期刊
IEEE ACCESS
卷 10, 期 -, 页码 15835-15844出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/ACCESS.2022.3149100
关键词
Legged locomotion; Dementia; Germanium; Inertial sensors; Biomarkers; Task analysis; Older adults; Mild cognitive impairment; dementia; gait analysis; inertial sensors; gait biomarkers; early detection
资金
- GIST Research Institute (GRI)
- Gwangju Institute of Science and Technology (GIST), South Korea
This paper investigates gait biomarkers for Mild Cognitive Impairment (MCI) and shows that dual-task walking provides better distinction between MCI and cognitively normal (CN) subjects. The machine learning model achieves high accuracy and sensitivity for MCI pre-screening.
The mild symptoms in Mild Cognitive Impairment (MCI), a precursor of dementia, often go unnoticed and are assumed as normal aging signs. Such negligence result in late visits which consequently, lead to the diagnosis and progression of dementia. An instrumented gait assessment in home settings may facilitate the detection of subtle MCI-related motor deficits thus, allowing early diagnosis and intervention. This paper investigates potential gait biomarkers derived from shank mounted inertial sensors signals under normal and dual-task walking conditions using data collected from thirty MCI and thirty cognitively normal (CN) subjects. To identify potential gait biomarkers for MCI screening, we assess the variance and predictive power of each feature. Moreover, multiple classification models using different machine learning and feature selection techniques are built to automate MCI detection by leveraging the gait biomarkers. Statistical analysis reveal multiple gait parameters that are significantly different under both single and dual-task settings. However, we show that dual-task walking provides better distinction between MCI and CN subjects. The machine learning model employed for MCI pre-screening based on the inertial sensor-derived gait biomarkers achieves accuracy and sensitivity of 71.67% and 83.33%, respectively.
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