PDF(Pubmed)
Abstract：
The behavior of pedestrians in a non-constrained environment is difficult to predict. In wearable robotics, this poses a challenge, since devices like lower-limb exoskeletons and active orthoses need to support different walking activities, including level walking and climbing stairs. While a fixed movement trajectory can be easily supported, switches between these activities are difficult to predict. Moreover, the demand for these devices is expected to rise in the years ahead. In this work, we propose a cloud software system for use in wearable robotics, based on geographical mapping techniques and Human Activity Recognition (HAR). The system aims to give context to the surrounding pedestrians by providing hindsight information. The system was partially implemented and tested. The results indicate a viable concept with great extensibility prospects.
摘要：
行人在非约束环境中的行为很难预测。在可穿戴机器人技术中，这构成了挑战，由于下肢外骨骼和活动矫形器等设备需要支持不同的步行活动，包括水平行走和爬楼梯。虽然固定的运动轨迹可以很容易地支持，这些活动之间的切换很难预测。此外，预计未来几年对这些设备的需求将上升。在这项工作中,我们提出了一种用于可穿戴机器人的云软件系统，基于地理制图技术和人类活动识别(HAR)。该系统旨在通过提供事后的信息来为周围的行人提供上下文。该系统已部分实现和测试。结果表明，这是一个可行的概念，具有很大的可扩展性前景。
