Abstract: An input device for controlling a computing system includes one or more sensors configured to sense a change in weight distribution of a user positioned on the input device in use, and a transmitter configured to transmit a signal based on the sensed change in weight distribution, for use in a virtual joystick input to a computing system.

Abstract: Methods, systems, and devices are provided for identifying hazards. According to one aspect, a computer-implemented method can include receiving a plurality of sensor data including one or more image files from a mobile device. The method can include generating one or more position and label pairs based on the plurality of sensor data. The method can include assigning a hazard recognition to each of the position and label pairs. The method can include assigning a score associated to each of the hazard recognitions. The method can include displaying a result including one or more image results based on the one or more image files, one or more hazard recognitions, the one or more hazard recognitions associated with at least one of the one or more image results, and one or more scores associated to each of the hazard recognitions.

Abstract: A system and method to determine a retention prediction for a caregiver is disclosed. The system includes a database of caregiver data and patient data. The set of caregiver data and patient data are normalized to create a modified set of caregiver and patient data. The modified set of caregiver and patient data defines a set of parameters or inputs from the set of caregiver and patient data and a corresponding employment status. An analysis is performed of parameters correlated with an employment status for each of the caregivers. Based on the correlation and the modified set of caregiver and patient data, a training set of caregiver data is generated that includes at least one parameter that correlates with employment status. The machine learning model is trained using the training set. The training allows a prediction of an employment status associated with the parameter. The accuracy of the trained machine learning model is evaluated.

Abstract: Methods, systems, and devices are provided for identifying hazards. According to one aspect, a computer-implemented method can include receiving a plurality of sensor data including one or more image files from a mobile device. The method can include generating one or more position and label pairs based on the plurality of sensor data. The method can include assigning a hazard recognition to each of the position and label pairs. The method can include assigning a score associated to each of the hazard recognitions. The method can include displaying a result including one or more image results based on the one or more image files, one or more hazard recognitions, the one or more hazard recognitions associated with at least one of the one or more image results, and one or more scores associated to each of the hazard recognitions.

Abstract: Methods, systems, and devices are provided for identifying hazards. According to one aspect, a computer-implemented method can include receiving a plurality of sensor data including one or more image files from a mobile device. The method can include generating one or more position and label pairs based on the plurality of sensor data. The method can include assigning a hazard recognition to each of the position and label pairs. The method can include assigning a score associated to each of the hazard recognitions. The method can include displaying a result including one or more image results based on the one or more image files, one or more hazard recognitions, the one or more hazard recognitions associated with at least one of the one or more image results, and one or more scores associated to each of the hazard recognitions.

Abstract: Methods, systems, and devices are provided for identifying hazards. According to one aspect, a computer-implemented method can include receiving a plurality of sensor data including one or more image files from a mobile device. The method can include generating one or more position and label pairs based on the plurality of sensor data. The method can include assigning a hazard recognition to each of the position and label pairs. The method can include assigning a score associated to each of the hazard recognitions. The method can include displaying a result including one or more image results based on the one or more image files, one or more hazard recognitions, the one or more hazard recognitions associated with at least one of the one or more image results, and one or more scores associated to each of the hazard recognitions.

Abstract: A system and method to provide patient education prior to surgery or post-surgery is disclosed. The patient education can be customized based on different factors and provided in a progressive sequence. Patients may be required to complete patient educational lessons and also provide feedback to assess how they are doing. Due to a variety of patient circumstances and the potential for abrupt changes in patient health status prior to, during, or following a health procedure, it is necessary for patient education to adapt to a patient's changing needs. The disclosure describes a technique to assign patient education materials in an adaptive and responsive manner.

Abstract: A method of accessing a plurality of network elements (NE) with at least one network element management program (NEMP) running on at least one element manager (EM) comprises the steps of capturing a username and a password within said network element management program (NEMP) and submitting said captured username and password to each of said plurality of network elements (NE) so as to effect administrative address privileges for each of said plurality of network elements (NE) without re-capturing said username and said password.