We have developed an interactive video-based walking exercise system aimed at preventing frailty. In our system, users perform walking exercises using a stepper while watching video footage of walking a path. The feature of our system is that users can create interactive video content within the system themselves. The users who are physically capable of walking outdoors can not only use the system, but also easily create video content for the system by walking outdoors while pushing a dedicated cart system. Thus, since it is possible to perform walking exercises while updating the contents on their own, it is expected that motivation for exercises can be improved.
Although a lot of BMI research using CNN has been performed, CNN’s response to changes in the input EEG is too late to proceed in real-time. We propose a method to improve the real-time performance by blending multiple CNNs with different input signal length. The proposed method generates a classifier which has the advantage of a classifier with short input signal length, i.e., fast response to changes in the input signal, and also the advantage of a classifier with long input signal length, i.e., high classification performance.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.