rdf-syntax-grammar
mods:
  schema location: http://www.loc.gov/mods/v3 http://www.loc.gov/standards/mods/v3/mods-3-2.xsd
  title info:
    title: Human hand motion analysis and modelling for eating activity in rehabilitation exercise
  name:
    name part: Zakia Hussain
    role:
      role term: creator
    role:
      role term: author
  name:
    name part: International Islamic University Malaysia
    name part: Department of Mechatronics Engineering
  genre: Theses, IIUM local
  origin info:
    place:
      place term: my
    date issued: 2019
  language:
    language term: eng
  physical description:
    extent: xix, 155 leaves : colour illustrations ; 30cm.
  abstract: Patients with upper limb impairments have a reduced ability to perform their normal activities of daily living (ADL), and hence, deteriorating their quality of life. Eating is one of the fundamental activities of survival for all living beings. The robotic rehabilitation systems for people with upper limb disabilities, must have the capability of assisting the patients, providing appropriate forces/torques, during various eating activities. Since, Human Upper Limb (HUL) motion is highly dexterous, a comprehensive hand motion analysis can be helpful in developing a dynamic model of the HUL, for predicting the torques during different eating activities. This research focuses on the analysis of HUL motion and the development of three-dimensional, 4 DOF mathematical model of the upper limb coupled with a robotic system, concentrating on the elbow flexion/extension, forearm pronation/supination, wrist adduction/abduction and wrist flexion/extension motions, using Kane's method and Artificial Neural Networks (ANNs), during different eating activities and using different cutlery. A motion analysis, simulation study, and experimental validation has been conducted involving five different food types and using two different types of cutlery, which are, a fork and a spoon, to study their effect on hand motion and the corresponding torques produced. Kinematic data from motion tracker sensors were utilized as inputs to the proposed models (i.e. 3D Kane's model and Nonlinear AutoRegressive eXogenous-Neural Network (NARX-NN) model) to determine the torques, while performing the different eating activities. Statistical tests like Analysis of Variance (ANOVA) and independent samples t-tests were employed to analyze data from the motion study. The results revealed that the bending motion of the index finger and the thumb varies with respect to different food characteristics and the type of cutlery used, whereas the bending motion of the middle finger remains unaffected. In addition, …
  note: by Zakia Hussain
  note: Thesis (MSMCT)--International Islamic University Malaysia, 2019.
  note: Includes bibliographical references (leaves 141-146).
  subject:
    geographic code: a-my---
  identifier: http://studentrepo.iium.edu.my/handle/123456789/4860
  record info:
    record content source: UIAM
    record creation date: 190306
    language of cataloging:
      language term: eng