A patient health management platform accesses a metabolic profile for a patient and biosignals recorded for the patient during a current time period comprising sensor data and/or lab test data collected for the patient. The platform receives patient data recorded during the current time period comprising food items consumed, medications taken, and symptoms experienced by the patient. The platform implements a machine-learned metabolic model to determine a metabolic state of the patient at a conclusion of the current time period by comparing a true representation of the metabolic state and a prediction of the metabolic state. The true representation and the prediction are determined based on the recorded biosignals and the recorded patient data, respectively. The platform generates a patient-specific treatment recommendation outlining instructions for the patient to improve their metabolic state and provides the patient-specific treatment recommendation to the patient device for display to the patient.

This document discusses, among other things, apparatus, systems, or methods to efficiently collect heart sound data, including detecting first heart sound information of a heart of a patient using a heart sound sensor in a first, low-power operational mode, and detecting second heart sound information of the heart using the heart sound sensor in a separate second, high-power operational mode. The operational mode of the heart sound sensor can be controlled using physiologic information from the patient, including heart sound information, information about a heart rate of the patient, or other physiologic information from the patient that indicates worsening heart failure.

Physiological signal processing systems include a photoplethysmograph (PPG) sensor that is configured to generate a physiological waveform, and an inertial sensor that is configured to generate a motion signal. A physiological metric extractor is configured to extract a physiological metric from the physiological waveform that is generated by the PPG sensor. The physiological metric extractor includes an averager that has an impulse response that is responsive to the strength of the motion signal. Related methods are also described.

Systems and methods for monitoring patients with a chronic disease are described. A patient management system may sense physiological signals from a patient using one or more implantable or other ambulatory sensors, and generate from the physiological signals a chronobiological rhythm indicator (CRI) such as indicating a circadian rhythm. A reference CRI associated with a prior hospital admission event of the patient may be provided to the patient management system, which compares the CRI to the reference CRI and generates a readmission risk score indicating the patient's risk of subsequent hospital readmission due to a worsened condition of the chronic disease. The readmission risk score may be provided to a user or a process, or used to initiate or adjust a therapy delivered to the patient.

Systems and methods are provided for optimizing hemodynamics within a patient's heart, e.g., to improve the patient's exercise capacity. In one embodiment, a system is configured to be implanted in a patient's body to monitor and/or treat the patient that includes at least one sensor configured to provide sensor data that corresponds to a blood pressure within or near the patient's heart; at least one component designed to cause dyssynchrony of the right ventricle, and a controller configured for adjusting the function of the at least one component based at least in part on sensor data from the at least one sensor.

A system, method, and computer program product for generating item recommendations based on sensor data captured by sensors on client machines. A server processes collected sensor data to estimate user physical activity based on sensor data patterns, and forms a user profile based on the estimated user physical activity. The server associates the user profile with relevant product or service recommendations, based on previous purchases by a user or by others matching the user profile. The user may make a purchase based on the displayed recommendations or forward the recommendations to others. The recommendations include discounts based on the user profile. Subsequent sensor data showing satisfaction with a purchased item serves as a testimonial to user satisfaction with the item. The sensor data may be collected from a user in an augmented or virtual reality system, and the recommendations may be displayed in such a system.

A movement tracking device that includes a housing, a rotatable spool secured within the housing, a rotary sensor in operable communication with the spool, and a conductive wire configured to be repeatedly unspooled from and respooled onto the rotatable spool. The conductive wire has a distal end extendable from the housing. The movement tracking device also includes a plurality of resonators and a processor in communication with the plurality of resonators and the rotary sensor. The plurality of resonators are disposed in or on the housing and positioned about the conductive wire. Each of the plurality of resonators is configured to create one or more magnetic fields through which the conductive wire extends. The processor is configured to receive information from the plurality of resonators and the rotary sensor and determine a position of the conductive wire.

A wearable drug delivery device, techniques, and computer-readable media that provide an application that implements a diabetes treatment plan for a user are described. The drug delivery device may include a controller operable to direct operation of the wearable drug delivery device. The controller may provide a selectable activity mode of operation for the user. Operation of the drug delivery device in the activity mode of operation may reduce a likelihood of hypoglycemia during times of increased insulin sensitivity for the user and may reduce a likelihood of hyperglycemia during times of increased insulin requirements for the user. The activity mode of operation may be manually activated by the user or may be activated automatically by the controller. The controller may automatically activate the activity mode of operation based on a detected activity level of the user and/or a detected location of the user.

A system for generating an alert for a systemic infection of a patient comprises an implantable medical device configured to measure at least one physiological parameter, a remote monitoring system configured to receive information from the implantable medical device, and an information system configured to communicate with said remote monitoring system. At least one of the implantable medical device, the remote monitoring system and the information system is configured to analyze information relating to said at least one physiological parameter to generate an alert signal for a systemic infection of the patient based on a state of the at least one physiological parameter, wherein at least one of the implantable medical device, the remote monitoring system and the information system is further configured to generate an alert message to be provided to a specified destination based on said alert signal.

A continuous glucose monitoring system may utilize externally sourced information regarding the physiological state and ambient environment of its user for externally calibrating sensor glucose measurements. Externally sourced factory calibration information may be utilized, where the information is generated by comparing metrics obtained from the data used to generate the sensor's glucose sensing algorithm to similar data obtained from each batch of sensors to be used with the algorithm in the future. The output sensor glucose value of a glucose sensor may also be estimated by analytically optimizing input sensor signals to accurately correct for changes in sensitivity, run-in time, glucose current dips, and other variable sensor wear effects. Correction actors, fusion algorithms, EIS, and advanced ASICs may be used to implement the foregoing, thereby achieving the goal of improved accuracy and reliability without the need for blood-glucose calibration, and providing a calibration-free, or near calibration-free, sensor.