Our research in sensors and machine learning techniques seeks to improve HCI experience by developing objective and near real-time methods for recognizing end-users’ behavioral patterns and in-situ contexts. For this, we utilize a range of sensor devices (e.g., eye trackers, ECG-enabled armband, GSR fingertip sensors, wireless EEG headset and HR monitor) to identify patterns in perceptual and cognitive activities between individuals at different levels of task-complexity. This research includes the development of visual analytics and usable machine learning tools for time-series sensor data and the development of UI/UX technology to overcome the limitations of wearable device UIs.