Digital Twin Smart Educational Platform for Visually Impaired Navigation Training
A Digital Twin Smart Educational Platform for Simulating Physical Obstacles and Safe Navigation Training for the Saudi Visually Impaired
2 other identifiers
interventional
30
1 country
1
Brief 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…
Trial Health
Trial Health Score
Automated assessment based on enrollment pace, timeline, and geographic reach
participants targeted
Target at below P25 for not_applicable
Started Jan 2026
Shorter than P25 for not_applicable
1 active site
Health score is calculated from publicly available data and should be used for screening purposes only.
Trial Relationships
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Study Timeline
Key milestones and dates
Study Start
First participant enrolled
January 7, 2026
CompletedPrimary Completion
Last participant's last visit for primary outcome
April 29, 2026
CompletedStudy Completion
Last participant's last visit for all outcomes
May 19, 2026
CompletedFirst Submitted
Initial submission to the registry
June 4, 2026
CompletedFirst Posted
Study publicly available on registry
June 11, 2026
CompletedJune 11, 2026
June 1, 2026
4 months
June 4, 2026
June 8, 2026
Conditions
Keywords
Outcome Measures
Primary Outcomes (1)
Real-world Collision Rate (RCR)
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.
At the completion of the 10-session training curriculum (Week 5).
Study Arms (2)
Experimental: AMRLE Group
EXPERIMENTALParticipants 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 COMPARATORParticipants 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.
Interventions
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.
Eligibility Criteria
You may qualify if:
- 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.
You may not qualify if:
- 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.
Contact the study team to confirm eligibility.
Sponsors & Collaborators
Study Sites (1)
Special Education Resource Rooms
Cairo, Egypt
MeSH Terms
Conditions
Condition Hierarchy (Ancestors)
Study Design
- Study Type
- interventional
- Phase
- not applicable
- Allocation
- RANDOMIZED
- Masking
- DOUBLE
- Who Masked
- PARTICIPANT, OUTCOMES ASSESSOR
- Purpose
- SUPPORTIVE CARE
- Intervention Model
- PARALLEL
- Sponsor Type
- OTHER
- Responsible Party
- PRINCIPAL INVESTIGATOR
- PI Title
- King Salman Center for Disability Research
Study Record Dates
First Submitted
June 4, 2026
First Posted
June 11, 2026
Study Start
January 7, 2026
Primary Completion
April 29, 2026
Study Completion
May 19, 2026
Last Updated
June 11, 2026
Record last verified: 2026-06
Data Sharing
- IPD Sharing
- Will share