|Purpose||○ Develop IMU-sensor based wearable device capable of human activity recognition and exercise information generation
○ Develop deep learning based human activity recognition techniques
○ Develop exercise information generation techniques from recognized human activities
○ Develop a wearable device combined with IMU sensors, signal processing module, and wireless communication modules.
○ Develop a flexible and stretchable platform for a small patch-type wearable device.
○ Develop a wearable device capable of life logging.
|Contents||○ Develop deep leaning based human activity recognition technology: Convolutional Neural Network (CNN) and Recurrent Network (RNN) based human activity recognition techniques.
○ Develop human exercise information generation technology: energy expenditure, step counts, exercise distance etc.
○ Develop software libraries and SDK based on C/Python for human activity recognition
○ Develop IMU sensor based wearable devices capable of human activity recognition and exercise information generation
○ Develop flexible and stretchable platforms for IMU sensor based wearable devices.
|Expected Contribution||○ Smart sensor based human activity recognition technology can contribute automated life logging services.
○ Life logging allows personalized life care and health care services.
○ Patch-type flexible and stretchable wearable devices can open new opportunities of human activity recognition and life logging services.
○ Smart wearable devices and life logging technologies can create new business markets