State-of-the-Art Review on Wearable Obstacle Detection Systems Developed for Assistive Technologies and Footwear

Walking independently is essential to maintaining our quality of life but safe locomotion depends on perceiving hazards in the everyday environment. To address this problem, there is an increasing focus on developing assistive technologies that can alert the user to the risk destabilizing foot contact with either the ground or obstacles, leading to a fall. Shoe-mounted sensor systems designed to monitor foot-obstacle interaction are being employed to identify tripping risk and provide corrective feedback. Advances in smart wearable technologies, integrating motion sensors with machine learning algorithms, has led to developments in shoe-mounted obstacle detection. The focus of this review is gait-assisting wearable sensors and hazard detection for pedestrians. This literature represents a research front that is critically important in paving the way towards practical, low-cost, wearable devices that can make walking safer and reduce the increasing financial and human costs of fall injuries.

Automated summary

Walking independently is crucial for quality of life, but recognizing hazards is important for safe locomotion. Assistive technologies, such as shoe-mounted sensor systems with machine learning algorithms, are being developed to detect and prevent tripping risks. This review focuses on wearable sensors for gait assistance and hazard detection, aiming to pave the way for practical and affordable devices that can improve walking safety and reduce fall injuries' costs.