University of Groningen
Machine learning for identifying patterns in human gait Zhou, Yuhan
DOI:
10.33612/diss.159240405
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Publication date: 2021
Link to publication in University of Groningen/UMCG research database
Citation for published version (APA):
Zhou, Y. (2021). Machine learning for identifying patterns in human gait: Classification of age and clinical groups. University of Groningen. https://doi.org/10.33612/diss.159240405
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PROPOSITIONS Belonging to the thesis MACHINE LEARNING FOR IDENTIFYING PATTERNS IN HUMAN GAIT:
CLASSIFICATION OF AGE AND CLINICAL GROUPS Yuhan Zhou Groningen, 17 March 2021 1. Gait characteristics differ
between age groups, between fallers and non-fallers; therefore, these groups were classified accurately based on gait variables as inputs to the machine learning classification models. (This thesis) 2.
Considering geriatric patients with or without cognitive impairment present with many comorbidities, the combination of gait and clinical characteristics can accurately classify these groups rather than inputting only gait or only clinical variables to the machine learning models. (This thesis) 3. Machine Learning algorithms are intelligent at handling gait characteristics calculated from 3D-accelerometers signals that are multidimensional and highly
correlated in a non-linear fashion. (This thesis) 4. The “black box” feature for machine learning limits a model's explanation capability to identify crucial variables related to specific groups. (This thesis) 5. Kernel Principal Component Analysis and Path Signature can pre-process gait characteristics, to reduce their dimensionality and
create independent and informative new features. (This thesis) 6. Artificial Neural Network was identified as an optimal classification method to accurately recognize specific gait changes from the
interrelated gait characteristics. (This thesis) 7. Machine learning can increase our understanding of how accelerated aging and pathology affect gait and help clinicians diagnose and eventually treat those with an increased risk for cognitive and motor
impairments. 8. “If we knew what it was we were doing, it would not be called research, would it?” (Albert Einstein) 9. “Study without thinking is labour lost; thinking without study is perilous.”
(Confucius)
