Biomarker detection and analysis for hand movement impairments

Movement disorders are one of the significant impairments of Huntington's disease (HD), as HD is clinically diagnosed when motor symptoms present. However, the Unified Huntington's Disease Rating Scale (UHDRS), one of the standard tests, has some limitations. It is a subjective assessment that depends on the examiner's experiences. So, an accurate clinical assessment along with the UHDRS is significantly required. In the absence of an objective assessment to detect movement impairments in HD patients, this thesis aimed to investigate the capability of the novel hand movement tasks as a motion biomarker to distinguish the movement impairments using frequency and time domain analysis. Consequently, three experiments were conducted in this thesis. In the first, the entropy measure was used as a method to analyse the degree of complexity of hand motion data. It should have its own input parameters for entropy calculations for the novel hand motion data. The current study revealed the optimal input parameters for all hand tasks. Experiment 2 examined the optimal input parameters for entropy calculations and the novel hand tasks. The results demonstrated the capability of the optimal input parameters and hand tasks to distinguish the movement differences between genders and age groups. The last experiment further investigated the capability of hand motion data of the novel hand movement tasks to distinguish the movement differences between healthy controls and HD patients. The results revealed that the novel hand movement tasks related to cognitive-motor function and linked to functional movements in daily living activities significantly distinguished the movement differences between healthy and people with HD. In conclusion, novel hand movement tasks, especially the Reaching with reverse sequences task, could be promising motion biomarkers and methods for detecting movement impairments in HD patients.