Personalized Health Wearable Metrics

This dataset contains high-frequency, time-stamped health metrics collected from wearable devices, including heart rate, step counts, sleep stages, activity types, and calories burned. It supports multi-user, multi-device scenarios and is ideal for developing personalized wellness algorithms, sleep analysis, and preventative health models. The structure enables granular time-series analytics and integration with broader health informatics systems.

  • last updated 22 Jul 2025
  • by GoMask
The brief that made it

Personalized wellness and fitness tracking

Sample rows

preview · 8 of 202 rows · all 10 columns
user_idstringactivity_typestringcalories_burnedfloatsleep_stagestringdevice_idstringtimestampdatetimeheart_rate_bpmintegersteps_countintegersleep_duration_minutesintegerdata_sourcestring
U1001sleepingblankdeepD201A2024-06-10T23:45:00Z47blank62SleepDoc
U1002sleepingblankremD202B2024-06-11T00:30:00Z51blank82FitBand Pro
U1003sleepingblanklightD203C2024-06-11T07:10:00Z66blank35NightMon
U1004sleepingblankawakeD204D2024-06-11T06:32:00Z39blank22SleepDoc
U1005sleepingblankdeepD205E2024-06-11T05:50:00Z44blank77NightMon
U1001walking51.3blankD201A2024-06-11T08:15:00Z911324blankWalkBuddy
U1006walking110.9blankD206F2024-06-11T10:20:00Z1062740blankHealthTrack X
U1007walking186.7blankD207G2024-06-11T13:05:00Z1164950blankHealthTrack X

What the 202 rows show

from the 202-row sample

Running (activity type) stands out: mean calories_burned is 494.9, against 47.0 for the rest.

  • 51.3median calories_burned
  • 8data sources
  • 89.5median heart_rate_bpm
  • 1,532median steps_count
  • 48median sleep_duration_minutes
Mean calories_burned by activity_type163 rows
040080091.5walking63 rows494.9running38 rows0.82idle37 rows3.4other25 rows
calories_burned163 rows, in bands of 100
055110101217886750400800calories_burned →

Median 51.3, from 0.20 to 784.1.

sleep_stage39 rows with a value · 163 left blank
  1. rem10
  2. light10
  3. deep9
  4. awake7
  5. unknown3
10 columns by typefrom the column list below
  • string 5
  • integer 3
  • float 1
  • datetime 1

Columns

10 columns in three groups
blueprint · 10 columns
columntypedescriptionexample
Text 5 columns
user_idstringUnique identifier for the individual wearing the deviceU1001
device_idstringUnique identifier for the wearable device used to collect the dataD201A
sleep_stagestringSleep stage detected at the timestamp (if applicable)awake · light · deep · rem · unknown · optionaldeep
activity_typestringType of activity detected (e.g., walking, running, sleeping, idle)walking · running · sleeping · idle · other · optionalsleeping
data_sourcestringSource of the data (e.g., device model or app name)8 sourcesSleepDoc
Numbers 4 columns
heart_rate_bpmintegerHeart rate in beats per minute recorded at the timestamp20 to 250 · optional47
steps_countintegerNumber of steps detected during the interval ending at the timestamp0 or more · optional1324
sleep_duration_minutesintegerDuration in minutes of the current sleep stage (if applicable)0 or more · optional62
calories_burnedfloatEstimated calories burned during the interval ending at the timestamp0 or more · optional51.3
Dates and times 1 column
timestampdatetimeDate and time when the metric was recorded (in UTC)2024-06-10T23:45:00Z

Use it for

  • median calorie…51.3163 rowsmean calories burned …91.5walk…494.9runn…0.82idle3.4other

    A healthcare dashboard

    Calories_burned by activity_type and a breakdown of sleep_stage. Excel, Power BI or Tableau.

  • Why do the 38 running rows have a mean calories_burned of 494.9?

    A root-cause class exercise

    Hand out the rows and one question. The answer is in the data, not in the brief.

  • A software demo

    Believable users with device_id, timestamp and heart_rate_bpm to fill a screen in front of a buyer.

Not quite right?

Make it yours.

Same 10 columns, your size and your rules. See 20 rows before you pay.

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This dataset202 rows10 columns
Yours1,000 rows10 columns

blueprint · personalized-health-wearable-metrics

Behind this dataset

Same schema. As many rows as you need.

These 202 rows came out of a blueprint — 10 columns with generation rules behind each one. Open it in Data Factory to retune a column, add your own, wire in foreign keys, and run it at the size you actually need.

Rules it was built with
  • Activity levels vary by weekday
  • Sleep metrics impacted by age group
  • Abnormal heart rates flagged
  • Device metadata included
  • Data gaps simulated for device off-periods
Rows
Open the blueprint in Data Factory

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Exports
CSV, JSON, JSONL, Parquet, SQL, Excel, TSV, XML
Licence
yours to use, including commercially
API slug
personalized-health-wearable-metrics

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