ArticleSensors (Basel, Switzerland)2022
Development of Smartphone Application for Markerless Three-Dimensional Motion Capture Based on Deep Learning Model.
Article in Sensors (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- Application of Deep Learning-Based Markerless Pose Estimation in a 3D Exergame System for Knee Osteoarthritis Exercise Management: Development and Preliminary Evaluation.Sensors (Basel, Switzerland) · 2026Article
- Video-Based Motion Capture Smartphone Apps for Testing Human Motor Performance Skills: Scoping Review.JMIR mHealth and uHealth · 2026Article
- Camera-based mobile applications for movement screening in healthy adults: a systematic review.Frontiers in sports and active living · 2025Review
- Development of a Gait Analysis Application for Assessing Upper and Lower Limb Movements to Detect Pathological Gait.Sensors (Basel, Switzerland) · 2024Article
- Classification of Fashion Models' Walking Styles Using Publicly Available Data, Pose Detection Technology, and Multivariate Analysis: From Past to Current Trendy Walking Styles.Sensors (Basel, Switzerland) · 2024Article
- Article
- Fluctuations in Upper and Lower Body Movement during Walking in Normal Pressure Hydrocephalus and Parkinson's Disease Assessed by Motion Capture with a Smartphone Application, TDPT-GT.Sensors (Basel, Switzerland) · 2023Article
- Artificial Intelligence Distinguishes Pathological Gait: The Analysis of Markerless Motion Capture Gait Data Acquired by an iOS Application (TDPT-GT).Sensors (Basel, Switzerland) · 2023Article
- Emerging biological insights enabled by high-resolution 3D motion data: promises, perspectives and pitfalls.The Journal of experimental biology · 2023Article
- Quantitative Gait Feature Assessment on Two-Dimensional Body Axis Projection Planes Converted from Three-Dimensional Coordinates Estimated with a Deep Learning Smartphone App.Sensors (Basel, Switzerland) · 2023Article
- Effective evaluation of HGcnMLP method for markerless 3D pose estimation of musculoskeletal diseases patients based on smartphone monocular video.Frontiers in bioengineering and biotechnology · 2023Article
Corrections and comments
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Authors and funding
11 authors.
Funding
Abstract
To quantitatively assess pathological gait, we developed a novel smartphone application for full-body human motion tracking in real time from markerless video-based images using a smartphone monocular camera and deep learning. As training data for deep learning, the original three-dimensional (3D) dataset comprising more than 1 million captured images from the 3D motion of 90 humanoid characters and the two-dimensional dataset of COCO 2017 were prepared. The 3D heatmap offset data consisting of 28 × 28 × 28 blocks with three red-green-blue colors at the 24 key points of the entire body motion were learned using the convolutional neural network, modified ResNet34. At each key point, the hottest spot deviating from the center of the cell was learned using the tanh function. Our new iOS application could detect the relative tri-axial coordinates of the 24 whole-body key points centered on the navel in real time without any markers for motion capture. By using the relative coordinates, the 3D angles of the neck, lumbar, bilateral hip, knee, and ankle joints were estimated. Any human motion could be quantitatively and easily assessed using a new smartphone application named Three-Dimensional Pose Tracker for Gait Test (TDPT-GT) without any body markers or multipoint cameras.
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Registered trials
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