ArticleSensors (Basel, Switzerland)2021
Using a Deep Learning Method and Data from Two-Dimensional (2D) Marker-Less Video-Based Images for Walking Speed Classification.
Article in Sensors (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed, 13 citations in OpenAlex.
- Predicting In-Hospital Fall Risk Using Machine Learning With Real-Time Location System and Electronic Medical Records.Journal of cachexia, sarcopenia and muscle · 2025Article
- A Comprehensive Review of Vision-Based Sensor Systems for Human Gait Analysis.Sensors (Basel, Switzerland) · 2025Review
- Introduction of AI Technology for Objective Physical Function Assessment.Bioengineering (Basel, Switzerland) · 2024Review
- Integrating OpenPose and SVM for Quantitative Postural Analysis in Young Adults: A Temporal-Spatial Approach.Bioengineering (Basel, Switzerland) · 2024Article
- Pedestrian walking speed monitoring at street scale by an in-flight drone.PeerJ. Computer science · 2023Article
- Walking Speed Classification from Marker-Free Video Images in Two-Dimension Using Optimum Data and a Deep Learning Method.Bioengineering (Basel, Switzerland) · 2022Article
- Deep Learning Methods for Speed Estimation of Bipedal Motion from Wearable IMU Sensors.Sensors (Basel, Switzerland) · 2022Article
Corrections and comments
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Authors and funding
8 authors at 4 institutions in 4 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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.
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