Hybrid and Convolutional Neural Networks for Locomotion Recognition

被引：8

作者：

Osmani, Aomar ^{[1
]}

Hamidi, Massinissa ^{[1
]}

机构：

[1] PRES Sorbonne Paris Cite, LIPN UMR CNRS 7030, F-93430 Villetaneuse, France

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：

Locomotion recognition; convolutional and recurrent neural networks; neural architecture search;

D O I：

10.1145/3267305.3267520

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

This paper explores the relevance of an approach based exclusively on deep neural networks for locomotion recognition. This work is done within the Sussex-Huawei Locomotion-Transportation (SHL) recognition challenge as team Power of Things. Provided data used during the experiments is part of the SHL dataset for which we emphasize the adaptability to different applications of the ubiquitous computing. This quality emerges from the broad spectrum of modalities that this dataset encompasses, they are 16 in total. More than 500 different convolutional and hybrid architectures are evaluated, and a Bayesian optimization procedure is used for hyper-parameters space exploration. The influence of these hyper-parameters on performances is analyzed using the fANOVA framework. Best models achieve a recognition rate of about 92% measured by the f1 score.

引用

页码：1531 / 1540

页数：10

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