Design a BCI model using a MI task to manipulate a small humanoid, which gives visual representation of MI for NFT, and 2) compare brain activations with and without NFT to investigate the performance to discriminate brain maps to send commands to the output device.
A personal sensing and logging system will have interactive procedures with a cloud system to organize a large cohort database. It will continuously collect and update the cohort database, and support daily life of older adults by reporting their physical activities and physiological status to give possible suggestions. In order to more precisely estimate physical activities and characteristics of movements, the handy monitoring systems to be developed will include a set of sensors such as camera-less motion-capture beside conventional life log sensors. A camera-less motion capture will be useful to identify the type of movements and more precisely estimate their amount. Sensing by an electromyogram (EMG) and an electro-encephalography (EEG) will be further implemented to investigate the potential of physiological logging to detect clinical signs by correlating with behavioral episodes.
Application of intelligent system will be quite useful to assist them to pace and maintain their activities for health promotions, since not a few of them live by themselves making them easily isolated in their community and cognitive decline due to aging. It will also inform older adults of instructions and suggestions to optimize physical / cognitive interventions depending on analysis of their various physical and psychological statuses. The medium to present such information may be a wearable device enough small and light for older adults or an agent like small humanoid.
Abstract: