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

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