Use this URL to cite or link to this record in EThOS:
Title: Joint estimation in optical marker-based motion capture
Author: Hang, Jianwei
ISNI:       0000 0004 7226 4275
Awarding Body: University of Cambridge
Current Institution: University of Cambridge
Date of Award: 2018
Availability of Full Text:
Access from EThOS:
Full text unavailable from EThOS. Please try the link below.
Access from Institution:
This thesis is concerned with the solutions to several issues, including the problems of joint localisation, motion de-noising/smoothing, and soft tissue artefacts correction, in skeletal motion reconstruction for motion analysis, using marker-based optical motion capture technologies. We propose a very efficient joint localisation method, which only needs to optimise over three parameters, regardless of the total numbers of markers and frames. A framework powered by this joint localisation solution is also developed, which can automatically find all the joints in an articulated body structure, and significantly reduce the total number of markers needed in a typical motion capture session, by implementing a solvability propagation process. This framework is also configured to operate in a hybrid scheme, which can automatically switch between the primary joint estimator and a slower solution having fewer conditions regarding the required number of markers on a given body segment. This makes the framework workable even for extreme scenarios in which there are fewer than three markers on any body segment. A non-linear optimisation method for 3D trajectory smoothing is also proposed for de-noising the estimated joint paths. By immobilising a series of characteristic points in the trajectory, this method is able to effectively preserve detailed information for vigorous motion sequences. Various other smoothing techniques in the literature are also discussed and compared, concluding that a size-3 weighted average filter implemented in an automatic manner is a good real-time solution for low intensity activities. The effects of skin deformation on marker position data, known as soft tissue artefacts, are learned via a behavioural study on the human upper-body, with specific emphasis on combined limb actions. Based on the experimental findings, mathematical models are proposed to characterise the development of different types of artefacts, including translational, rotational, and transverse. We also theoretically demonstrate the feasibility of using a Kalman filter to correct the soft tissue artefacts, using the mathematical models.
Supervisor: Lasenby, Joan Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
Keywords: Motion Capture ; Motion Denoising ; Joint Estimation