Healthcare
16 columns
200 rows
8 formats

Healthcare Wearable Vital Streams

This dataset provides high-frequency, multi-sensor vital sign measurements from synthetic wearable devices, including heart rate, blood pressure, respiratory rate, blood oxygen saturation, and skin temperature. Each record is timestamped and linked to a user and device, with contextual information such as activity type, sensor status, and geolocation, making it ideal for time-series analysis, anomaly detection, and digital health research.

opened 7 times
last updated 22 Jul 2025
by GoMask

The brief that made it

Real-time anomaly detection in patient vital signs

Sample rows

preview · 8 of 200 rows · all 16 columns

stream_idstring	activity_typestring	blood_oxygen_saturationfloat	anomaly_flagboolean	device_idstring	user_idstring	timestampdatetime	heart_rate_bpmfloat	blood_pressure_systolicfloat	blood_pressure_diastolicfloat	respiratory_ratefloat	skin_temperature_celsiusfloat	sensor_statusstring	device_firmware_versionstring	location_latitudefloat	location_longitudefloat
stream001	resting	98.2	false	deviceA	userX	2024-04-01T08:00:00Z	62.5	116	74	15	35.1	normal	2.3.5	40.7128	-74.006
stream002	walking	97.5	false	deviceB	userY	2024-04-01T08:02:00Z	77.2	128	82	17	35.8	normal	3.1.2	41.8781	-87.6298
stream003	running	89.9	true	deviceC	userZ	2024-04-01T08:04:00Z	158.3	182	104	39	38.4	battery_low	1.4.0	34.0522	-118.2437
stream004	sleeping	99.1	false	deviceD	userW	2024-04-01T08:06:00Z	51.7	105	66	12	34.2	normal	2.0.1	48.8566	2.3522
stream005	cycling	96.7	false	deviceE	userV	2024-04-01T08:08:00Z	96.4	133	90	23	36.9	normal	4.2.3	52.52	13.405
stream006	running	92.6	true	deviceF	userU	2024-04-01T08:10:00Z	133.7	162	108	29	37.7	error	3.5.9	35.6895	139.6917
stream007	resting	98.8	false	deviceG	userT	2024-04-01T08:12:00Z	63.9	118	76	16	34.7	normal	2.5.1	37.7749	-122.4194
stream008	walking	93.1	true	deviceH	userS	2024-04-01T08:14:00Z	122.1	144	97	25	37.2	battery_low	3.2.0	51.5074	-0.1278
stream009	walking	97.7	false	deviceI	userR	2024-04-01T08:16:00Z	70.8	126	80	17	35.4	normal	2.8.4	28.6139	77.209
stream010	sleeping	98.6	false	deviceJ	userQ	2024-04-01T08:18:00Z	42.5	99	60	10	33.5	normal	2.1.0	55.7558	37.6173
stream011	running	74.7	true	deviceK	userP	2024-04-01T08:20:00Z	182.3	224	132	44	41.9	error	1.2.2	-33.8688	151.2093
stream012	resting	96.9	false	deviceL	userO	2024-04-01T08:22:00Z	59.1	111	69	13	34	normal	2.6.7	19.4326	-99.1332
stream013	cycling	97.9	false	deviceM	userN	2024-04-01T08:24:00Z	74.6	134	86	18	36	normal	4.3.5	39.9042	116.4074
stream014	resting	99.3	false	deviceN	userM	2024-04-01T08:26:00Z	48.3	96	58	9	33.3	normal	2.4.3	35.6895	51.389
stream015	other	50	true	deviceO	userL	2024-04-01T08:28:00Z	203.1	250	150	60	45	error	1.1.7	-89.9999	179.9999
stream016	cycling	94.5	true	deviceP	userK	2024-04-01T08:30:00Z	130.2	156	98	26	37.8	battery_low	3.7.2	59.9343	30.3351
stream017	walking	98.5	false	deviceQ	userJ	2024-04-01T08:32:00Z	68.5	124	79	16	35.5	normal	2.9.0	22.3964	114.1095
stream018	other	79.8	true	deviceA	userI	2024-04-01T08:34:00Z	29.8	60	40	6	23.6	disconnected	3.0.8	-34.6037	-58.3816
stream019	resting	97	false	deviceB	userH	2024-04-01T08:36:00Z	57.2	109	67	14	34.6	normal	2.7.1	41.3851	2.1734
stream020	running	68.4	true	deviceC	userG	2024-04-01T08:38:00Z	172.9	208	128	49	42.2	error	1.5.4	23.1291	113.2644

What the 200 rows show

from the 200-row sample

Other (activity type) stands out: mean blood_oxygen_saturation is 70.3, against 94.1 for the rest.

