Using a Deep Learning Method and Data from Two-Dimensional (2D) Marker-Less Video-Based Images for Walking Speed Classification

Human body measurement data related to walking can characterize functional movement and thereby become an important tool for health assessment. Single-camera-captured two-dimensional (2D) image sequences of marker-less walking individuals might be a simple approach for estimating human body measurem...

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Bibliographic Details
Published in:Sensors (Basel, Switzerland) Switzerland), 2021-04, Vol.21 (8), p.2836
Main Authors: Sikandar, Tasriva, Rabbi, Mohammad F, Ghazali, Kamarul H, Altwijri, Omar, Alqahtani, Mahdi, Almijalli, Mohammed, Altayyar, Saleh, Ahamed, Nizam U
Format: Article
Language:English
Subjects:
2D image
Accuracy
Aged
Alzheimer's disease
Artificial intelligence
Body measurements
Cameras
Classification
Datasets
Deep Learning
Gait
Garments
Human body
Human motion
Humans
Image classification
Indoor environments
Kinematics
LSTM
marker-less video
Markers
Mortality
Movement
Neural networks
quasi-periodic pattern
Two dimensional bodies
Video data
Walking
Walking Speed
walking speed classification
walking speed pattern
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Summary:Human body measurement data related to walking can characterize functional movement and thereby become an important tool for health assessment. Single-camera-captured two-dimensional (2D) image sequences of marker-less walking individuals might be a simple approach for estimating human body measurement data which could be used in walking speed-related health assessment. Conventional body measurement data of 2D images are dependent on body-worn garments (used as segmental markers) and are susceptible to changes in the distance between the participant and camera in indoor and outdoor settings. In this study, we propose five ratio-based body measurement data that can be extracted from 2D images and can be used to classify three walking speeds (i.e., slow, normal, and fast) using a deep learning-based bidirectional long short-term memory classification model. The results showed that average classification accuracies of 88.08% and 79.18% could be achieved in indoor and outdoor environments, respectively. Additionally, the proposed ratio-based body measurement data are independent of body-worn garments and not susceptible to changes in the distance between the walking individual and camera. As a simple but efficient technique, the proposed walking speed classification has great potential to be employed in clinics and aged care homes.
ISSN:1424-8220
1424-8220
DOI:10.3390/s21082836