Mechanically characterizing the human-machine interface is essential to understanding user behavior and optimizing wearable robot performance. This interface has been challenging to sensorize due to manufacturing complexity and non-linear sensor responses. Here, we measure human limb-device interaction via fluidic innervation, creating a 3D-printed silicone pad with embedded air channels to measure forces. As forces are applied to the pad, the air channels compress, resulting in a pressure change measurable by off-the-shelf pressure transducers. We demonstrate in benchtop testing that pad pressure is highly linearly related to applied force ($R^2 = 0.998$). This is confirmed with clinical dynamometer correlations with isometric knee torque, where above-knee pressure was highly correlated with flexion torque ($R^2 = 0.95$), while below-knee pressure was highly correlated with extension torque ($R^2 = 0.75$). We build on these idealized settings to test pad performance in more unconstrained settings. We place the pad over \textit{biceps brachii} during cyclic curls and stepwise isometric holds, observing a correlation between pressure and elbow angle. Finally, we integrated the sensor into the strap of a lower-extremity robotic exoskeleton and recorded pad pressure during repeated squats with the device unpowered. Pad pressure tracked squat phase and overall task dynamics consistently. Overall, our preliminary results suggest fluidic innervation is a readily customizable sensing modality with high signal-to-noise ratio and temporal resolution for capturing human-machine mechanical interaction. In the long-term, this modality may provide an alternative real-time sensing input to control / optimize wearable robotic systems and to capture user function during device use.

翻译：对人机交互界面进行力学表征对于理解用户行为及优化可穿戴机器人性能至关重要。由于制造复杂性及传感器非线性响应，该界面的传感化一直面临挑战。本文通过流体神经支配技术测量人体肢体与设备间的相互作用，创建了一种嵌入空气通道的3D打印硅胶垫以测量作用力。当垫体受到外力时，空气通道受压形变，产生可由商用压力传感器测量的压力变化。我们在台架测试中证明，垫体压力与施加力呈高度线性关系（$R^2 = 0.998$）。该结论通过临床测力计与等长膝关节扭矩的关联性测试得到验证：膝上压力与屈曲扭矩高度相关（$R^2 = 0.95$），而膝下压力与伸展扭矩高度相关（$R^2 = 0.75$）。基于这些理想化场景，我们进一步在非约束条件下测试垫体性能。将传感垫置于肱二头肌进行周期性弯举和阶梯式等长收缩测试，观察到压力与肘关节角度的相关性。最后，我们将传感器集成至下肢机器人外骨骼的绑带中，在设备未供电状态下记录重复深蹲过程中的垫体压力。垫体压力能持续追踪深蹲阶段及整体任务动态。总体而言，我们的初步结果表明，流体神经支配是一种易于定制的传感模式，具有高信噪比和时间分辨率，适用于捕捉人机机械交互。长期来看，该模式或可为控制/优化可穿戴机器人系统及捕捉设备使用过程中的用户功能提供替代性实时传感输入。