Development of type classification system based on weight transfer for sports training

Author(s):  
Taisei Nanri ◽  
Tomoyuki Ohkubo ◽  
Kazuyuki Kobayashi
2009 ◽  
Vol 45 (2) ◽  
pp. 96-111 ◽  
Author(s):  
Christopher M. Breeding ◽  
James E. Shigley

2020 ◽  
Vol 18 (4) ◽  
pp. 91-102
Author(s):  
SeungHyeok Yang ◽  
JoonHo Kang ◽  
SangHyun Park

2021 ◽  
Vol 87 (1) ◽  
pp. 181-190
Author(s):  
Zion Hwang ◽  
James Houston ◽  
Evangelos M. Fragakis ◽  
Cristina Lupu ◽  
Jason Bernard ◽  
...  

Controversy surrounding the classification of thoracolumbar injuries has given rise to various classification systems over the years, including the most recent AOSpine Thoracolumbar Injury Classification System (ATLICS). This systematic review aims to provide an up-to-date evaluation of the literature, including assessment of a further three studies not analysed in previous reviews. In doing so, this is the first systematic review to include the reliability among non-spine subspecialty professionals and to document the wide variety between reliability across studies, particularly with regard to sub-type classification. Relevant studies were found via a systematic search of PubMed, EBESCO, Cochrane and Web of Science. Data extraction and quality assessment were conducted in line with Cochrane Collaboration guidelines. Twelve articles assessing the reliability of ATLICS were included in this review. The overall inter-observer reliability varied from fair to substantial, but the three additional studies in this review, compared to previous reviews, presented on average only fair reliability. The greatest variation of results was seen in A1 and B3 subtypes. Least reliably classified on average was A4 subtype. This systematic review concludes that ATLICS is reliable for the majority of injuries, but the variability within subtypes suggests the need for further research in assessing the needs of users in order to increase familiarity with ATLICS or perhaps the necessity to include more subtype-specific criteria into the system. Further research is also recommended on the reliability of modifiers, neurological classification and the application of ATLICS in a paediatric context.


Medicine ◽  
2015 ◽  
Vol 94 (32) ◽  
pp. e1280 ◽  
Author(s):  
Chang-Ming Huang ◽  
Rui-Fu Chen ◽  
Qi-Yue Chen ◽  
Jin Wei ◽  
Chao-Hui Zheng ◽  
...  

Author(s):  
Wahyono Wahyono ◽  
Joko Hariyono

 Convolutional neural network is a machine learning that provides a good accura-cy for many problems in the field of computer vision, such as segmentation, de-tection, recognition, as well as classification systems. However, the results and performance of the system are affected by the CNN architecture. In this paper, we propose the utilization of evolutionary computation using genetic algorithm to de-termine the optimal architecture for CNN with transfer learning strategy from parent network. Furthermore, the optimal CNN produced is used as a model for the case of the vehicle type classification system. To evaluate the effectiveness of the utilization of evolutionary computing to CNN, the experiment will be conducted using vehicle classification datasets.


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