Search results

This paper aims to propose a novel balance-assistive control strategy for hip exoskeleton robot.

A hierarchical balance assistive controller based on the virtual stiffness model of extrapolated center of mass (XCoM) is proposed and tested by exoskeleton balance assistive control experiments.

Experiment results show that the proposed controller can accelerate the swing foot chasing XCoM and enlarge the margin of stability.

As a proof of concept, this paper shows the potential for exoskeleton to actively assist human regain balance in sagittal plane when human suffers from a forward or backward disturbing force.

This paper aims at the problem of attaching the data of doctors, patients and the real-time sensor data of the exoskeleton to the cloud in intelligent rehabilitation applications. This study designed the exoskeleton cloud-brain platform and validated its safety assessment.

According to the dimension of data and the transmission speed, this paper implements a three-layer cloud-brain platform of exoskeleton based on Alibaba Cloud's Lambda-like architecture. At the same time, given the human–machine safety status detection problem of the exoskeleton, this paper built a personalized machine-learning safety detection module for users with the multi-dimensional sensor data cloned by the cloud-brain platform. This module includes an abnormality detection model, prediction model and state classification model of the human–machine state.

These functions of the exoskeleton cloud-brain and the algorithms based on it were validated by the experiments, they meet the needs of use.

This thesis innovatively proposes a cloud-brain platform for exoskeletons, beginning the digitalization and intelligence of the exoskeletal rehabilitation process and laying the foundation for future intelligent assistance systems.