Real-Time Walk Detection for Robotic Hip Exoskeleton Applications
Real-Time Walk Detection for Robotic Hip Exoskeleton Applications
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DOI：
10.1109/ismr48347.2022.9807510
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发表时间：
2022-04
期刊：
影响因子：
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通讯作者：
Hang Man Cho;Inseung Kang;DongHo Park;Dean D. Molinaro;Aaron J. Young
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作者：
Hang Man Cho;Inseung Kang;DongHo Park;Dean D. Molinaro;Aaron J. Young
检测用户的行走是外骨骼技术的关键部分，用于在运动过渡期间实现平滑和无缝辅助的完全自动化。研究人员已经通过使用不同种类的传感器采取了几种方法来开发行走检测系统;然而，目前仅存在几种解决方案，其可以仅使用嵌入在机器人髋关节外骨骼上的传感器来检测这些转变（即，髋部编码器和躯干IMU），这是用于在髋部外骨骼控制器的环路中实施这些系统的关键考虑。作为解决方案，我们探索并开发了两个步行检测模型，当模型使用两个过渡条件在步行和站立状态之间切换时，实现了有限状态机：站立到步行和步行到站立。其中一个模型使用两个髋关节编码器和一个IMU动态检测用户的步态周期;另一个模型仅使用两个髋关节编码器。我们的模型是使用公开的数据集开发的，并使用可穿戴传感器套件进行了在线验证，该套件包含通常嵌入在机器人髋关节外骨骼上的传感器。然后将这两个模型与足部接触估计方法进行比较，该方法作为评估我们模型的基线。我们的在线实验结果验证了我们的模型的性能，当使用HIP+IMU和HIP ONLY模型时，延迟时间分别为274 ms和507 ms。因此，在我们的研究中建立的步行检测模型在多个运动环境下实现了可靠的性能，而不需要手动调整或传感器，除了那些通常在机器人髋关节外骨骼上实现的。
Detection of the user’s walking is a critical part of exoskeleton technology for the full automation of smooth and seamless assistance during movement transitions. Researchers have taken several approaches in developing a walk detection system by using different kinds of sensors; however, only a few solutions currently exist which can detect these transitions using only the sensors embedded on a robotic hip exoskeleton (i.e., hip encoders and a trunk IMU), which is a critical consideration for implementing these systems in-the-loop of a hip exoskeleton controller. As a solution, we explored and developed two walk detection models that implemented a finite state machine as the models switched between walking and standing states using two transition conditions: stand-to-walk and walk-to-stand. One of our models dynamically detected the user’s gait cycle using two hip encoders and an IMU; the other model only used the two hip encoders. Our models were developed using a publicly available dataset and were validated online using a wearable sensor suite that contains sensors commonly embedded on robotic hip exoskeletons. The two models were then compared with a foot contact estimation method, which served as a baseline for evaluating our models. The results of our online experiments validated the performance of our models, resulting in 274 ms and 507 ms delay time when using the HIP+IMU and HIP ONLY model, respectively. Therefore, the walk detection models established in our study achieve reliable performance under multiple locomotive contexts without the need for manual tuning or sensors additional to those commonly implemented on robotic hip exoskeletons.