用惯性数据识别穿戴者走路模式，支持实时自适应控制。
User-Tailored Learning to Forecast Walking Modes for Exosuits
- 仅用两个传感器的惯性数据，预测上下楼梯和平地行走
- 可同时预测未来和过去步态，支持闭环控制与在线更新
- 适合需要个性化适应的外骨骼设备开发者
助行机器人如柔性下肢外骨骼或外骨骼服正日益普及，有望在日常生活中帮助人们。为使系统适应不同使用者，需赋予外骨骼先进感知能力。然而，因需轻便易穿，外骨骼传感器数量有限。本文提出一种基于机器学习的感知模块，仅利用两个传感器的惯性数据，估计用户三种行走模式：上楼、下楼和平地行走。该方法可同时提供未来与过去时间步的预测，支持控制策略并实现在线自标注，可用于新用户模型的持续优化。在真实生活数据集上的全面分析验证了该用户定制化感知模块的有效性。最终，我们将系统集成至外骨骼中，在单人在线实验中完成闭环控制器验证，性能表现良好。
原文摘要 · Abstract (English)
Assistive robotic devices, like soft lower-limb exoskeletons or exosuits, are widely spreading with the promise of helping people in everyday life. To make such systems adaptive to the variety of users wearing them, it is desirable to endow exosuits with advanced perception systems. However, exosuits have little sensory equipment because they need to be light and easy to wear. This paper presents a perception module based on machine learning that aims at estimating 3 walking modes (i.e., ascending or descending stairs and walking on level ground) of users wearing an exosuit. We tackle this perception problem using only inertial data from two sensors. Our approach provides an estimate for both future and past timesteps that supports control and enables a self-labeling procedure for online model adaptation. Indeed, we show that our estimate can label data acquired online and refine the model for new users. A thorough analysis carried out on real-life datasets shows the effectiveness of our user-tailored perception module. Finally, we integrate our system with the exosuit in a closed-loop controller, validating its performance in an online single-subject experiment.
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