Institute for Health and Sport, Victoria University, Melbourne, VIC 3000, Australia.
Sensors (Basel). 2023 Mar 3;23(5):2802. doi: 10.3390/s23052802.
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.
独立行走对于维持我们的生活质量至关重要，但安全的行动取决于在日常生活环境中感知危险。为了解决这个问题，越来越多的人关注开发能够提醒用户注意不稳定脚部接触地面或障碍物的风险的辅助技术，从而导致跌倒。为了识别绊倒风险并提供纠正反馈，正在使用设计用于监测脚-障碍物相互作用的鞋装传感器系统。智能可穿戴技术的进步，将运动传感器与机器学习算法相结合，为鞋装障碍物检测带来了新的发展。本综述的重点是用于行人的步态辅助可穿戴传感器和危险检测。该文献代表了一个至关重要的研究前沿，为实用、低成本、可穿戴设备铺平了道路，这些设备可以使行走更安全，并降低跌倒伤害带来的日益增加的财务和人力成本。
