Healthcare Wearable Device Readings
This dataset contains time-stamped, multi-vital sign readings from wearable health devices, including heart rate, steps, SpO2, and sleep stages, along with device and data quality metadata. Designed for health monitoring, anomaly detection, and longitudinal analysis, it enables researchers and clinicians to track user health trends, detect anomalies, and assess data reliability at scale.
Sample rows
preview · 8 of 200 rows · all 11 columns| reading_idstring | sleep_stagestring | spo2_percentfloat | anomaly_flagboolean | device_idstring | user_idstring | timestampdatetime | heart_rate_bpminteger | steps_countinteger | device_battery_levelinteger | data_quality_scorefloat |
|---|---|---|---|---|---|---|---|---|---|---|
| R001 | deep | 96.1 | false | D-Alpha | U1023 | 2022-01-01T00:15:00Z | 75 | 0 | 81 | 0.85 |
| R002 | rem | 97.8 | false | D-Beta | U1872 | 2022-02-28T23:58:00Z | 115 | 120 | 73 | 0.93 |
| R003 | blank | 87.4 | true | D-Kappa | U1502 | 2022-04-15T07:34:00Z | 133 | 18340 | 12 | 0.42 |
| R004 | awake | 97.6 | false | D-Delta | U1429 | 2022-03-01T06:50:00Z | 65 | 5 | 44 | 0.8 |
| R005 | unknown | 77.6 | true | D-Epsilon | U1166 | 2022-07-04T16:10:00Z | 38 | 0 | 6 | 0.34 |
| R006 | blank | 94.2 | false | D-Beta | U1354 | 2022-12-31T23:57:00Z | 158 | 20130 | 95 | 0.91 |
| R007 | light | 98.7 | false | D-Gamma | U1078 | 2023-02-14T11:02:00Z | 102 | 6000 | 76 | 0.98 |
| R008 | deep | 92.1 | false | D-Alpha | U1901 | 2022-11-22T03:21:00Z | 41 | 0 | 18 | 0.74 |
| R009 | blank | 85.7 | true | D-Lambda | U1108 | 2022-10-10T21:45:00Z | 191 | 3020 | 3 | 0.18 |
| R010 | awake | 99.1 | false | D-Kappa | U1571 | 2022-09-25T14:56:00Z | 73 | 16720 | 62 | 0.83 |
| R011 | deep | 90.2 | false | D-Theta | U1218 | 2022-12-25T00:05:00Z | 48 | 0 | 29 | 0.65 |
| R012 | blank | 52.8 | true | D-Omega | U1456 | 2022-08-08T08:20:00Z | 250 | 5012 | 8 | 0.07 |
| R013 | rem | 98.2 | false | D-Epsilon | U1127 | 2023-03-12T01:23:00Z | 54 | 0 | 37 | 0.77 |
| R014 | light | 97.2 | false | D-Delta | U1321 | 2023-04-01T13:00:00Z | 62 | 4512 | 53 | 0.91 |
| R015 | unknown | 75.2 | true | D-Gamma | U1192 | 2023-06-17T19:41:00Z | 39 | 0 | 1 | 0.13 |
| R016 | awake | 99.9 | false | D-Kappa | U1847 | 2023-01-01T00:00:00Z | 97 | 23400 | 100 | 1 |
| R017 | blank | 91.7 | false | D-Sigma | U1290 | 2023-01-14T21:11:00Z | 68 | 8900 | 51 | 0.84 |
| R018 | blank | 81.3 | true | D-Lambda | U1755 | 2023-03-20T22:57:00Z | 200 | 3000 | 92 | 0.23 |
| R019 | light | 97.5 | false | D-Mu | U1235 | 2023-07-04T09:47:00Z | 115 | 14210 | 88 | 0.89 |
| R020 | deep | 91.4 | false | D-Beta | U1884 | 2023-09-18T02:33:00Z | 45 | 0 | 18 | 0.61 |
What the 200 rows show
from the 200-row sampleUnknown (sleep stage) stands out: 19 of its 24 rows have anomaly_
- 36%anomaly_
flag = true - 94.3median spo2_
percent - 83.5median heart_
rate_ bpm - 3,010median steps_
count - 51.5median device_
battery_ level - 0.69median data_
quality_ score
Median 94.3, from 50.0 to 100.0.
- string 4
- integer 3
- float 2
- datetime 1
- boolean 1
Columns
11 columns in four groups| column | type | description | example |
|---|---|---|---|
| Text 4 columns | |||
reading_id | string | Unique identifier for each wearable device reading recordunique | R001 |
user_id | string | Unique identifier for the user wearing the device | U1023 |
device_id | string | Unique identifier for the wearable device | D-Alpha |
sleep_stage | string | Sleep stage detected at the given timestampawake · light · deep · rem · unknown · optional | deep |
| Numbers 5 columns | |||
heart_rate_bpm | integer | Heart rate measured in beats per minute30 to 250 · optional | 75 |
steps_count | integer | Number of steps recorded at the given timestamp0 or more · optional | 0 |
spo2_percent | float | Blood oxygen saturation level as a percentage50 to 100 · optional | 96.1 |
device_battery_level | integer | Battery level of the wearable device as a percentage0 to 100 · optional | 81 |
data_quality_score | float | Score indicating the quality of the recorded data (0.0 to 1.0)0 to 1 · optional | 0.85 |
| Dates and times 1 column | |||
timestamp | datetime | Date and time when the reading was recorded | 2022-01-01T00:15:00Z |
| True or false 1 column | |||
anomaly_flag | boolean | Indicates if the reading is flagged as an anomalyoptional | false |
Use it for
A healthcare dashboard
The anomaly_
flag rate, spo2_ percent by sleep_ stage and a breakdown of device_ id. Excel, Power BI or Tableau. Why do 71 of 200 rows have anomaly_
flag = true? A root-cause class exercise
Hand out the rows and one question. The answer is in the data, not in the brief.
- Readings200R00196.1deepR00387.4—R00577.6unknown
A software demo
Believable readings with user_
id, device_ id and timestamp to fill a screen in front of a buyer.
blueprint · healthcare-wearable-device-readings
Behind this dataset
Same schema. As many rows as you need.
These 200 rows came out of a blueprint — 11 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.
- Readings timestamped at regular intervals
- Each user has baseline demographic data
- Outliers and missing data inserted randomly
- User IDs are anonymized
- Labels for normal vs. abnormal periods
1 credit per row. Preview 20 rows free. Buy rows for the full run.
- Exports
- CSV, JSON, JSONL, Parquet, SQL, Excel, TSV, XML
- Licence
- yours to use, including commercially
- API slug
- healthcare-wearable-device-readings