Assistive Navigation Technologies for Inclusive Mobility
Identifying Key Environmental Factors Influencing Wheelchair Navigation Through a Scoping Review
Bibliographic Data
| ID | 22033105 |
|---|---|
| Authors | Ali Ahmadi (0009-0005-8140-1571, Université Laval, corresponding author), Maryam Naghdizadegan Jahromi (0000-0002-5285-2622, Université Laval), Mir Abolfazl Mostafavi (0000-0002-3688-6638, Centre for Interdisciplinary Research in Rehabilitation), Ernesto Morales (0000-0002-6488-5093, Centre for Interdisciplinary Research in Rehabilitation), Nouri Sabo (Université de Sherbrooke) |
| Year | 2026 |
| Volume | 15 |
| Issue | 2 |
| Pages | 75 |
| Publication date | 2026-02-12 |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | ISPRS International Journal of Geo-Information (JOURNAL) |
| Journal identifiers | ISSN: 2220-9964 • E-ISSN: 2220-9964 |
| Publisher | MDPI AG (PUBLISHER • IT) |
| DOI | 10.3390/ijgi15020075 |
| OpenAlex | W7128744132 |
| Language | EN |
| References cited | 47 |
Despite advancements in navigation apps for wheelchair users, there is no consensus on which environmental factors to prioritize for personalized accessible routes. This scoping review synthesizes factors influencing wheelchair mobility in urban settings, evaluates measurement methods, and assesses their integration into routing algorithms. Following Arksey and O’Malley’s framework and PRISMA-ScR guidelines, we analyzed six databases for English-language articles from 2005 to 2023, supplemented by an updated search covering 2023 to 2026. Two reviewers screened 6966 records and examined 79 full-text articles, with 24 meeting the inclusion criteria for data extraction. Environmental factors were categorized into static and dynamic factors affecting mobility. Key components included sidewalks (96%), ramps (63%), curb cuts (54%), stairs (50%), crosswalks (50%), and streets (38%). Common factors examined were length, slope, width, and surface properties. Data collection methods varied: 42% relied on measurements, 8% used user assessments and sensors, while 50% combined both approaches. Recent studies (2023–2026) demonstrate increasing adoption of AI and machine learning techniques, including crowdsourced smartphone data and generative AI for feature detection. This review identifies essential factors for wheelchair navigation and highlights significant gaps in dynamic factor assessment and real-time data integration
Assistive technology · Data collection · Global Positioning System · Personalization · Wheelchair · Assistive Technology in Communication and Mobility · Gaze Tracking and Assistive Technology · Tactile and Sensory Interactions
| Highly cited | No |
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