The use case: how VirtuSense deploys Labelbox
VirtuSense uses Labelbox as a data engine for its fall-prevention AI. Pre-generated sensor data is imported via the Python SDK, reviewed and corrected by an internal expert team, and exported to model training so edge models are retrained regularly.
More reported results
Figures published in Labelbox’s case study. They are not independently measured by usedby.
has produced over one million evaluated assets
False alarm rates fell from roughly 28% to 6%
average accuracy increased by over 20%
In the customer’s words
Out of all the platforms we explored, Labelbox is the easiest to use and the best for managing experts and workflows and monitoring performance.Labelbox users in HealthTech & Digital Health
Companies in the same sector, with public evidence.
See all Labelbox customersGo straight to the source
Based on Labelbox’s published case study. We check the public evidence; the results are reported by the vendor.
labelbox.com- Last checked
- Oct 7, 2026
Open source capture What the story claims, and what we checked
What we compared with the page.
- The figure: 5XChecked
The figure is on the page; its label is our wording.
- The passage quoted aboveChecked
Copied word for word from the page, near the name of VirtuSense.
- VirtuSense uses LabelboxChecked
Confirmed line.
- The result itselfNot checked
We quote it; we did not measure it.
Since adopting Labelbox, VirtuSense increased the training data it creates by 5X and has produced over one million evaluated assets. False alarm rates fell from roughly 28% to 6%, the daily average number of alerts dropped from seven to five