This refers to an AsleepTrack 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 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

AsleepTrack 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)