Wearable Device-Based Gait Recognition Using Angle Embedded Gait Dynamic Images and a Convolutional Neural Network Article Swipe
Yongjia Zhao
,
Suiping Zhou
·
YOU?
·
· 2017
· Open Access
·
· DOI: https://doi.org/10.3390/s17030478
YOU?
·
· 2017
· Open Access
·
· DOI: https://doi.org/10.3390/s17030478
The widespread installation of inertial sensors in smartphones and other wearable devices provides a valuable opportunity to identify people by analyzing their gait patterns, for either cooperative or non-cooperative circumstances. However, it is still a challenging task to reliably extract discriminative features for gait recognition with noisy and complex data sequences collected from casually worn wearable devices like smartphones. To cope with this problem, we propose a novel image-based gait recognition approach using the Convolutional Neural Network (CNN) without the need to manually extract discriminative features. The CNN’s input image, which is encoded straightforwardly from the inertial sensor data sequences, is called Angle Embedded Gait Dynamic Image (AE-GDI). AE-GDI is a new two-dimensional representation of gait dynamics, which is invariant to rotation and translation. The performance of the proposed approach in gait authentication and gait labeling is evaluated using two datasets: (1) the McGill University dataset, which is collected under realistic conditions; and (2) the Osaka University dataset with the largest number of subjects. Experimental results show that the proposed approach achieves competitive recognition accuracy over existing approaches and provides an effective parametric solution for identification among a large number of subjects by gait patterns.
Related Topics
Concepts
Gait
Discriminative model
Convolutional neural network
Computer science
Artificial intelligence
Wearable computer
Computer vision
Pattern recognition (psychology)
Inertial measurement unit
Gait analysis
Identification (biology)
Physical medicine and rehabilitation
Embedded system
Medicine
Biology
Botany
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/s17030478
- https://www.mdpi.com/1424-8220/17/3/478/pdf?version=1488282872
- OA Status
- gold
- Cited By
- 66
- References
- 33
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W2592878160
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W2592878160Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.3390/s17030478Digital Object Identifier
- Title
-
Wearable Device-Based Gait Recognition Using Angle Embedded Gait Dynamic Images and a Convolutional Neural NetworkWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2017Year of publication
- Publication date
-
2017-02-28Full publication date if available
- Authors
-
Yongjia Zhao, Suiping ZhouList of authors in order
- Landing page
-
https://doi.org/10.3390/s17030478Publisher landing page
- PDF URL
-
https://www.mdpi.com/1424-8220/17/3/478/pdf?version=1488282872Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
-
https://www.mdpi.com/1424-8220/17/3/478/pdf?version=1488282872Direct OA link when available
- Concepts
-
Gait, Discriminative model, Convolutional neural network, Computer science, Artificial intelligence, Wearable computer, Computer vision, Pattern recognition (psychology), Inertial measurement unit, Gait analysis, Identification (biology), Physical medicine and rehabilitation, Embedded system, Medicine, Biology, BotanyTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
66Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 5, 2024: 5, 2023: 5, 2022: 9, 2021: 9Per-year citation counts (last 5 years)
- References (count)
-
33Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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