Digital Twin Smart Educational Platform for Visually Impaired Navigation Training

NCT07641127 — Not applicable, Completed, 30 participants, King Salman Center for Disability Research

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A Digital Twin Smart Educational Platform for Simulating Physical Obstacles and Safe Navigation Training for the Saudi Visually Impaired

Registry	NCT07641127
Status	Completed
Phase	Not applicable
Study type	Interventional; randomized, parallel, masking: double, supportive care
Enrollment	30 (Actual)
Sponsor	King Salman Center for Disability Research (Other)
Conditions	Visual Impairment
Interventions	Adaptive Multi-modal Digital Twin Navigation Training (Behavioral)
Dates	start 2026-01-07, primary completion 2026-04-29, completion 2026-05-19, first posted 2026-06-11, last updated 2026-06-11
Locations	1 site in Egypt

-Summary

This study evaluates a novel digital twin smart educational platform designed to train visually impaired individuals on safe navigation in Saudi urban environments. Independent mobility is challenging for visually impaired people due to dynamic hazards and architectural changes. This interventional study utilizes an advanced computer simulation (digital twin) modeled after real streets in Jeddah, Saudi Arabia. Participants are randomly assigned to either the experimental group (receiving training via the adaptive digital twin platform with 3D spatial audio and wearable haptic feedback) or the control group (receiving traditional orientation and mobility instruction). The training consists of 10 structured sessions over 5 weeks. The primary goal is to determine…

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+Detailed description

The purpose of this study is to examine the engineering validity and pedagogical efficacy of the Adaptive Multi-modal Reality Learning Environment (AMRLE) for visually impaired orientation and mobility (O\&M) training. The platform features a three-tier architecture: Data Acquisition Tier: Utilizing Mobile Laser Scanning (MLS) and LiDAR point clouds to generate high-fidelity 3D environments compliant with the Saudi Building Code (SBC 201) universal access standards. Processing Tier: Running a custom 3D simulation engine embedded with an AI-driven adaptive algorithm. The algorithm dynamically adjusts environmental complexity and obstacle density based on the user's real-time collision metrics and path deviation speeds. Human Interaction Tier: Delivering sensory...

+Arms and interventions

Arm	Type	Interventions	Description
Experimental: AMRLE Group	Experimental	Behavioral: Adaptive Multi-modal Digital Twin Navigation Training	Participants assigned to this group will undergo orientation and mobility (O\&M) training within the Adaptive Multi-modal Reality Learning Environment (AMRLE) powered by a dynamic Digital Twin simulation engine. Over a 5-week curriculum comprising 10 structured sessions (25 minutes per session), trainees interact with virtual replicas of real-world Saudi urban layouts. The system delivers non-visual feedback using low-latency 3D spatial binaural audio and localized directional haptic/vibrotactile device telemetry. An AI-driven optimization model automatically scales environmental complexity and obstacle generation based on real-time participant performance loops.
Active Control: Conventional O&M Group	Active comparator	Behavioral: Adaptive Multi-modal Digital Twin Navigation Training	Participants assigned to this control group will undergo standard, conventional orientation and mobility (O\&M) rehabilitation instructions. Over a 5-week period consisting of 10 structured training sessions (25 minutes per session), trainees practice navigation along fixed real-world paths using traditional white canes. The instruction relies entirely on conventional tactile maps, physical paving cues, and static verbal orientation scripts provided by an O\&M instructor. No virtual reality simulators, adaptive AI algorithms, or high-fidelity digital twin interventions are utilized.

Adaptive Multi-modal Digital Twin Navigation Training (Behavioral): A structured 10-session orientation and mobility (O\&M) curriculum distributed over 5 weeks (2 sessions/week, 25 minutes/session). The intervention leverages a dynamic digital twin simulation engine of Saudi urban spaces to proactively train visually impaired users on hazard mitigation. Trainees navigate via non-visual multi-modal feedback loops: 3D spatialized binaural audio (HRTF) pings indicating structural pathways, combined with directional haptic/vibrotactile vest telemetry for real-time proximity boundaries. An AI optimization model continuously adjusts environmental complexity and obstacle generation (static, semi-dynamic, and crowded scenarios) matching the real-time collision metrics of the participant to prevent learning plateaus and optimize cognitive mapping.

+Outcome measures

Primary outcomes

Real-world Collision Rate (RCR) [At the completion of the 10-session training curriculum (Week 5).]: The cumulative number of physical obstacle impacts or structural contact errors recorded per 100 meters during the final real-world post-test field navigation trial.

+Eligibility

Sex: All, ages: 18 Years to 60 Years

Inclusion Criteria:

* Certified clinical diagnosis of severe visual impairment or legal blindness.
* Aged between 18 and 60 years old.
* Physical ability to walk independently and unassisted for at least 15 continuous minutes.
* Cognitive and neurological competence to comprehend and interact with multi-modal software telemetry.
* Baseline proficiency in using accessibility screen-reader features on mobile devices.

Exclusion Criteria:

* Profound sensorineural hearing loss or auditory dysfunction that prevents 3D spatial binaural sound localization.
* Active upper or lower-limb motor neuropathies that disrupt the perception of haptic micro-actuator vibrations.
* History of severe vestibular disorders, severe motion sickness, or inner ear syndromes causing chronic dizziness.
* Current participation in concurrent physical orientation and mobility clinical trials.

+Locations (1)

Facility	City	Country	Status
Special Education Resource Rooms	Cairo	Egypt

+Study documents

Study Protocol (PDF, 2026-06-03)