Recent Patents on End Traction Upper Limb Rehabilitation Robot

Jiang, Jingang; Huo, Biao; Han, Yingshuai; Yu, Xiaoyang; Guo, Xiaowei

doi:10.2174/2212797610666170411104024

Cited by 3 publications

(1 citation statement)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…First of all, medical staff select different rehabilitation tasks for multiple patients from the system's rehabilitation task target library through the master control software, and assign them to multiple patients at the same time. Each patient's rehabilitation platform and auxiliary rehabilitation environment are the same, and the auxiliary rehabilitation process can be Realize unmanned and self-service, and doctors can monitor the rehabilitation process of each patient at any time through the main control terminal software and camera [41]. According to the evaluation method of cognitive ability in the Webster's Adult Intelligence Scale, it can be concluded that building blocks based on building can realize the training of cognitive functions such as memory, attention, and visual space.…”

Section: B Functional Requirements Of Rehabilitation Robot Systemmentioning

confidence: 99%

Retracted: Target Recognition Method of Rehabilitation Robot Based on Image Local Features

Xing

2020

IEEE Access

View full text Add to dashboard Cite

Target recognition technology is an important topic in the field of artificial intelligence, which is widely used in fields such as medicine, robot vision, highway traffic, and VR technology. In the robot vision system, the target recognition technology requires high precision, real-time, and practicality, and it must be able to cope with the harsh recognition environment on site. This paper mainly studies the target recognition method of rehabilitation robot based on image local features. In this paper, a rehabilitation robot recognition system is established based on the local features of the image and the target recognition technology. Firstly, after collecting or processing the initially prepared images, feature extraction is performed, and the feature vectors extracted from the features are classified and recognized and then transferred to the recognition model of the rehabilitation robot. Then, according to the functional requirements of the rehabilitation robot recognition system, the "one-to-many" rehabilitation mode and real-time system monitoring are realized to accurately identify the target state. Finally, the Harris algorithm is used to convert the predetermined image set into a grayscale image, and the Gaussian distribution is used uniformly to determine the location and proportion of the feature points, and extract the corresponding image.The Harris matching method used in this article is very effective and has the highest accuracy. The number of correct matches accounted for 58.3%. Compared with the SIFT algorithm and the Harris+SIFT algorithm, the accuracy rate increases by 12.3% and 14.4%, respectively. The recall rate increased by 13.2% and 8.9% respectively. The experimental results show that the target recognition technology of rehabilitation robot based on image local features is more accurate than general technology.

show abstract

Section: B Functional Requirements Of Rehabilitation Robot Systemmentioning

confidence: 99%