Authors
Joo Kooi Tan1, *, Tomoyuki Kurosaki2
1Faculty of Engineering, Kyushu Institute of Technology, 1-1 Sensuicho,
Tobata, Kitakyushu, Fukuoka 804-8550, Japan
2Graduate School of Engineering, Kyushu Institute of Technology, 1-1 Sensuicho,
Tobata, Kitakyushu, Fukuoka 804-8550, Japan
*Corresponding author. Email: [email protected]
Corresponding Author
Joo Kooi Tan
Received 10 November 2019, Accepted 18 May 2020, Available Online 11 September
2020.
DOI
https://doi.org/10.2991/jrnal.k.200909.002
Keywords
Posture; posture analysis; pedestrian; MY VISION; depth and motion network
Abstract
A system is proposed that performs gait analysis of a pedestrian to improve
a walk posture and at the same time to prevent a fall. In the system, a
user walks with a chest-mounted camera. His/her walking posture is estimated
using a pair of images obtained from the camera. Normally it is difficult
to estimate the camera movement, when the parallax of the image pair is
small. Therefore, the system uses a convolutional neural network. Optical
flow and camera movement, and depth images are estimated alternately. Satisfactory
results were obtained experimentally.
Copyright
© 2020 The Authors. Published by ALife Robotics Corp. Ltd.
Open Access
This is an open access article distributed under the CC BY-NC 4.0 license
(http://creativecommons.org/licenses/by-nc/4.0/).