An athletic parameter measurement device worn by an athlete during an athletic activity session includes a housing which attaches to the athlete, a display, a processor associated with the display, and an athletic parameter measurement sensor. During the athletic activity, the device detects, using the sensor, a vertical jump height of the athlete, and displays, during the performance of the athletic activity session, a representation of the vertical jump height on the display.

A device management system having a device having a structural component and a terminal connected to the device via a network includes a feature value detection unit provided in the device and for detecting a feature value of the structural component, a storage device storing in time series the feature value detected by the feature value detection unit, a lifespan estimation unit for estimating a lifespan of the structural component from the feature value stored in the storage device, a determination unit for determining whether a remaining period that is a time period from a time at which estimation is performed to an end of the lifespan is less than or equal to a set scheduled period, and a notification unit provided in the terminal and for sending a notification indicating that the remaining period is less than or equal to the set scheduled period.

A method for detecting an exit of a patient from a hospital bed, the method comprising: detecting a presence of the patient on the bed, upon detection of the patient on the bed, monitoring an indication that the patient has moved from a predetermined patient area on a patient receiving surface of the bed to outside the predetermined patient area, upon detection that the patient has moved outside the predetermined patient area, activating a bed alarm to indicate to a user that an exit of the patient from the hospital bed has been detected, and after monitoring the indication that the patient has moved from the predetermined patient area and further upon receiving a pause command, suspending monitoring the indication that the patient has moved from the predetermined patient area. There is also provided a system for detecting an exit of a patient from a hospital bed.

Systems and methods are described for securely monitoring a shipping container for an environmental anomaly using elements of a wireless node network of sensor-based ID nodes disposed within the container and a command node associated with the container. The method has the command node identifying which of the ID nodes are confirmed as trusted sensors based upon a security credential specific to each of the ID nodes; monitoring only the confirmed ID nodes for sensor data broadcast those ID nodes; detecting the anomaly based upon the sensor data from at least one of the confirmed ID nodes; automatically generating an alert notification related to the detected environmental anomaly for the shipping container; and transmitting the alert notification to the external transceiver to initiate a mediation response related to the detected environmental anomaly.

Systems and methods are described for securely monitoring a shipping container for an environmental anomaly using elements of a wireless node network of sensor-based ID nodes disposed within the container and a command node associated with the container. The method has the command node identifying which of the ID nodes are confirmed as trusted sensors based upon a security credential specific to each of the ID nodes; monitoring only the confirmed ID nodes for sensor data broadcast those ID nodes; detecting the anomaly based upon the sensor data from at least one of the confirmed ID nodes; automatically generating an alert notification related to the detected environmental anomaly for the shipping container; and transmitting the alert notification to the external transceiver to initiate a mediation response related to the detected environmental anomaly.

A building system for generating input forecast data. The building system comprising one or more memory devices configured to store instructions thereon that, when executed by one or more processors, cause the one or more processors to retrieve a current prediction set of measurements comprising current values associated with a plurality of time-steps; identify time-steps of the plurality of time-steps for which historical prediction values of a historical prediction set of measurements are outside of a tolerance of corresponding historical actual values of a historical actual set of measurements; replace each current value of the current prediction set of measurements that is associated with the identified time-steps with a predetermined value to generate an updated prediction set of measurements; and provide the updated prediction set of measurements to a prediction model.

A method, system, apparatus, and/or device that may include: a first sensor operable to take a first physiological measurement; a second sensor operable to take a second physiological measurement; and a processing device operatively coupled to the first sensor and the second sensor. The processing device may be operable to: receive a first measurement data for the first physiological measurement; receive a second measurement data for the second physiological measurement; generate an event data set based on the first measurement data with the second measurement data; determine an event occurred based on the event data set; determine a type of the event; and in response to the event being a safety event: determine a type of the safety event; identify a risk level of the safety event; and in response to the risk level exceeding a threshold level, notify a second device of the safety event.

A machine learning system passively monitors sensor data from the living space of a patient. The sensor data may include audio data. Audio features are generated. A trained machine learning model is used to detect a change in condition. In some implementations, the machine learning model is trained in a learning phase based on training data that includes questionnaires completed by caregivers and identified audio features.

A machine learning system passively monitors sensor data from the living space of a patient. The sensor data may include audio data. Audio features are generated. A trained machine learning model is used to detect a change in condition. In some implementations, the machine learning model is trained in a learning phase based on training data that includes questionnaires completed by caregivers and identified audio features.

A method includes receiving, from a plurality of magnetic field receivers including magnetic sensors, data characterizing samples obtained by the plurality of magnetic field receivers, the samples of a combination of a first magnetic field and a second magnetic field resulting from interaction of the first magnetic field and an object; determining, using the received data, a polarizability index of the object, the polarizability index characterizing a magnetic polarizability property of the object; classifying, using the determined polarizability index, the object as threat or non-threat; and providing the classification. Related apparatus, systems, techniques, and articles are also described.