Abstract: The present invention relates to a method and apparatus of electronic surveillance, which employs data gathering devices that continuously collect and relay data (such as images and sounds) to a processor, which analyzes and compares the analyzed data to data samples stored in a catalog, interpreting the data in real time, and if a closely correlated data sample is identified by the program, and if that correlation has a feedback command associated with it in the feedback command catalog, then the program will issue a feedback command to at least one of a specific combination of data feedback devices, and/or may inform users using electronic methods. The present invention is used to track and provide feedback with various quality and safety measures for various activities in diverse environments, such as prisoner management, elder or children's care, or compliance in health care or in the food industry including hand washing behavior.

Abstract: Techniques for automatically detecting meniscus include receiving pixels from a scanning device directed to a knee of a subject. Also received is a region of interest that includes pixels that correspond to at least a portion of a meniscus of the knee. Without human intervention, a meniscus extraction threshold is determined based on pixel intensities in the region of interest. A meniscus object portion of the scan data is further determined without human intervention based on the meniscus extraction threshold and a geometrical constraint. Other techniques for automatically detecting meniscal tears includes receiving a meniscus object portion of scan data from a scanning device and receiving threshold data that indicates a meniscus extraction threshold. Without human intervention, a propensity for meniscal tears is determined based on the threshold data and the meniscus object portion of the scan data.

Abstract: Techniques for automatically detecting meniscus include receiving pixels from a scanning device directed to a knee of a subject. Also received is a region of interest that includes pixels that correspond to at least a portion of a meniscus of the knee. Without human intervention, a meniscus extraction threshold is determined based on pixel intensities in the region of interest. A meniscus object portion of the scan data is further determined without human intervention based on the meniscus extraction threshold and a geometrical constraint. Other techniques for automatically detecting meniscal tears includes receiving a meniscus object portion of scan data from a scanning device and receiving threshold data that indicates a meniscus extraction threshold. Without human intervention, a propensity for meniscal tears is determined based on the threshold data and the meniscus object portion of the scan data.