37%anomaly_flag = true
96.1median blood_oxygen_saturation
4sensor statuses
26users
76.4median heart_rate_bpm
132median blood_pressure_systolic

Mean blood_oxygen_saturation by activity_type200 rows

blood_oxygen_saturation200 rows, in bands of 10

Median 96.1, from 50.0 to 99.8.

device_id200 rows · top 10 of 26 values

deviceC12
deviceE12
deviceF12
deviceA11
deviceB11
deviceD11
deviceG11
deviceH11
deviceI11
deviceJ11

16 columns by typefrom the column list below

string 6
float 8
datetime 1
boolean 1

Columns

16 columns in four groups

blueprint · 16 columns
column	type	description	example
Text 6 columns
`stream_id`	string	Unique identifier for each continuous data stream from a wearable device.unique	stream001
`device_id`	string	Unique identifier for the wearable device generating the data.	deviceA
`user_id`	string	Unique identifier for the user wearing the device.	userX
`activity_type`	string	Type of activity detected at the time of measurement (e.g., resting, walking, running, sleeping).6 values · optional	resting
`sensor_status`	string	Status of the sensor at the time of measurement (e.g., normal, battery_low, disconnected, error).normal · battery_low · disconnected · error · optional	normal
`device_firmware_version`	string	Firmware version of the wearable device at the time of measurement.optional	2.3.5
Numbers 8 columns
`heart_rate_bpm`	float	Heart rate in beats per minute measured at the given timestamp.20 to 250 · optional	62.5
`blood_pressure_systolic`	float	Systolic blood pressure (mmHg) measured at the given timestamp.50 to 250 · optional	116
`blood_pressure_diastolic`	float	Diastolic blood pressure (mmHg) measured at the given timestamp.30 to 150 · optional	74
`respiratory_rate`	float	Respiratory rate in breaths per minute measured at the given timestamp.5 to 60 · optional	15
`blood_oxygen_saturation`	float	Blood oxygen saturation (SpO2) percentage measured at the given timestamp.50 to 100 · optional	98.2
`skin_temperature_celsius`	float	Skin temperature in degrees Celsius measured at the given timestamp.20 to 45 · optional	35.1
`location_latitude`	float	Latitude coordinate of the device at the time of measurement.-90 to 90 · optional	40.7128
`location_longitude`	float	Longitude coordinate of the device at the time of measurement.-180 to 180 · optional	-74.006
Dates and times 1 column
`timestamp`	datetime	Precise date and time when the measurement was recorded.	2024-04-01T08:00:00Z
True or false 1 column
`anomaly_flag`	boolean	Indicates if the measurement is flagged as an anomaly (true) or normal (false).optional	false

Use it for

A healthcare dashboard
The anomaly_flag rate, blood_oxygen_saturation by activity_type and a breakdown of device_id. Excel, Power BI or Tableau.
Why do the 20 other rows have a mean blood_oxygen_saturation of 70.3?
A root-cause class exercise
Hand out the rows and one question. The answer is in the data, not in the brief.
Streams200
stream00198.2resting
stream00389.9running
stream00692.6running
A software demo
Believable streams with device_id, user_id and timestamp to fill a screen in front of a buyer.

This dataset200 rows16 columns

Yours1,000 rows16 columnslocation_latitude: UK only

blueprint · healthcare-wearable-vital-streams

Behind this dataset

Same schema. As many rows as you need.

These 200 rows came out of a blueprint — 16 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

Record heart rate, temperature, movement data
Simulate daily and circadian cycles
Include health event and activity tags
Model device dropouts and erroneous readings

Rows1,000 rows10,000 rows100,000 rows1,000,000 rows

Open the blueprint in Data Factory

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-vital-streams

What should your data show?

No signup. No card.

Sample rows

What the 200 rows show

Columns

Use it for

A healthcare dashboard

A root-cause class exercise

A software demo

Make it yours.

Same schema. As many rows as you need.

More in Healthcare

What should your data show?