Patent pending · IEEE Access under review
Awdah AI builds map-free augmented-reality navigation that records a route as it's walked and guides a disoriented person back along it — no pre-built maps, no installed infrastructure, working entirely on-device and offline.
Architecture
The route is authored simply by walking it once. Everything after that — reconstruction, detection, and guidance — happens automatically on-device.
Walk the route once. The app records position fixes on the phone as you go — outdoors via GPS, indoors via visual-inertial tracking.
Confidence-Weighted Trajectory Reconstruction scores every fix and fuses them through a robust Kalman smoother, gating multipath outliers.
A confidence-gated disorientation detector senses separation, wandering, a geofence breach, or a caregiver / panic trigger.
The reconstructed route is reversed and rendered as profile-adaptive AR cues — footprints, arrows, or panels — leading the user home.
Core Algorithm · Patent §5.15
In heavy urban multipath, raw satellite fixes scatter wildly. Awdah weights each fix by accuracy, kinematics, and temporal gap, then filters through an RTS Kalman smoother with hard outlier gating.
Multiplicative product gating horizontal dilution, pedestrian speed ceiling, and logging time intervals.
Measurement-noise variance dynamically inflates on degraded fixes to eliminate visual cue jitter.
Trajectory error reduction achieved across 55-trace field trial around the Prophet's Mosque.
Accessibility Adaptation · Patent Fig. 11
The engine reverses the same spatial path, but adapts the AR presentation layer to the subject's cognitive profile to avoid sensory overload or panic.
Traditional maps demand spatial rotation and multi-step cognitive abstraction. For users on the autism spectrum or experiencing sensory overwhelm, Awdah replaces cartography with floor-registered, non-blinking step cues that direct gaze downward to physical footsteps, calming sensory input.
Field Deployments
Snapshots from field testing the deployed iOS and ARKit prototype across dense urban and indoor venues.
Awdah AI in 60 seconds
A short introduction to Awdah AI — the problem, the idea, and what it feels like to be guided back to where you started.
Live On-Device Run
A real recording of the deployed iOS prototype: a route is trained by walking it once, then re-navigated using floor-registered AR cues with no satellite signal and no pre-built map.
Android app
Download the Android build and walk a route once — then let Awdah guide you back along it, entirely on-device and offline.
Point your Android camera at the code to download app.apk directly.
app.apkWhere it matters
From the dense crowds of the Haram district to hospitals, campuses, and family safety — the same map-free engine adapts to the venue and the user.
Reuniting pilgrims separated in the crowds of Makkah and Madinah, where satellite reception is heavily degraded by multipath and signage is limited. Validated with a 55-trace field campaign around the Prophet's Mosque.
Map-free wayfinding and return navigation in large, complex venues with zero installed infrastructure.
Helping patients and visitors retrace their way through unfamiliar buildings without relying on signage.
Designed for neurodiverse users and at-risk wanderers, with cueing calibrated to lower cognitive and sensory load.
A parent follows AR arrows straight to a separated child in a crowd-dense venue — no app install required to find them.
The same two-device tracker/finder mode applies to recovering a wandering pet via its logged GPS path.
Intellectual Property · USPTO 64/084,040
Official drawings submitted under U.S. 35 U.S.C. § 111(b) for the Adaptive Reverse-Trajectory Recovery System and Method.
Depicts Single-Device mode for personal self-navigation and Two-Device mode with cloud relay for caregiver tracking.
Continuous outbound trajectory logging, disorientation state sensing, trajectory inversion, and profile-adaptive cue generation.
Comparison of footprint sequences (autism), cartoon icons (children), and oversized directional panels (elderly dementia).
Automatic SMS dispatch with zero-install browser tracking URL upon departure from designated safety perimeter.
Founder & inventor
Research and development engineer turned researcher with 18+ years of combined industry and academic experience. Currently a Research Assistant at the Dr. Hussein El-Sayyed Center for Scientific Research, University of Prince Mugrin, Al Madinah — leading work on safety-critical vehicle control, AI-based smart-city transportation, and adaptive XR systems.
Previously a Software Engineer at Advantest Corporation (Japan, 2006–2018), and later founded and led Inventus Limited (2018–2024). Sole inventor on two U.S. patent applications, including the adaptive reverse-trajectory recovery system behind Awdah AI.
Whether it's pilgrims in Madinah, visitors in a crowd-dense venue, or a family member who wandered off — we'd like to talk about your use case.
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