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Objectives

The objective of this project is to build a Nodejs library capable of interfacing the mainstream health and fitness trackers and to export their data in a way that is consistent across different brands and devices. This will allow to run data analysis on larger populations of users who are willing to offer access to their personal data, e.g. for medical research.

Status

The project is in its very early stages. Data extraction is supported only for the Jawbone UP. The design of the cross-device API has not started.

Supported devices

• Jawbone UP: all versions

Candidates: Fitbit, Moves (app only)

Usage

Command line tools

Jawbone Up (upcl)

node ./upcl.js command --email email --password password [--jpath jpath] [--csv] [--rfriendly]

Returns the raw JSON output of the execution of the /nudge/api/users/user_xid/band API. command is one of: band, sleeps, sleepsDetail, workouts (to be expanded as ready).

In the name of not risking polluting / compromising the data in any way, please note that the values returned by the API are not processed and this can be occasionally inconvenient, e.g. dates are expressed as seconds from epoch rather than JavaScript's standard of milliseconds from epoch.

email and password are the credentials required to authenticate on the Jawbone servers.

When specifying a jpath (an XPath-like way of specifying a location in the JSON structure; see the node-jPath project page) only that part of the data is returned.

Alternatively, the tool can output to CSV format with the --csv option. In that case it is also necessary to specify a --jpath pointing to a point in the original JSON structure that is an array of objects and that can be 'flattened' to CSV unambiguously.

If, for example, the API returned:

{ "meta": { "user_xid": "[removed]", "message":"OK", "code":200, "time":1384158919 },
  "data": { "ticks": [
					   { "value": {
					   		"distance": 27,
					   		"active_time": 27,
					   		"aerobic": false,
					   		"calories": 1.66470658779,
					   		"steps": 41,
					   		"time": 1380588480,
					   		"speed": 1 }
					   },
					   { "value": {
					   		"distance": 3,
					   		"active_time": 2,
					   		"aerobic": false,
					   		"calories": 0.167267397046,
					   		"steps": 4,
					   		"time": 1380588540,
					   		"speed": 1 }
					   },
					   (...)

the jPath path we are interested in is data.ticks.value. If we then run:

node upcl.js band --fromDate 2013-10-01 --toDate 2013-10-31 -e [e-mail] -p password --csv --json data.ticks.value > band.csv

we will be saving a csv file named band.csv, with one line being created for every item in 'data.ticks', with one column for every property in the 'value' property of each item. The file contents will look like:

distance,active_time,aerobic,calories,steps,time,speed
27,27,,1.66470658779,41,1380588480,1
3,2,,0.167267397046,4,1380588540,1
(...)

Finally, the --rfriendly option produces R-friendly CSV files, and it replaces the joint use of the options --csv and --jpath to pre-selected suitable points. E.g. when executing the band command, using --rfriendly is equivalent to doing --csv --json data.ticks.value .

Credits

Jawbone UP

Data extraction from the UP band was made possible thanks to Eric Blue's pioneering work aimed at studying the communication protocol between the UP mobile app and the Jawbone servers, described in his blog post dated 28/11/2011.

Almost two years later and to the best of my knowledge, the protocol documented by Eric is still the only way UP users can own the entire detailed dataset produced by the device, as Jawbone only offers aggregated data for download. Alternatively, you need to develop a custom UP application, that is beyond most users' reach.

I re-iterate the same recommendation Eric made then: as this API isn’t officially supported and appears to be used only by Jawbone's app, please use it sparingly (no excessive requests) and don’t do anything that would violate a TOS/AUP. Use at your own risk.