Cane-Type Robots for Mobility Assistance—Design, Sensing, Control, and Fall Prevention: A Narrative Review

Introduction: Cane-type robots are emerging as a promising class of assistive devices for individuals with reduced mobility because they combine portability with the potential to improve stability, support gait, and contribute to fall detection and fall prevention. This review examines how these systems have been designed and validated, with particular emphasis on the trade-offs that may determine their real-world usefulness. Methods: This narrative review analyzed 46 eligible articles selected from an initial pool of 2439 records. The included studies were examined with respect to structural design, sensing and actuation components, motion-control strategies, fall-detection methods, fall-prevention approaches, and experimental validation. Results: Mobile bases provide up to 3 degrees of freedom (DOF), with additional DOFs enabled by revolute joints (1 DOF) and universal joints (2 DOFs). However, canes with 1 or 2 wheels often lack the stability required to support body weight safely during walking or fall events, which limits their suitability for the target population. All systems rely on cane-mounted sensors, whereas some also incorporate wearable sensors that may improve functionality but can reduce usability and long-term acceptability. Across the reviewed literature, the most important limitations are the scarcity of clinical gait trials involving older adults or mobility-impaired users, the very limited availability of active fall-prevention strategies that do not rely on wearable sensors, and the persistent absence of gait-phase identification in many systems. Conclusions: Cane-type robots show clear potential as mobility-assistance technologies, but the field remains constrained by limited clinical validation and by design choices that often increase sensing burden without demonstrating translational readiness. Future research should prioritize clinically validated prototypes, robust gait-phase recognition, and fall-prevention strategies based exclusively on cane-integrated sensing to improve reliability, usability, and real-world adoption.