Design and Control of an Ergonomic Wearable Full-Wrist Exoskeleton for Pathological Tremor Alleviation

Activities of daily living (ADL) such as writing, eating, and object manipulation are challenging for patients suffering from pathological tremors. Pathological tremors are involuntary, rhythmic, and oscillatory movements that manifest in limbs, the head, and other body parts. Among the existing treatments, mechanical loading through wearable rehabilitation devices is popular for being non-invasive and innocuous to the human body. In particular, a few exoskeletons are developed to actively mitigate pathological tremors in the forearm. While these forearm exoskeletons can effectively suppress tremors, they still require significant improvements in ergonomics to be implemented for ADL applications. The ergonomics of the exoskeleton can be improved via design and motion control pertaining to human biomechanics, which leads to better efficiency, comfort, and safety for the user.

The wrist is a complicated biomechanical joint with two coupled degrees of freedom (DOF) pivotal to human manipulation capabilities. Existing exoskeletons either do not provide tremor suppression in all wrist DOFs, or can be restrictive to the natural wrist movement. This motivates us to explore a better exoskeleton solution for wrist tremor suppression. We propose TAWE - a wearable exoskeleton that provides alleviation of pathological tremors in all wrist DOFs. The design adopts a 6-DOF rigid linkage mechanism to ensure unconstrained natural wrist movements, and wearability features without extreme tight-binding or precise positioning for convenient ADL applications.

When TAWE is equipped by the user, a closed-kinematic chain is formed between the exoskeleton and the forearm. We analyze the coupled multibody dynamics of the human-exoskeleton system, which reveals a few robotic control problems - (i) The first problem is the identification of the unknown wrist kinematics within the closed kinematic chain. We realize the real-time wrist kinematic identification (WKI) based on a novel ellipsoidal joint model that describes the coupled wrist kinematics, and a sparsity-promoting Extended Kalman Filter for the efficient real-time regression; (ii) The second problem is the exoskeleton motion control for tremor suppression. We design a robust adaptive controller (IO-RAC) based on model reference adaptive control and inverse optimal robust control theories, which can identify the unknown model inertia and load, and provide stable tracking control under disturbance; (iii) The third problem is the estimation of voluntary movement from tremorous motion data for the motion planning of exoskeleton. We develop a lightweight and data-driven voluntary movement estimator (SVR-VME) based on least square support vector regression, which can estimate voluntary movements with real-time signal adaptability and significantly reduced time delay.

Simulations and experiments are carried out to test the individual performance of robotic control algorithms proposed in this study, and their combined real-time performance when integrated into the full exoskeleton control system. We also manufacture the prototype of TAWE, which helps us validate the proposed solutions in tremor alleviation exoskeletons. Overall, the design of TAWE meets the expectations in its compliance with natural wrist movement and simple wearability. The exoskeleton control system can execute stably in real-time, identify unknown system kinematics and dynamics, estimate voluntary movements, and suppress tremors in the wrist. The results also indicate a few limitations in the current approaches, which require further investigations and improvements. Finally, the proposed exoskeleton control solutions are developed based on generic formulations, which can be applied to not only TAWE, but also other rehabilitation exoskeletons.