Title
Development of a Novel Sensor Unit to build up Massage Manipulation for Medical Training
Abstract
The traditional Chinese medicine massage has a long history and the efficacy has been wildly acknowledged, especially for childhood diarrhea treatment. As same as the traditional Chinese medicine, there is lack of quantitative evaluation methods for Chinese medicine staff training. With the development of information detection technology, a variety of equipment for the detection of massage techniques has been proposed, which makes it possible to quantify massage operation. However, since there are no embedded sensors, most of the manipulations cannot feedback the operation information of trainee. And the massage training still needs follow the directions of the supervisor. This situation caused the insufficient of medical training on mastering massage skills. In this paper, a massage manipulation with a novel sensor unit embedded is proposed. The sensor unit is composed of four thin film press sensors as a group. Take advantage of the sensor unit, not only the pressure but also the operation sequence can be detected. A testing apparatus mimicking the abdominal muscle tissue is carried out to examine the abilities of proposed sensor unit. Experiments with the apparatus show that the proposed idea of the sensor unit can fulfil the evaluation of massage operation with a simplified structure. All the results lead to the further researches on the quantitative medical education training methods of massage operation.
Year
DOI
Venue
2018
10.1109/IISR.2018.8535836
2018 IEEE International Conference on Intelligence and Safety for Robotics (ISR)
Keywords
DocType
ISBN
massage manipulation,novel sensor unit,childhood diarrhea treatment,quantitative evaluation methods,information detection technology,embedded sensors,chinese medicine massage,medical education training methods,abdominal muscle tissue
Conference
978-1-5386-5548-1
Citations 
PageRank 
References 
0
0.34
5
Authors
8
Name
Order
Citations
PageRank
Xiaojiao Chen100.68
Chunbao Wang200.68
Quanquan Liu377.90
Gong Chen400.68
Jianjun Long501.69
yulong wang603.38
Lihong Duan700.34
Wanfeng Shang8144.69