Abstract: A brain control interface system for determining a baseline for detecting brain activity of a user is disclosed. The brain control interface system comprising: a brain control interface configured to detect brain signals indicative of brain activity of a user in an environment, a memory configured to store activities of the user associated with different light scenes, a processor configured to: select, from the activities stored in the memory, a first activity of the user, control one or more lighting devices according to a first light scene associated with the first activity, detect brain signals of the user while the first light scene is active, determine, based on the detected brain signals, a first baseline for the brain signals, and store an association between the first baseline and the first light scene and/or the first activity.

Abstract: The brain control interface system comprises: a brain control interface configured to detect brain signals indicative of brain activity of a user in an environment, an input configured to obtain data indicative of a current light scene of one or more lighting devices in the environment, a memory configured to store processing methods associated with different light scenes, one or more processor configured to: select, from the processing methods stored in the memory, a processing method in accordance with the current light scene, apply the selected processing method to obtain and/or process the brain signals, derive a control command and/or a mental state of the user from the brain signals, and control the controllable device based on the derived control command and/or the derived mental state.

Abstract: The present disclosure is generally directed to systems and methods for controlling lighting based on the type of written content on smart coated surfaces. A smart coated surface generates pressure data based on the written content. A feature extractor then extracts features from the pressure data describing the written content. A content classifier then feeds the features into a recurrent neural network, such as a Long Short-Term Memory network to select a content label. A lighting controller then controls luminaires based on the content label and data from additional sensors. In this way, the system controls the luminaires based on the type of the written content without evaluating or decoding the content itself. For example, the system is able to label the content as text based on written patterns without interpreting what the text actually says. Accordingly, the content written on the smart coated surface remains private and secure.

Abstract: A system for monitoring face mask wearing of a monitored person is provided. The system includes a controller communicatively coupled to one or more multipixel thermopile sensors (“MPTs”). The controller is configured to (1) detect a monitored person region within a heat map based on one or more data sets captured by the MPTs; (2) locate a head region within the monitored person region by identifying a high intensity pixel cluster within the monitored person region; (3) determine a facing-direction of the head region based on a major axis of the monitored person region or a minor axis of the monitored person region; (4) locate a mouth region within the head region based on the facing-direction; (5) determine an exhalation region of the heat map based on the mouth region; (6) determine a mask state of the monitored person based on the exhalation region and a temperature gradient classification model.

Abstract: A system for monitoring social distancing is provided. The system includes a controller communicatively coupled to sensors within an environment. The controller may be configured to (1) receive an expected sensor behavior model for each of the sensors; (2) generate an adjusted sensor behavior model for each of the sensors based on the expected sensor behavior model for each of the sensors and an environment adjustment model for each of the sensors; (3) receive a layout of the environment; (4) generate an expected layout behavior model based on the layout and the adjusted sensor behavior model; (5) capture, via the sensors, an observed behavior data set during a measurement period; and (6) determine a social distancing state of individuals in the environment as distanced or not-distanced based on the observed behavior data set and the expected layout behavior model.

Abstract: Systems for detecting and localizing a person exhibiting a symptom of infection in a space are provided. The systems include a user interface configured to receive position information of the space and a plurality of connected sensors in the space, wherein the plurality of connected sensors are configured to capture sensor signals from the person exhibiting the symptom of infection. The systems further include a processor configured to input captured sensor signals from the plurality of connected sensors to at least one convolutional neural network (CNN) model selected based on a confidence value, wherein the processor is further configured to locate the symptomatic person. The systems further include a graphical user interface connected to the processor and configured to display the location of the person exhibiting the symptom of infection within the space.

Abstract: A method for enforcing contact tracing is provided including: associating, via a processor associated with a plurality of connected sensors, a first person with a first mobile device based in part on a first distance between the first person and the first mobile device; determining, via the processor, that the first person and the first associated mobile device are separated from each other by a second distance; notifying, via at least one light effect provided by an illumination device in communication with the processor, the first person that the first person and the first associated mobile device are separated from each other by the second distance. The method can further include: determining, via the processor, that a second person is positioned within predetermined proximity to the first person, and receiving, via the illumination device, contact tracing data on behalf of the first mobile device from the second mobile device.

Abstract: A system for evaluating a workspace surface having one or more objects placed thereon is disclosed. The system may include sensors configured to capture data corresponding to the workspace surface and a controller communicatively coupled to the sensors. The controller may generate an illuminance map of the workspace surface based on the captured data. The controller may generate a clutter entropy level based on the illuminance map and an entropy assessor. The controller may determine whether the clutter entropy level exceeds a clutter threshold. The controller may provide a clutter indication to a user device if the clutter entropy level exceeds the clutter threshold. The controller may identify the objects placed on the workspace surface. The controller may generate a decluttered layout based on the one or more objects and a layout generator.

Abstract: The present disclosure is directed to systems and method for adaptive fusion of sensor data from multiple sensors. The method comprises: collecting sensor data generated by a plurality of sensors in an environment; determining the quality of the sensor data generated by each sensor of the plurality of sensors, the quality metric corresponding to a suitability of the sensor data for performing a given task; selecting, via an assessment artificial intelligence program, one or more sensors from the plurality of sensors based on the determined quality metric of the sensor data that yields a desired accuracy for performing the given task; and selecting for each sensor of the plurality of sensors selected, via the assessment artificial intelligence program, a machine learning algorithm from a predetermined set of machine learning algorithms based on the determined quality metric of the sensor data that yields the desired accuracy for performing the given task.

Abstract: A method of predicting a data point from a predictor, wherein the predictor comprises a trained machine which has been trained based on a training dataset comprising at least one labelled data point; wherein the method comprises: assigning a first function to the at least one labelled data point and a second function to the data point; determining a level of similarity based on a comparison of the first function and the second function; determining a similarity information between the at least one labelled data point and the data point; assigning a first weight to a prediction from the trained machine for the data point, and a second weight to the similarity information; determining an adjustment to the first and/or the second weight as a function of the level of similarity; and determining a prediction for the data point, wherein the prediction is based on combining the prediction from the trained machine with the adjusted first weight and the similarity information with the adjusted second weight.

Abstract: A system for generating two-dimensional images is provided. The system may include a controller configured to receive a three-dimensional design image. The controller may be further configured to generate a fake 2D image based on the 3D design image and one or more 3D image parameters, such as object orientation. The controller may be further configured to generate a realistic 2D image based on the fake 2D image and one or more image features. The controller may be further configured to evaluate a realism value of the realistic 2D image based on one or more real 2D images. The controller may be further configured to evaluate a detected orientation of the realistic 2D image. The controller may be further configured to update the realistic image generator based on the realism value or the detected orientation.

Abstract: The present disclosure is directed to systems and method for adaptive fusion of sensor data from multiple sensors. The method comprises: collecting sensor data generated by a plurality of sensors in an environment; determining the quality of the sensor data generated by each sensor of the plurality of sensors, the quality metric corresponding to a suitability of the sensor data for performing a given task; selecting, via an assessment artificial intelligence program, one or more sensors from the plurality of sensors based on the determined quality metric of the sensor data that yields a desired accuracy for performing the given task; and selecting for each sensor of the plurality of sensors selected, via the assessment artificial intelligence program, a machine learning algorithm from a predetermined set of machine learning algorithms based on the determined quality metric of the sensor data that yields the desired accuracy for performing the given task.