Abstract: Closed loop control of a multimedia system may be achieved with a control space that includes at least two control axes. In each control axis, a coordinate on the axis may indicate a degree to which a multimedia scene facilitates or is perceived to facilitate a user state or a degree to which the user state is achieved. For example, the two control axes may be focus and restoration. A user may provide input that specifies a target state. The target state may be different than, or the same as, the user's current state. The system may select a scene that has coordinates, in the control space, that are closest, by at least a threshold, to the target state, and present the scene to the user. Sensors in the system may measure a user state that results from presenting the scene. The system may revise the scene's coordinates accordingly.

Abstract: A sensor system detects hand-to-mouth behavior. The system includes an electrical bio-impedance spectrometer and an inertial measurement unit. The sensor system may be worn on the forearm. The sensor system recognizes hand-to-mouth behavior in real-time, facilitating monitoring and immediate interventions An electrode positioning strategy optimizes the device's sensitivity and accuracy. Machine learning algorithms are leveraged to infer the hand-to-mouth detection. A prototype of the sensor system achieves 92% detection accuracy for recurrent usage by a single user and 90% accuracy for users that have not been previously encountered.

Abstract: Closed loop control of a multimedia system may be achieved with a control space that includes at least two control axes. In each control axis, a coordinate on the axis may indicate a degree to which a multimedia scene facilitates or is perceived to facilitate a user state or a degree to which the user state is achieved. For example, the two control axes may be focus and restoration. A user may provide input that specifies a target state. The target state may be different than, or the same as, the user's current state. The system may select a scene that has coordinates, in the control space, that are closest, by at least a threshold, to the target state, and present the scene to the user. Sensors in the system may measure a user state that results from presenting the scene. The system may revise the scene's coordinates accordingly.

Abstract: A sensor system detects hand-to-mouth behavior. The system includes an electrical bio-impedance spectrometer and an inertial measurement unit. The sensor system may be worn on the forearm. The sensor system recognizes hand-to-mouth behavior in real-time, facilitating monitoring and immediate interventions An electrode positioning strategy optimizes the device's sensitivity and accuracy. Machine learning algorithms are leveraged to infer the hand-to-mouth detection. A prototype of the sensor system achieves 92% detection accuracy for recurrent usage by a single user and 90% accuracy for users that have not been previously encountered.