Abstract: A method comprises accessing historical signal and other information received from a tracking device configured to scan for signals transmitted by local devices and record other data as the tracking device moves within the geographic area during each of a plurality time intervals. A training dataset is generated based on the historical signal and other information and used to train a machine learning model configured to predict tracking device movement patterns. The machine learning model is applied to current signal and other information to detect a variance from one or more predefined routines associated with the tracking device. A notification is sent to a monitoring device associated with the tracking device in response to detecting the variance from the one or more predefined routines associated with the tracking device.

Abstract: A method comprises accessing historical signal and other information received from a tracking device configured to scan for signals transmitted by local devices and record other data as the tracking device moves within the geographic area during each of a plurality time intervals. A training dataset is generated based on the historical signal and other information and used to train a machine learning model configured to predict tracking device movement patterns. The machine learning model is applied to current signal and other information to detect a variance from one or more predefined routines associated with the tracking device. A notification is sent to a monitoring device associated with the tracking device in response to detecting the variance from the one or more predefined routines associated with the tracking device.

Abstract: A method comprises accessing historical signal and other information received from a tracking device configured to scan for signals transmitted by local devices and record other data as the tracking device moves within the geographic area during each of a plurality time intervals. A training dataset is generated based on the historical signal and other information and used to train a machine learning model configured to predict tracking device movement patterns. The machine learning model is applied to current signal and other information to detect a variance from one or more predefined routines associated with the tracking device. A notification is sent to a monitoring device associated with the tracking device in response to detecting the variance from the one or more predefined routines associated with the tracking device.

Abstract: A method comprises accessing historical signal and other information received from a tracking device configured to scan for signals transmitted by local devices and record other data as the tracking device moves within the geographic area during each of a plurality time intervals. A training dataset is generated based on the historical signal and other information and used to train a machine learning model configured to predict tracking device movement patterns. The machine learning model is applied to current signal and other information to detect a variance from one or more predefined routines associated with the tracking device. A notification is sent to a monitoring device associated with the tracking device in response to detecting the variance from the one or more predefined routines associated with the tracking device.

Abstract: A method comprises accessing historical signal and other information received from a tracking device configured to scan for signals transmitted by local devices and record other data as the tracking device moves within the geographic area during each of a plurality time intervals. A training dataset is generated based on the historical signal and other information and used to train a machine learning model configured to predict tracking device movement patterns. The machine learning model is applied to current signal and other information to detect a variance from one or more predefined routines associated with the tracking device. A notification is sent to a monitoring device associated with the tracking device in response to detecting the variance from the one or more predefined routines associated with the tracking device.

Abstract: Systems and methods for performing a red blood cell exchange procedure are disclosed. In one aspect, the flow rates of a replacement fluid (e.g., red blood cells from a healthy donor) and plasma being flowed to a blood source are calculated based on certain parameters of the procedure that are known, including the flow rate of blood being drawn from the source and the volume of replacement fluid to be used during the procedure. The replacement fluid flow rate and the plasma flow rate are calculated in a way that allows for simultaneous depletion of the supply of replacement fluid and achievement of another prescribed process parameter. The other prescribed process parameter depends on the nature of the procedure, namely whether the fluid volume and hematocrit of the source are to change by the end of the procedure or remain the same.

Abstract: Systems and methods for performing a red blood cell exchange procedure are disclosed. In one aspect, the flow rates of a replacement fluid (e.g., red blood cells from a healthy donor) and plasma being flowed to a blood source are calculated based on certain parameters of the procedure that are known, including the flow rate of blood being drawn from the source and the volume of replacement fluid to be used during the procedure. The replacement fluid flow rate and the plasma flow rate are calculated in a way that allows for simultaneous depletion of the supply of replacement fluid and achievement of another prescribed process parameter. The other prescribed process parameter depends on the nature of the procedure, namely whether the fluid volume and hematocrit of the source are to change by the end of the procedure or remain the same.