Skip to main navigation
Skip to search
Skip to main content
Hong Kong Baptist University Home
Help & FAQ
Link opens in a new tab
Search content at Hong Kong Baptist University
Home
Scholars
Departments / Units
Research Output
Projects / Grants
Prizes / Awards
Activities
Press/Media
Student theses
Datasets
Classification of Pilates Using MediaPipe and Machine Learning
Mengjiao Zhao
*
, Nike Lu
, Yifeng Guan
*
Corresponding author for this work
Academy of Wellness and Human Development
Research output
:
Contribution to journal
›
Journal article
›
peer-review
4
Citations (Scopus)
Overview
Fingerprint
Fingerprint
Dive into the research topics of 'Classification of Pilates Using MediaPipe and Machine Learning'. Together they form a unique fingerprint.
Sort by:
Weight
Alphabetically
Keyphrases
Machine Learning
100%
Pilates
100%
MediaPipe
100%
Climber
11%
Convolutional Neural Network
11%
YouTube
11%
Deep Learning Methods
11%
Secondary Injury
11%
Video Frames
11%
Real-time Video
11%
Extracting Features
11%
Disability
11%
Spine
11%
Physical Performance
11%
Physical Condition
11%
Life Stages
11%
Professional Supervision
11%
3D Humans
11%
Squat
11%
Rehabilitation Therapy
11%
Kneel
11%
Crisscross
11%
Plank
11%
Pike
11%
Long Short-term Memory Algorithm
11%
Sidekick
11%
Exercise Posture
11%
Professional Guidance
11%
Rolling Ball
11%
Cross Rolling
11%
Chemical Engineering
Deep Learning Method
100%
Neural Network
100%
Learning System
100%
Long Short-Term Memory
100%
Long Short-Term Memory Network
100%
Engineering
Learning System
100%
Keypoints
100%
Deep Learning Method
50%
Convolutional Neural Network
50%
Long Short-Term Memory
50%
Learning Technique
50%
Lstm
50%
Psychology
Short-Term Memory
100%
YouTube
100%
Neural Network
100%
Medicine and Dentistry
Pilates
100%
Body Position
11%
Rehabilitation (Therapeutic Procedure)
11%
Physical Performance
11%
Short Term Memory
11%
Secondary Injury
11%