Tamper-proof movement verification
All industries
Insurance & wellness
The first fraud-proof movement signal.
Auditable proof a movement actually happened. Reward real activity, not a step count.
Joint travel and motion stability block spoofing
Per-call pricing. Pay only on validated sessions
Drops into existing wellness platforms
The challenge
Step counts and self-reports are not a movement signal.
Wellness incentive programmes have a credibility problem. A pedometer counts a phone in a tumble dryer. A self-reported workout counts whatever the user says it counts. The signal you are paying out against is gameable, and the actuaries know.
Behavioural fraud is endemic
Step-spoofing apps. Shake jigs. Self-report rounding. The cheapest dollar in your incentive programme is the one paid out to a session that did not happen.
Spend is decoupled from outcome
You are paying per claimed activity. The honest member subsidises the gamer. The unit economics of the programme degrade as participation grows.
The activity signal is too coarse
Even with a clean pedometer signal, 8 000 steps is not a movement programme. It does not differentiate the user mobilising their joints from the one strolling to the kitchen.
How PoseFlow fits
A fraud-proof movement signal you can pay against.
PoseFlow turns the user's phone into a movement-verification instrument. The output is a per-rep proof trail your actuaries can defend and your finance team can budget against.
Tamper-proof, on the device
PoseFlow runs entirely on-device. There is no API call to spoof, no network event to fake. Verification happens against the actual movement, in the actual camera frame.
Multi-signal proof per rep
Pose confidence, joint travel, motion stability, tempo. Each rep carries a structured proof signature. Falsifying it requires reproducing all four signals, in sync, on a real body, in the camera.
Per-call pricing matches your unit economics
PoseFlow is priced per validated session, not per seat. Spend scales with the actual movement happening on your platform, not with optimistic MAU forecasts.
Drops into your existing wellness stack
You already have a member app, an incentive engine, a partner network. PoseFlow plugs in as the verification layer underneath the existing reward flow. Nothing structural needs to move.
Use cases
Where PoseFlow turns spend into signal.
Three patterns we see across health-insurance, employer-wellness and government wellness programmes.
01
Premium-discount programmes
Members earn a premium discount for completing a verified movement programme. PoseFlow validates each session. The discount lands on actual activity, not declared activity.
11–18% Fraud-flagged sessions removed from payout
02
Reward-points marketplaces
Verified movement converts to points. Points convert to partner offers. PoseFlow makes the front of the funnel honest so the marketplace economics stay viable as you scale.
−24% Cost per validated reward vs claimed reward
03
Post-claim rehabilitation incentives
After an MSK claim, the member is incentivised to complete a movement programme. PoseFlow verifies the work. Claim outcomes improve because the rehabilitation is actually happening.
+41% Programme completion vs self-reported control
Integration
How PoseFlow drops into a wellness programme.
The verification layer slots underneath your existing reward flow without restructuring it.
Read the technical docs- 01 Add the PoseFlow SDK to your member app (we support iOS, Android and web).
- 02 Author the programme's movements once in PoseFlow Studio.
- 03 Wrap your existing "session complete" UX with `TrackedMovementView` so verification happens in-flow.
- 04 On `onRepCompleted`, post the verified-session payload (with its proof signature) to your reward engine.
- 05 Audit the per-rep proof trail anywhere downstream the actuarial team needs to defend a payout.
Common questions
Insurance & wellness FAQ
How do we audit a verified session?
Every rep PoseFlow validates emits a structured proof signature: pose confidence, joint-travel envelope, tempo, motion-stability score. Your backend stores the signatures alongside the session. An auditor can re-evaluate any payout against the underlying signal months later.
What stops a member from holding the phone over a video?
The pose-confidence signal flags playback-shaped poses (unnatural stability, no depth cues, lighting mismatches). The motion-stability signal catches the impossible-smoothness signature of a screen recording. False-positive rates from naïve spoofing attempts are visible enough that your fraud team can drop them at first signal.
How is data privacy handled?
No video leaves the device. The member-side app sees only pose-coordinate data. The backend receives session-level aggregates and proof signatures. GDPR, HIPAA and equivalent regimes are cleared without extra engineering.
Does it work with members who do not have a recent phone?
PoseFlow runs on iOS 13+ and Android API 24+. That covers the bulk of the installed base. On older or under-spec devices, the SDK degrades to a lower frame rate before refusing to run, so the programme stays reachable.
Talk to us
Ready to explore PoseFlow for insurance & wellness?
We will prep a technical walkthrough tailored to your stack and your deployment timeline.
Ready to integrate
See how PoseFlow integrates into your product.
Each engagement starts with a technical walkthrough, tailored to your use case.