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Junto Nozaki

dblp:205/5838 · DBLP profile ↗
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1ranked-venue papers
0as first author
0since 2021 · last 2019
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Computer networks · 1

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Human-computer interaction and pervasive computing
1 paper
Wearable and physiological sensing · 100%

Topics — the 3 heaviest of 3, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Wearable and physiological sensing › inertial sensing
pedestrian dead reckoning
0.412019
Gait Dependency of Smartphone Walking Speed Estimation using Deep Learning · MobiSys 2019
Wearable and physiological sensing › gait analysis
walking speed estimation
0.412019
Gait Dependency of Smartphone Walking Speed Estimation using Deep Learning · MobiSys 2019
Wearable and physiological sensing › inertial sensing
smartphone inertial sensing
0.112019
Gait Dependency of Smartphone Walking Speed Estimation using Deep Learning · MobiSys 2019

Methods — techniques the papers use, named apart from their topics

deep learning · 0.4
YearPublicationVenuePosition
2019 Gait Dependency of Smartphone Walking Speed Estimation using Deep Learning
abstract
This paper proposes an accurate estimation method of walking speed using deep learning for smartphone-based Pedestrian Dead Reckoning (PDR).PDR requires to estimate speed and direction of pedestrians accurately using accelerometer and gyroscope.To improve the accuracy of PDR, existing works focused to improve the key factors of speed estimation (i.e., stride and/or step estimation) by adapting deep learning.On the contrary, our research proposes to adapt deep learning more directly to estimate walking speed from sensor data of smartphone. We evaluate the accuracy of proposed method by comparing with conventional PDR method. As a result, we confirmed that proposed method can estimate the speed more accurately.
Takuto Yoshida, Junto Nozaki, Kenta Urano, Kei Hiroi, Takuro Yonezawa, Nobuo Kawaguchi
MobiSys2