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

共 50 条

[1] Accelerometer and GPS Sensor Combination Based System for Human Activity Recognition
Kaghyan, Sahak
Sarukhanyan, Hakob
2013 COMPUTER SCIENCE AND INFORMATION TECHNOLOGIES (CSIT), 2013,
[2] Cost Sensitive GPS-based Activity Recognition
Huang, Wenhao
Li, Man
Hu, Weisong
Song, Guojie
Xing, Xingxing
Xie, Kunqing
2013 10TH INTERNATIONAL CONFERENCE ON FUZZY SYSTEMS AND KNOWLEDGE DISCOVERY (FSKD), 2013, : 962 - 966
[3] HHMM based recognition of human activity
Kawanaka, Daiki
Okatani, Takayuki
Deguchi, Koichiro
IEICE TRANSACTIONS ON INFORMATION AND SYSTEMS, 2006, E89D (07): : 2180 - 2185
[4] Human activity recognition based on LPA
Li, Ruixiang
Li, Hui
Shi, Weibin
MULTIMEDIA TOOLS AND APPLICATIONS, 2020, 79 (41-42) : 31069 - 31086
[5] Human activity recognition based on LPA
Ruixiang Li
Hui Li
Weibin Shi
Multimedia Tools and Applications, 2020, 79 : 31069 - 31086
[6] Human Activity Recognition Based on Similarity
Zhu, Yangda
Wang, Changhai
Zhang, Jianzhong
Xu, Jingdong
2014 IEEE 17TH INTERNATIONAL CONFERENCE ON COMPUTATIONAL SCIENCE AND ENGINEERING (CSE), 2014, : 1382 - 1387
[7] Hierarchical conditional random fields for GPS-Based activity recognition
Liao, Lin
Fox, Dieter
Kautz, Henry
ROBOTICS RESEARCH, 2007, 28 : 487 - +
[8] Human Activity Recognition Based on Human Shape Dynamics
Cheng, Zhiqing
Mosher, Stephen
Cheng, Huaining
Webb, Timothy
SIGNAL PROCESSING, SENSOR FUSION, AND TARGET RECOGNITION XXII, 2013, 8745
[9] Human Activity Recognition Based on Random Forests
Xu, Lu
Yang, Weidu
Cao, Yueze
Li, Quanlong
2017 13TH INTERNATIONAL CONFERENCE ON NATURAL COMPUTATION, FUZZY SYSTEMS AND KNOWLEDGE DISCOVERY (ICNC-FSKD), 2017, : 548 - 553
[10] Human Activity Recognition Based on Silhouette Directionality
Singh, Meghna
Basu, Anup
Mandal, Mrinal Kr.
IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY, 2008, 18 (09) : 1280 - 1292

← 1 2 3 4 5 →