High-Fidelity, Customizable Force Sensing for the Wearable Human-Robot Interface

TL;DR

This paper introduces a customizable, high-fidelity force sensing method using fluidic innervation embedded in 3D-printed silicone pads, enabling accurate measurement of human-robot interface forces in various settings.

Contribution

The study presents a novel fluidic innervation sensing technique integrated into wearable devices, demonstrating high linearity, adaptability, and potential for real-time human-machine interaction monitoring.

Findings

Pad pressure correlates strongly with applied force ($R^2=0.998$).

Pressure correlates with knee torque during clinical tests ($R^2=0.95$).

Sensor tracks movement and task phases during dynamic activities.

Abstract

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.