Prescreening MCI and Dementia Using Shank-Mounted IMU during TUG Task
Issued Date
2022-12-15
Resource Type
ISSN
1530437X
eISSN
15581748
Scopus ID
2-s2.0-85141579442
Journal Title
IEEE Sensors Journal
Volume
22
Issue
24
Start Page
24550
End Page
24558
Rights Holder(s)
SCOPUS
Bibliographic Citation
IEEE Sensors Journal Vol.22 No.24 (2022) , 24550-24558
Suggested Citation
Cherachapridi P., Wachiraphan P., Rangpong P., Kiatthaveephong S., Kongwudhikunakorn S., Thanontip K., Piriyajitakonkij M., Chinkamol A., Likitvanichkul C., Dujada P., Senanarong V., Wilaiprasitporn T., Sudhawiyangkul T., Senanarong V., Wilaiprasitporn T., Sudhawiyangkul T. Prescreening MCI and Dementia Using Shank-Mounted IMU during TUG Task. IEEE Sensors Journal Vol.22 No.24 (2022) , 24550-24558. 24558. doi:10.1109/JSEN.2022.3220238 Retrieved from: https://repository.li.mahidol.ac.th/handle/20.500.14594/84575
Title
Prescreening MCI and Dementia Using Shank-Mounted IMU during TUG Task
Other Contributor(s)
Abstract
Detection of mild cognitive impairment (MCI) and dementia (DEM) is an important topic because, unless it is treated early, MCI can progress to DEM, which is an untreatable disease. This article proposes a timed-up-and-go (TUG) task features analysis and classification of MCI and DEM using inertial measurement units (IMUs) in wearable devices. Our goal is to create a generalized model that can be used for preclinical screening. As a result, rather than classifying only one subtype of DEM, such as Alzheimer's disease (AD) or Parkinson's disease (PD), we classify all subtypes as DEM. We also utilize feature selection methods on features from TUG tasks to optimize the MCI and DEM classification performance. From the results, our generalized model can outperform other works in normal control (NC)-MCI&DEM classification with an accuracy of 86.94% and sensitivity of 97.40%. For NC-DEM classification, the performance of our generalized model is slightly lower than that of specific-subtype models (e.g., NC versus AD). However, our generalized model can outperform the specific-subtype models when using a diverse variety of subtypes. It is a reasonable tradeoff, and it can be a good first step toward a future where the patient can preclinically self-screening the cognitive impairments using wearable devices in free-living environments. This could allow patients to notice the cognitive impairment early on and seek a comprehensive diagnosis from a doctor.