High-Fidelity, Customizable Force Sensing for the Wearable Human-Robot Interface
🤖 AI Summary
This work proposes a soft force-sensing approach inspired by fluidic innervation for high-fidelity, customizable tactile interfaces in wearable human-machine systems, addressing limitations imposed by fabrication complexity and sensor nonlinearity. The method employs 3D-printed silicone structures embedded with microfluidic channels, transducing applied forces into pressure changes measured by off-the-shelf pressure sensors to achieve highly linear, high signal-to-noise ratio, and temporally precise force readings. Benchtop characterization demonstrates an exceptionally linear force–pressure relationship (R² = 0.998). Clinical validation shows strong correlations between measured and actual knee flexion and extension torques (R² = 0.95 and 0.75, respectively) and accurate tracking of joint angles and movement phases during bicep curls and squats, confirming its suitability for complex, real-world wearable applications.
Application Category
📝 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.
Problem
Research questions and friction points this paper is trying to address.
force sensing
wearable human-robot interface
mechanical interaction
sensor nonlinearity
customizable sensing
Innovation
Methods, ideas, or system contributions that make the work stand out.
fluidic innervation
wearable human-robot interface
3D-printed force sensor
high-fidelity force sensing
customizable soft sensor
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