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Current1assistive technologies need complicated, cumbersome, and expensive equipment, which are not user-friendly, not portable, and often require extensive fine motor control. Our approach aims at solving these problems by developing EXGbuds, a compact, non-obtrusive and ergonomic wearable device, to measure signals associated with human physiological gestures, and therefore generate useful commands to interact with the environment. Our innovation uses machine learning and non-invasive biosensors on top of the ears to identify eye movements and facial expressions with over 95% accuracy. Users can control different applications, such as a cell phone, powered wheelchair, smart home, or other IoT (Internet of Things) devices. Combined with VR headset, the user can use our technology to control a camera-mounted robot (e.g., telepresence robot, drone, or any robotic manipulator) to navigate around the environment in the first-person's view (FPV) simply by eye movements and facial expressions. It enables a intuitive way of interaction totally hands-free and touch-free. The experiments showed satisfactory results in different applications. Such method can be further applied on surgical robots to help surgeons change endoscopic camera's viewpoints by eye and facial expressions in the future. [ABSTRACT FROM AUTHOR]