Is providing an unpublished dataset worthy of coauthorship?
The Vancouver protocol gives authorship according to the following criteria:

Substantial contributions to the conception or design of the work; or the acquisition, analysis, or interpretation of data for the work; AND
Drafting the work or revising it critically for important intellectual content; AND
Final approval of the version to be published; AND
Agreement to be accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved.

So collecting data fulfills the first criteria. Therefore, the nice thing to do is to offer a co-authorship for your colleague. It is then up to him or her to decide whether he or she would like participate, which would entail fulfilling the other three criteria as well.

In this case, “collecting data” means finding and equipping participants with technology to monitor them perform a simple task for a brief amount of time (5 min).

Be careful not to underestimate the amount of time needed to recruit participants. The recruiter usually has to write several emails and advertisements, answer phone calls, brief and debrief the participants, prepare and take written informed consents, etc.
In answer to the comment, if your colleague accepts the co-authorship, ask him or her to write about the data collection, participants, motivation and possible limitations of the test, how the results can be interpreted and so on. Machine learning that matters should communicate something back to problem domain. This would be sufficient to fulfill the second Vancouver criteria.
Anyway, some fields have very different conventions, so your mileage may vary.