PILaN: Generating Task-Individual Independent Customized Assistive Control on a Hip-Knee Powered Exoskeleton

Abstract

Generating task-individual independent customized assistive control is a big challenge of wearable powered exoskeletons, which usually relies on accurate and robust motion intention perceptions (MIP) of the wearer's limb. Traditional physics-based models and deep learning models, as two commonly used MIP methods, suffer from poor generalization and strong data dependence, respectively. To overcome these limitations, in this study, we propose a physics-informed neural network (PINN) model named PILaN, which integrates the Lagrange dynamics with a deep learning model, to realize lower limb motion intention perception-based (LLMIP-based) assistive control on a hip-knee powered exoskeleton (HKPE). The proposed PILaN trained by a self-collected small scale dataset is capable of conducting 2-degree of freedom (DoF) lower limb dynamics estimation (LLDE) with the current hip and knee joint states, and using LLDE results to obtain the next joint state according to the Lagrangian dynamics. The assistive control of applied HKPE depends on the estimated joint states from the PILaN. Eight participants are invited to perform designed motion sequences consisting of one or multiple actions with the assistance of the HKPE. Experimental results demonstrate that our proposed PILaN can successfully provide accurate and robust LLMIP for the HKPE assistive control under task-individual independent conditions.