Art
J-GLOBAL ID:201902222419522116
Reference number:19A1932044
歩容に頑健なPDRのための深層学習を用いた歩行速度推定手法
Author (6):
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Material:
Volume:
2019
Issue:
UBI-63
Page:
Vol.2019-UBI-63,No.3,1-7 (WEB ONLY)
Publication year:
Aug. 19, 2019
JST Material Number:
U0451A
Document type:
Proceedings
Article type:
短報
Country of issue:
Japan (JPN)
Language:
JAPANESE (JA)
Thesaurus term:
Thesaurus term/Semi thesaurus term
Keywords indexed to the article.
All keywords is available on JDreamIII(charged).
On J-GLOBAL, this item will be available after more than half a year after the record posted. In addtion, medical articles require to login to MyJ-GLOBAL.
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Semi thesaurus term:
Thesaurus term/Semi thesaurus term
Keywords indexed to the article.
All keywords is available on JDreamIII(charged).
On J-GLOBAL, this item will be available after more than half a year after the record posted. In addtion, medical articles require to login to MyJ-GLOBAL.
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JST classification (3):
JST classification
Category name(code) classified by JST.
Pattern recognition
, Artificial intelligence
, Measuring methods and instruments of time,speed,acceleration,angular veloc?
Reference (16):
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Harle, R.: A Survey of Indoor Inertial Positioning Systems for Pedestrians, IEEE Communications Surveys Tutorials, Vol. 15, No. 3, pp. 1281-1293 (2013).
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Correa Vila, A., Barceló, M., Morell, A. and Lopez Vicario, J.: A Review of Pedestrian Indoor Positioning Systems for Mass Market Applications, Sensors (Switzerland), Vol. 17 (2017).
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Sakpere, W., Oshin, M. A. and Mlitwa, N.: A State-of-the-Art Survey of Indoor Positioning and Navigation Systems and Technologies, South African Computer Journal, Vol. 29, No. 3 (online), available from <http://sacj.cs.uct.ac.za/index.php/sacj/article/view/452> (2017).
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Roberto, R., Lima, J. P., Araújo, T. and Teichrieb, V.: Evaluation of Motion Tracking and Depth Sensing Accuracy of the Tango Tablet, 2016 IEEE International Symposium on Mixed and Augmented Reality (ISMAR-Adjunct), pp. 231-234 (2016).
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Francisco JavierOrd‘o nez,Roggen, D.: Deep Convolutional and LSTM Recurrent Neural Networks for Multimodal Wearable Activity Recognition, Sensors, Vol. 16, No. 1, p. 115 (2016).
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