Development of Fall Prediction Model for Older Adults Based on Multi-faceted Data

Development of Fall Prediction Model for Older Adults by Analysis of Walking Patterns Based on Multi-faceted Biosignal Data

This study aimed to develop a fall prediction model for older adults by measuring the multi-faceted biosignal data to classify the walking patterns and by identifying causal relationships with the variables that affect the fall.

This study aimed to develop a fall prediction model by measuring the multi-paceted biosignal data for the elderly's physical function and walking to classify the walking patterns of the elderly, and by identifying causal relationships and influences with the variables that affect the fall.

Inclusion Criteria:

• Community seniors aged 65 to 84 years
• A person who has no history of central nervous system disease

Exclusion Criteria:

• Older adults who are unable to walk independently due to vision loss, fractures, etc.
• An elderly person who had musculoskeletal history that could cause problems in the function of the lower extremities, such as fractures, within three months before recruitment;
• In case it is difficult to understand the task due to severe cognitive impairment (Korean simplified mental health examination, K-MMSE score 10 or less)
• In the event of a serious mental illness, such as schizophrenia or bipolar disorder;
• In case of severe dizziness and difficulty in pedestrian inspection
• Persons not eligible for examination