Chia-Lung Liu, Ph.D., Feng-Jie Tsai, MS, Mu-Liang Wang, BS, and Ting-Wu Ho, PhD | Industrial Technology Research Institute – Information and Communications Research Laboratories, Hsinchu, Taiwan
Competition Sponsor: Academia Sinica
Awardee Year: 2020
As the global population continues to age, the number of people suffering from Alzheimer’s Disease (AD) currently accounts for two-thirds of all demented people, and it has become one of the most serious illnesses in many developed countries. AD is a degenerative and irreversible dementia. However, if diagnosed early, it can be treated early and the symptoms mitigated, which could result in substantial savings from the direct costs of healthcare and from indirect costs of family members’ inability to work due to patient care. AD diagnosis is not difficult. The difficulty is to diagnose the Mild Cognitive Impairment (MCI) stage before AD. People with MCI are at high-risk to later develop into AD. The differences between MCI and Normal Cognition (NC) are very inconspicuous and difficult to be accurately distinguished by physicians. This proposal plans to diagnose AD early, and the goal is to identify the MCI stage before it evolves into AD. Early diagnosis and delaying the onset of symptoms will reduce social costs. This project innovatively applies a composite approach of using WiFi sensing based non-contact long-term tracking technology for Sleep Apnea and Artificial Intelligence (AI) and machine learning algorithms to detect variations in brainwaves to build a MCI stage detection model. This model can help doctors accurately determine the MCI stage to achieve early treatment and delayed onset of symptoms.
To learn more about this proposal, email healthylongevity@nas.edu.
View this project poster, first displayed at the 2021 Global Innovator Summit.