Activity Recognition: Translation across Sensor Modalities Using Deep Learning

被引：5

作者：

Okita, Tsuyoshi ^{[1
]}

Inoue, Sozo ^{[1
]}

机构：

[1] Kyushu Inst Technol, 1-1 Sensui Cho, Kitakyushu, Fukuoka 8048550, Japan

来源：

PROCEEDINGS OF THE 2018 ACM INTERNATIONAL JOINT CONFERENCE ON PERVASIVE AND UBIQUITOUS COMPUTING AND PROCEEDINGS OF THE 2018 ACM INTERNATIONAL SYMPOSIUM ON WEARABLE COMPUTERS (UBICOMP/ISWC'18 ADJUNCT) | 2018年

关键词：

Activity Recognition; Deep Learning; Multi-modality;

D O I：

10.1145/3267305.3267512

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

We propose a method to translate between multi-modalities using an RNN encoder-decoder model. Based on such a model allowing to translate between modalities, we built an activity recognition system. The idea of equivalence of modality was investigated by Banos et al. This paper replaces this with deep learning. We compare the performance of translation with/without clustering and sliding window. We show the preliminary performance of activity recognition attained the F1 score of 0.78.

引用

页码：1462 / 1471

页数：10

共 50 条

[21] Wireless body area sensor networks based human activity recognition using deep learning
Ehab El-Adawi
Ehab Essa
Mohamed Handosa
Samir Elmougy
[J]. Scientific Reports, 14
[22] Wearable Sensor-Based Human Activity Recognition Using Hybrid Deep Learning Techniques
Wang, Huaijun
Zhao, Jing
Li, Junhuai
Tian, Ling
Tu, Pengjia
Cao, Ting
An, Yang
Wang, Kan
Li, Shancang
[J]. SECURITY AND COMMUNICATION NETWORKS, 2020, 2020
[23] Deep Transfer Learning Using Class Augmentation for Sensor-Based Human Activity Recognition
Kondo, Kazuma
Hasegawa, Tatsuhito
[J]. IEEE SENSORS LETTERS, 2022, 6 (10)
[24] Wireless body area sensor networks based human activity recognition using deep learning
El-Adawi, Ehab
Essa, Ehab
Handosa, Mohamed
Elmougy, Samir
[J]. SCIENTIFIC REPORTS, 2024, 14 (01)
[25] Gas Recognition Under Sensor Drift by Using Deep Learning
Hu, Xiaonan
Liu, Qihe
Cai, Hongbin
Li, Fan
[J]. PRACTICAL APPLICATIONS OF INTELLIGENT SYSTEMS, ISKE 2013, 2014, 279 : 23 - 33
[26] Gas Recognition under Sensor Drift by Using Deep Learning
Liu, Qihe
Hu, Xiaonan
Ye, Mao
Cheng, Xianqiong
Li, Fan
[J]. INTERNATIONAL JOURNAL OF INTELLIGENT SYSTEMS, 2015, 30 (08) : 907 - 922
[27] Human Activity Recognition Based on Deep Learning Regardless of Sensor Orientation
He, Zhenyu
Sun, Yulin
Zhang, Zhen
[J]. APPLIED SCIENCES-BASEL, 2024, 14 (09):
[28] Human activity recognition using deep electroencephalography learning
Salehzadeh, Amirsaleh
Calitz, Andre P.
Greyling, Jean
[J]. BIOMEDICAL SIGNAL PROCESSING AND CONTROL, 2020, 62
[29] Human Activity Recognition in Videos Using Deep Learning
Kumar, Mohit
Rana, Adarsh
Ankita
Yadav, Arun Kumar
Yadav, Divakar
[J]. SOFT COMPUTING AND ITS ENGINEERING APPLICATIONS, ICSOFTCOMP 2022, 2023, 1788 : 288 - 299
[30] Deep Learning for Activity Recognition Using Audio and Video
Reinolds, Francisco
Neto, Cristiana
Machado, Jose
[J]. ELECTRONICS, 2022, 11 (05)

← 1 2 3 4 5 →