Remote intelligent Brunnstrom assessment system for upper limb rehabilitation for post-stroke based on extreme learning machine.
- Author:
Yue WANG
;
Lei YU
;
Jianming FU
;
Qiang FANG
- Publication Type:Journal Article
- MeSH:
Algorithms;
Arm;
physiopathology;
Artificial Intelligence;
Humans;
Movement;
Recovery of Function;
Reproducibility of Results;
Software;
Stroke Rehabilitation
- From:
Journal of Biomedical Engineering
2014;31(2):251-256
- CountryChina
- Language:Chinese
-
Abstract:
In order to realize an individualized and specialized rehabilitation assessment of remoteness and intelligence, we set up a remote intelligent assessment system of upper limb movement function of post-stroke patients during rehabilitation. By using the remote rehabilitation training sensors and client data sampling software, we collected and uploaded the gesture data from a patient's forearm and upper arm during rehabilitation training to database of the server. Then a remote intelligent assessment system, which had been developed based on the extreme learning machine (ELM) algorithm and Brunnstrom stage assessment standard, was used to evaluate the gesture data. To evaluate the reliability of the proposed method, a group of 23 stroke patients, whose upper limb movement functions were in different recovery stages, and 4 healthy people, whose upper limb movement functions were normal, were recruited to finish the same training task. The results showed that, compared to that of the experienced rehabilitation expert who used the Brunnstrom stage standard table, the accuracy of the proposed remote Brunnstrom intelligent assessment system can reach a higher level, as 92.1%. The practical effects of surgery have proved that the proposed system could realize the intelligent assessment of upper limb movement function of post-stroke patients remotely, and it could also make the rehabilitation of the post-stroke patients at home or in a community care center possible.
