Example apparatus disclosed herein includes a grouper to generate a first presence indication based on a threshold number of segments of image data including patterns of light values corresponding to a first human pulse. Disclosed example apparatus also include a face identifier to identify a first location associated with a human face in an environment associated with the image data based on thermal imaging data from a thermal imaging device, and output the first location associated with the human face in the environment associated with the image data.

Example apparatus disclosed herein includes a grouper to generate a first presence indication based on a threshold number of segments of image data including patterns of light values corresponding to a first human pulse. Disclosed example apparatus also include a face identifier to identify a first location associated with a human face in an environment associated with the image data based on thermal imaging data from a thermal imaging device, and output the first location associated with the human face in the environment associated with the image data.

In accordance with some implementations, a method for measuring viewership of media content is disclosed. The method is performed on a client system. The client system obtains identification information for individuals who have agreed to participate in a media viewership totals measurement study, detects a first user of the client system, determines media content being presented in proximity to the first user, automatically determines if the first user is an individual who has agreed to participate in the media viewership totals measurement study; and generates viewership data according to the determination.

In accordance with some implementations, a method for measuring viewership of media content is disclosed. The method is performed on a client system. The client system obtains identification information for individuals who have agreed to participate in a media viewership totals measurement study, detects a first user of the client system, determines media content being presented in proximity to the first user, automatically determines if the first user is an individual who has agreed to participate in the media viewership totals measurement study; and generates viewership data according to the determination.

Methods, apparatus, and systems to collect audience measurement data are disclosed. Disclosed example systems include first means for processing to collect (i) first media identification data corresponding to a first time period and (ii) second media identification data corresponding to a second time period after the first time period, the first media identification data and the second media identification data associated with a monitored location and second means for processing to develop behavior data based on the first media identification data and people meter data collected during the first time period with a people meter associated with the monitored location, the people meter to be deactivated during the second time period and identify an audience member associated with the second media identification data based on the behavior data and without access to any people meter data corresponding to the second time period.

Methods, apparatus, and systems to collect audience measurement data are disclosed. Disclosed example systems include first means for processing to collect (i) first media identification data corresponding to a first time period and (ii) second media identification data corresponding to a second time period after the first time period, the first media identification data and the second media identification data associated with a monitored location and second means for processing to develop behavior data based on the first media identification data and people meter data collected during the first time period with a people meter associated with the monitored location, the people meter to be deactivated during the second time period and identify an audience member associated with the second media identification data based on the behavior data and without access to any people meter data corresponding to the second time period.

Example meters disclosed herein include a user interface to accept user input in response to activation of a prompting indicator. Disclosed example meters also include a controller to activate a light projector to project light onto a first projection area of a surface, the light projector activated in association with the activation of the prompting indicator. In some examples, the controller is also to control the light projector to project light onto a second projection area of the surface in response to expiration of a time period without receipt of any user input responsive the activation of the prompting indicator.

Example meters disclosed herein include a user interface to accept user input in response to activation of a prompting indicator. Disclosed example meters also include a controller to activate a light projector to project light onto a first projection area of a surface, the light projector activated in association with the activation of the prompting indicator. In some examples, the controller is also to control the light projector to project light onto a second projection area of the surface in response to expiration of a time period without receipt of any user input responsive the activation of the prompting indicator.

Techniques for regional viewership predictions of broadcast events such as live broadcast professional sporting events. The techniques can make the predictions without a direct response variable such as regional viewership data for training a prediction model. Instead, in one technique, demand information for a good or service is used. From the demand information, a derivative demand for the good or service relative to a normal demand is determined. A regression framework is used to learn relationships between the derivative demand for the good or service and features of past live broadcast sporting events. This results in a matrix of feature weights. A non-parametric mixture framework is then used to find a set of feature weights that can be applied to features of future broadcast events to generate regional viewership predictions for the events.

Techniques for regional viewership predictions of broadcast events such as live broadcast professional sporting events. The techniques can make the predictions without a direct response variable such as regional viewership data for training a prediction model. Instead, in one technique, demand information for a good or service is used. From the demand information, a derivative demand for the good or service relative to a normal demand is determined. A regression framework is used to learn relationships between the derivative demand for the good or service and features of past live broadcast sporting events. This results in a matrix of feature weights. A non-parametric mixture framework is then used to find a set of feature weights that can be applied to features of future broadcast events to generate regional viewership predictions for the events.