English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Millions of people worldwide suffer from stroke each year. One way to assist patients cost-effectively during their rehabilitation process is using end-effector-based robot-assisted rehabilitation. Such systems allow patients to use their own movement strategies to perform a movement task, which encourages them to do self-motivated training but also allow compensation movements if they have problems executing the movement tasks. Therefore, a patient supervision system was developed on the basis of inertial measurement units and a patient-tailored movement interpretation system. Very light and small inertial measurement units were developed to record the patients’ movements during a teaching phase in which the desired movement is shown to the patient by a physiotherapist. During a following exercise phase, the patient is training the previously shown movement alone with the help of an end-effector-based robot-assisted rehabilitation system, and the patient’s movement is recorded again. The data from the teaching and exercise phases are compared with each other and evaluated by using fuzzy logic tailored to each patient. Experimental tests with one healthy subject and one stroke patient showed the capability of the system to supervise patient movements during the robot-assisted end-effector-based rehabilitation.

A proposal for patient-tailored supervision of movement performance during end-effector-based robot-assisted rehabilitation of the upper extremities