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PulseClean
Clean wearable health data into analysis-ready CSVs
PulseClean converts messy Apple HealthKit exports and wearable CSV files into clean Standard Schema CSVs ready for analysis in Python, R, Excel, or BI tools. Start free with 3 jobs per month, no credit card required.
About PulseClean on Product Hunt
“Clean wearable health data into analysis-ready CSVs”
PulseClean was submitted on Product Hunt and earned 3 upvotes and 1 comments, placing #153 on the daily leaderboard. PulseClean converts messy Apple HealthKit exports and wearable CSV files into clean Standard Schema CSVs ready for analysis in Python, R, Excel, or BI tools. Start free with 3 jobs per month, no credit card required.
On the analytics side, PulseClean competes within SaaS, Data and Health — topics that collectively have 51.5k followers on Product Hunt. The dashboard above tracks how PulseClean performed against the three products that launched closest to it on the same day.
Who hunted PulseClean?
PulseClean was hunted by Byeonghag Gwon. A “hunter” on Product Hunt is the community member who submits a product to the platform — uploading the images, the link, and tagging the makers behind it. Hunters typically write the first comment explaining why a product is worth attention, and their followers are notified the moment they post. Around 79% of featured launches on Product Hunt are self-hunted by their makers, but a well-known hunter still acts as a signal of quality to the rest of the community. See the full all-time top hunters leaderboard to discover who is shaping the Product Hunt ecosystem.
For a complete overview of PulseClean including community comment highlights and product details, visit the product overview.
Hi Product Hunt 👋
I built PulseClean to make wearable health data easier to clean and use for analysis.
Right now, PulseClean supports Apple HealthKit XML and generic wearable CSV files. It converts messy exports into a standardized CSV with quality flags, conservative missing-value handling, outlier handling, and documentation for the preprocessing policy.
The default pipeline is designed to avoid future-value imputation, so it is safer for ML-oriented workflows than simple bidirectional interpolation.
PulseClean is still early, and I would really appreciate feedback from researchers, data scientists, builders, and anyone working with wearable data.
If a preprocessing rule does not fit your workflow, or if you need support for another wearable format, please let me know. I’ll prioritize updates based on real user needs.
Thanks for checking it out.