Ted Yeung
author of 2016 doctoral thesis at University of Auckland titled Kinematics of The Human Shoulder Complex: Model Registration, X Ray Videoradiography and Soft Tissue Artefacts
Facts
8 properties- Instance of
- human
- Occupation
- researcher
- Educated at
- University of Auckland[2]
- Doctoral advisor
- Kumar Mithraratne[3]
- Academic degree
- Doctor of Philosophy[4]
- Academic thesis
- Kinematics of The Human Shoulder Complex: Model Registration, X Ray Videoradiography and Soft Tissue Artefacts[6]
- On focus list of Wikimedia project
- NZThesisProject[7]
🏷️ Also known as
en Ted Yeung
🔖 Identifiers · 2
- ORCID iD
- 0000-0002-2932-1516
- OpenAlex ID
- A5062292589
🔗 Connections
Author of 6
3D gait analysis in children using wearable sensors: feasibility of predicting joint kinematics and kinetics with personalized machine learning models and inertial measurement units,
3D trunk orientation measured using inertial measurement units during anatomical and dynamic sports motions,
A comparison of machine learning models’ accuracy in predicting lower-limb joints’ kinematics, kinetics, and muscle forces from wearable sensors,
Personalized Machine Learning Approach to Estimating Knee Kinematics Using Only Shank-Mounted IMU,
Predicting ankle and knee sagittal kinematics and kinetics using an ankle-mounted inertial sensor,
The Effect of IMU Sensor Location, Number of Features, and Window Size on a Random Forest Model’s Accuracy in Predicting Joint Kinematics and Kinetics During Gait
← Authored by 6
3D gait analysis in children using wearable sensors: feasibility of predicting joint kinematics and kinetics with personalized machine learning models and inertial measurement units,
3D trunk orientation measured using inertial measurement units during anatomical and dynamic sports motions,
A comparison of machine learning models’ accuracy in predicting lower-limb joints’ kinematics, kinetics, and muscle forces from wearable sensors,
Personalized Machine Learning Approach to Estimating Knee Kinematics Using Only Shank-Mounted IMU,
Predicting ankle and knee sagittal kinematics and kinetics using an ankle-mounted inertial sensor,
The Effect of IMU Sensor Location, Number of Features, and Window Size on a Random Forest Model’s Accuracy in Predicting Joint Kinematics and Kinetics During Gait
Affiliated with 2
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