TY - BOOK

T1 - Human hand neuromechanics for the design of robotic intelligent upper limb prostheses

AU - Ma'touq, Jumana

N1 - Funding Information: I would like to thank the German Academic Exchange Service (Deutscher Akademischer Austauschdienst DAAD) for the financial support and giving me the rare opportunity to pursue my Ph.D. in Germany.

PY - 2019

Y1 - 2019

N2 - Understanding human neuromechanics is the first step in transforming human capabilities and behaviour into smart human-like robotic systems. The aim of this thesis is to develop a human hand neuromusculoskeletal model that serves as a tool in understanding and replicating human behaviour. In this thesis, five models of the human hand are proposed, i.e. skeletal kinematics, skeletal dynamics, musculotendon kinematics, musculotendon dynamics, and muscle activation estimation. The skeletal kinematic model is a 26 degree of freedom model that includes the five digits and the palm arc. It estimates skeletal joint rotational angles from motion tracking data based on mapping functions between surface landmarks and the estimated joint centres of rotation. In the skeletal dynamic model, both the link torque due to gravitational and inertial forces and the passive torque due to the passive joint properties are estimated. The musculotendon kinematic model calculates musculotendon lengths, length change rates, and musculotendon excursion moment arms as a function of joint configuration. The musculotendon dynamic model used is a Hill-type muscle model that predicts the musculotendon forces for given musculotendon lengths, length change rates, and muscle activations. The musculotendon length and its rate of change are obtained from the proposed musculotendon kinematic model while muscle activations are obtained from the proposed muscle activation estimation model. Using this model, muscle activations are optimised by minimising the difference between the torque resulting from the musculotendon dynamic model and skeletal dynamic model. The proposed models were validated either experimentally using a motion tracking system or by comparing model results with available cadaver/experimental measurements taken from the literature. The sub-millimetre difference between measured and estimated surface markers indicates that the proposed skeletal kinematic model and associated identification procedure are consistent and highly accurate. The high similarity (similarity coefficient s ≥ 0.70 for 92% of cases) shown between the modelled moment arms and available cadaver measurements from the literature suggests the correctness of the modelled moment arms, and implies the feasibility of the modelled musculotendon paths, lengths, and length change rates. Finally, the overall consistency between the five models proposed will be demonstrated and highlights the quality of the developed models.

AB - Understanding human neuromechanics is the first step in transforming human capabilities and behaviour into smart human-like robotic systems. The aim of this thesis is to develop a human hand neuromusculoskeletal model that serves as a tool in understanding and replicating human behaviour. In this thesis, five models of the human hand are proposed, i.e. skeletal kinematics, skeletal dynamics, musculotendon kinematics, musculotendon dynamics, and muscle activation estimation. The skeletal kinematic model is a 26 degree of freedom model that includes the five digits and the palm arc. It estimates skeletal joint rotational angles from motion tracking data based on mapping functions between surface landmarks and the estimated joint centres of rotation. In the skeletal dynamic model, both the link torque due to gravitational and inertial forces and the passive torque due to the passive joint properties are estimated. The musculotendon kinematic model calculates musculotendon lengths, length change rates, and musculotendon excursion moment arms as a function of joint configuration. The musculotendon dynamic model used is a Hill-type muscle model that predicts the musculotendon forces for given musculotendon lengths, length change rates, and muscle activations. The musculotendon length and its rate of change are obtained from the proposed musculotendon kinematic model while muscle activations are obtained from the proposed muscle activation estimation model. Using this model, muscle activations are optimised by minimising the difference between the torque resulting from the musculotendon dynamic model and skeletal dynamic model. The proposed models were validated either experimentally using a motion tracking system or by comparing model results with available cadaver/experimental measurements taken from the literature. The sub-millimetre difference between measured and estimated surface markers indicates that the proposed skeletal kinematic model and associated identification procedure are consistent and highly accurate. The high similarity (similarity coefficient s ≥ 0.70 for 92% of cases) shown between the modelled moment arms and available cadaver measurements from the literature suggests the correctness of the modelled moment arms, and implies the feasibility of the modelled musculotendon paths, lengths, and length change rates. Finally, the overall consistency between the five models proposed will be demonstrated and highlights the quality of the developed models.

U2 - 10.15488/5588

DO - 10.15488/5588

M3 - Doctoral thesis

CY - Hannover

ER -