| CPC G16H 40/20 (2018.01) [A61B 5/7264 (2013.01); A61B 5/7275 (2013.01); A61B 5/747 (2013.01); G06N 20/00 (2019.01); G06Q 10/1095 (2013.01); G06Q 50/06 (2013.01); G06Q 50/26 (2013.01); G16H 10/60 (2018.01); G16H 40/40 (2018.01); G16H 40/67 (2018.01); G16H 50/20 (2018.01); G16H 50/70 (2018.01); H04L 9/32 (2013.01); A61B 2560/0214 (2013.01)] | 20 Claims |
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1. A method, in a data processing system comprising a processor and a memory, the memory comprising instructions that are executed by the processor to configure the processor to implement a multi-sensor health monitoring platform, the method comprising:
applying a machine learning model to predict patient needs and patient activity trends based on physiological features and activity features of the patient;
applying the machine learning model to predict energy requirements and energy availability for a plurality of medical sensors based on the predicted patient needs and patient activity trends, wherein the predicted energy requirements indicates an amount of energy needed to power a subset of medical sensors, of the plurality of medical sensors, to monitor at least one predicted patient need or activity at a future time point based on the predicted patient needs and patient activity trends, wherein the subset of medical sensors are medical sensors that monitor physiological features or activity features of the patient specific to the at least one predicted patient need or patient activity;
scheduling recharging of the subset of medical sensors, in the plurality of medical sensors, based on the predicted energy requirements and predicted energy availability of the subset of medical sensors, wherein the scheduling schedules recharging of the subset of medical sensors at a time point prior to the future time point such that the recharging causes at least one power source of the subset of medical sensors to have available electrical power to satisfy the predicted energy requirements at the future time point;
automatically controlling the plurality of medical sensors based on the predicted patient needs, patient activity trends, and predicted energy requirements, to set the subset of medical sensors to a recharge state in response to a current time being the time point prior to the future time point, to set the subset of medical sensors to an activate state in response to the current time being the future time point, and to set one or more other medical sensors in the subset of medical sensors to an inactive state in response to the current time being the future time point;
collecting sensor data from the subset of medical sensors in response to the current time being the future time point; and
applying the machine learning model to generate a point-of-care recommendation based on the collected sensor data.
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