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Sensors 2017, 17(6), 1214; doi:10.3390/s17061214

DeepMap+: Recognizing High-Level Indoor Semantics Using Virtual Features and Samples Based on a Multi-Length Window Framework

and
†,*
College of Computer Science and Electronic Engineering, Hunan University, Changsha 410000, China
These authors contributed equally to this work.
*
Author to whom correspondence should be addressed.
Academic Editor: Gert F. Trommer
Received: 25 February 2017 / Revised: 21 May 2017 / Accepted: 22 May 2017 / Published: 26 May 2017
(This article belongs to the Section Physical Sensors)

Abstract

Existing indoor semantic recognition schemes are mostly capable of discovering patterns through smartphone sensing, but it is hard to recognize rich enough high-level indoor semantics for map enhancement. In this work we present DeepMap+, an automatical inference system for recognizing high-level indoor semantics using complex human activities with wrist-worn sensing. DeepMap+ is the first deep computation system using deep learning (DL) based on a multi-length window framework to enrich the data source. Furthermore, we propose novel methods of increasing virtual features and virtual samples for DeepMap+ to better discover hidden patterns of complex hand gestures. We have performed 23 high-level indoor semantics (including public facilities and functional zones) and collected wrist-worn data at a Wal-Mart supermarket. The experimental results show that our proposed methods can effectively improve the classification accuracy. View Full-Text
Keywords: indoor semantic inference; activity recognition; multi-length windows; virtual samples; virtual features; deep learning indoor semantic inference; activity recognition; multi-length windows; virtual samples; virtual features; deep learning
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Zhang, W.; Zhou, S. DeepMap+: Recognizing High-Level Indoor Semantics Using Virtual Features and Samples Based on a Multi-Length Window Framework. Sensors 2017, 17, 1214.

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