Phased Human Activity Recognition based on GPS

被引：7

作者：

Sekiguchi, Ryoichi ^{[1
]}

Abe, Kenji ^{[1
]}

Suzuki, Shogo ^{[1
]}

Kumano, Masayasu ^{[1
]}

Asakura, Daisuke ^{[1
]}

Okabe, Ryo ^{[1
]}

Kariya, Takeru ^{[1
]}

Kawakatsu, Masaki ^{[1
]}

机构：

[1] Tokyo Denki Univ, Adachi Ku, 5 Senju Asahi Cho, Tokyo 1208551, Japan

来源：

UBICOMP/ISWC '21 ADJUNCT: PROCEEDINGS OF THE 2021 ACM INTERNATIONAL JOINT CONFERENCE ON PERVASIVE AND UBIQUITOUS COMPUTING AND PROCEEDINGS OF THE 2021 ACM INTERNATIONAL SYMPOSIUM ON WEARABLE COMPUTERS | 2021年

关键词：

Classification Flow; LSTM; Gradient Boosting Decision Tree; Median Filter;

D O I：

10.1145/3460418.3479382

中图分类号：

TP3 [计算技术、计算机技术];

学科分类号：

0812 ;

摘要：

This paper describes an activity recognition method for Sussex-Huawei Locomotion-Transportation (SHL) recognition challenge by team TDU_BSA_BCI. The classification accuracy has been improved by switching the estimation model, depending on whether the location is available. Data, including location were classified by Deep Neural Network including LSTM layer. Data that exclude location were classified by the Gradient Boosting Decision Tree. The 2 outputs have been combined. They were optimized by applying a median filter. In the submission phase, the best F-measure obtained for the SHL validation-set was 65%.

引用

页码：396 / 400

页数：5

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