Abstract: In one embodiment, a device in a network receives a machine learning encoder and decoder trained by a supervisory service. The service trains the encoder and decoder using vibration measurement data sent to the service by a plurality of devices. The device trains, based on the received encoder, a classifier to determine whether vibration measurement data is indicative of a behavioral anomaly. The device receives vibration measurement data captured by a particular set of one or more vibration sensors of a monitored system. The device evaluates, using the trained decoder, the received vibration measurement data to determine whether the data is indicative of a structural anomaly in the monitored system. The device evaluates, using the trained classifier, the received vibration measurement data to determine whether the data is indicative of a behavioral anomaly in the monitored system.

Abstract: In one embodiment, a device in a network receives a machine learning encoder and decoder trained by a supervisory service. The service trains the encoder and decoder using vibration measurement data sent to the service by a plurality of devices. The device trains, based on the received encoder, a classifier to determine whether vibration measurement data is indicative of a behavioral anomaly. The device receives vibration measurement data captured by a particular set of one or more vibration sensors of a monitored system. The device evaluates, using the trained decoder, the received vibration measurement data to determine whether the data is indicative of a structural anomaly in the monitored system. The device evaluates, using the trained classifier, the received vibration measurement data to determine whether the data is indicative of a behavioral anomaly in the monitored system.

Abstract: A system provides an interface which includes a plurality of rows associated with patients, and a plurality of data columns for different departments that provide a holistic overview of action items for the different departments. Cells in the data columns can be manipulated, e.g. by selection of an option in a dropdown menu for the cell, to request an action of a corresponding department, or to indicate completion of a task by the corresponding department. Input into a cell can also automatically trigger changing of data or color coding in another cell for the patient. Color coding is utilized to allow viewers to quickly assimilate information from a displayed interface of the system. A number of calculations are performed based on input and/or imported data. For example, a value or color coding may be altered based on a calculated risk of readmission score.