Abstract: Apparatus and associated methods relate to predicting a health outcome of a patient by a machine learning model operating on a video stream of the patient. Video data, audio data, and semantic text data are extracted from a video stream of the patient. The video data are analyzed to identify a first feature set. The audio data are analyzed to identify a second feature set. The semantic text data are analyzed to identify a third feature set. Using a computer-implemented machine-learning model, a health outcome of the patient is predicted based on the first, second, and/or third features sets. The health outcome that is predicted is then reported.

Abstract: Apparatus and associated methods relate to invoking an alert based upon a behavior of a patient as determined by a machine-learning model operating on a video stream of the patient. Video data, audio data, and semantic text data are extracted from a video stream of the patient. The video data are analyzed to identify first, second, and third features sets of video, audio, and semantic text features, respectively, which have been identified by a computer-implemented machine-learning engine as being indicative of at least one of a set of alerting behaviors corresponding to a patient classification of the patient. Using a computer-implemented machine-learning model, a patient behavior of the patient is determined based on the first, second, and/or third features sets. The patient's behavior is compared with the set of alerting behaviors, and, when the patient's behavior is determined to be included therein, the alert is automatically invoked.

Abstract: Apparatus and associated methods relate to assessing a confidence level of a verbal communication of a person as determined by a machine learning model operating on a video stream of the person. Video data, audio data, and semantic text data are extracted from a video stream of the person. The video data are analyzed to identify a first feature set. The audio data are analyzed to identify a second feature set. The semantic text data are analyzed to identify a third feature set. Using a computer-implemented machine-learning model, a confidence level of the verbal communication of the person is assessed. The confidence level is then associated with a time of the video stream to which the confidence level pertains. The confidence level and the associated time of the video stream are then reported.

Abstract: Apparatus and associated methods relate to enhancing care of a patient using video and audio analytics. Video data, audio data, and semantic text data are extracted from a video stream of the patient. The video data are analyzed to identify a first feature set. The audio data are analyzed to identify a second feature set. The semantic text data are analyzed to identify a third feature set. Using a computer-implemented machine-learning model, a health outcome of the patient is predicted based on the first, second, and/or third features sets. The health outcome that is predicted is compared with the set of health outcomes of the training patients classified with the patient classification of the patient. Differences are identified between the feature sets corresponding to the patient and feature sets of the training patients who have better health outcomes the patient's predicted health outcome. The differences identified are then reported.

Abstract: Apparatus and associated methods relate to invoking an alert based upon a behavior of a patient as determined by a machine learning model operating on an audio stream of the patient. Audio data, and semantic text data are extracted from an audio stream of the patient. The audio data are analyzed to identify a first feature set. The semantic text data are analyzed to identify a second feature set. Using a computer-implemented machine-learning model, a patient behavior of the patient is determined based on the first and/or second features sets. The patient behavior is compared with a set of alerting behaviors corresponding to a patient classification of the patient. The alert is automatically invoked when the patient behavior is determined to be included in the set of alerting behaviors corresponding to the patient classification of the patient.

Abstract: An imaging system includes a camera and a controller. The camera is configured to be disposed on a first vehicle system or at a wayside location along a route to generate image data within a field of view of the camera. The controller is configured to monitor a data rate at which the image data is provided from the camera. The controller also is configured to identify a stimulus event within the field of view of the camera based on a change in the data rate at which the image data is generated by the camera.

Abstract: A magnetic harness for holding magnetically attractable metallic work items there against for convenient access is described. The harness includes an elongated pocket that holds a plurality of magnets. The plurality of magnets are attached side by side and one atop the other to increase the strength of the magnetic field or attractability of the magnets. The pocket is easily accessible to the user to allow the user to manually manipulate the strength of the magnetic field by increasing or decreasing the amount of magnets in the pocket. The harness includes a series of elongated pockets across the front of the harness.

Abstract: A magnetic harness for holding magnetically attractable metallic work items there against for convenient access is described. The harness includes an elongated pocket that holds a plurality of magnets. The plurality of magnets are attached side by side and one atop the other to increase the strength of the magnetic field or attractability of the magnets. The pocket is easily accessible to the user to allow the user to manually manipulate the strength of the magnetic field by increasing or decreasing the amount of magnets in the pocket. The harness includes a series of elongated pockets across the front of the harness.

Abstract: An imaging system includes a camera and a controller. The camera is configured to be disposed on a first vehicle system or at a wayside location along a route to generate image data within a field of view of the camera. The controller is configured to monitor a data rate at which the image data is provided from the camera. The controller also is configured to identify a stimulus event within the field of view of the camera based on a change in the data rate at which the image data is generated by the camera.

Abstract: An improved vehicle jack and jack stand is provided, used separately or in combination, where the both the vehicle jack and jack stand are adapted to receive and support a cup means that also supports a block. The block is able to be interchanged with other blocks to accommodate virtually any type of vehicle. A single jack is able to be used with multiple jack stands, since each jack stand is able to provide support at the identical location as the jack does. This is extremely useful on vehicles with uni-body construction. The jack stand defines an adjustable head, where the head is able to fit between side members of the jack stand, so that both the jack and jack stand are able to be concurrently positioned beneath the cup and block means. The jack stand also provides a removable cross support, which allows the jack stand to be moved in or out of position while the jack is being used to elevate an automobile. The support is replaced prior to the time that the vehicle is lowered onto the jack stand.