Research Information System
APPLIED SCIENCES-BASEL, vol.16, no.10, 2026 (SCI-Expanded, Scopus)
Falls in older adults and individuals with balance impairments remain a major concern because they are closely associated with injury, reduced mobility, and loss of independence. This study presents a preclinical proof-of-concept for a cognitive smart walker architecture that combines user-compatible walking assistance with active safety intervention. The system integrates a 2D LiDAR sensor for contactless lower-limb monitoring, a six-degree-of-freedom (6-DOF) force/torque sensor to measure user-walker interaction, and an inertial measurement unit (IMU) for dynamic monitoring, with all data processed in real time on a Raspberry Pi/ROS-based platform. Normal walking assistance is provided through a command-level variable admittance-based controller that converts interaction forces into a smoothed signed duty-cycle command rather than a rigid speed-control signal. Safety decisions are managed by a Hierarchical State Machine (HSM). Early-risk conditions are handled through motor-based dynamic braking, whereas severe physical crises additionally deploy lateral support legs to enlarge the base of support. Within this framework, the system can detect and manage foot entanglement, grip loss, forward fall, vertical collapse, lateral fall, successive crises, and recovery-abort events. In experiments across multiple scenarios, the system correctly detected all 50 crisis cases and did not issue unnecessary interventions in 30 non-crisis cases. These findings show that the proposed architecture can preserve transparent walking assistance during normal gait while providing graded, context-sensitive active safety when risk emerges.