An apparatus for generating a metabolism model may include a processor configured to obtain a predetermined number of bio-information profiles from a bio-sensor, extract a representative bio-information profile from the obtained predetermined number of bio-information profiles, and generate the metabolism model for correcting an error of the bio-sensor by using the extracted representative bio-information profile.

Disclosed are methods and medical device systems for automated delivery of therapies for pain and determination of need for and safety of treatment. In one embodiment, such a medical device system may comprise a sensor configured to sense at least one body signal from a patient; and a medical device configured to receive a first sensed body signal from the sensor; determine a patient pain index based at least in part on said first sensed body signal; determine whether said patient pain index is above at least a first pain index threshold; determine a safety index based at least in part on a second sensed body signal; select a pain treatment regimen based on at least one of said safety index and or a determination that said pain index is above said first pain index threshold; and deliver said pain treatment regimen.

A headset comprise a frame and a vibration sensor coupled to the frame. The vibration sensor may be located in a nosepad of the frame, and configured to measure tissue vibrations of a user when the headset of worn by the user. A controller receives a signal corresponding to the measured vibration data from the vibration sensor, and analyzes the received signal to infer a sequence of states of the received signal, such as a sequence of respiratory states. The controller further determines a value of a health metric based upon the inferred sequence of states, e.g., a respiratory rate of the user, and performs an action using the determined value of the health metric.

The invention provides systems for characterizing a biological sample by analyzing emission of fluorescent light from the biological sample upon excitation and methods for using the same. The system includes a laser source, collection fibers, a demultiplexer and an optical delay device. All references cited herein are incorporated by reference in their entirety as though fully set forth. Unless defined otherwise, technical and scientific terms used herein have the same meaning as commonly understood by one of-ordinary skill in the art in which this invention belongs.

A system for monitoring a patient's nutrition administration, the system including a processing device and a memory storing instructions executable by the processing device for: accessing nutrition administration information for a plurality of patients describing nutrition administered by different forms to the plurality of patients; accessing medical information about the plurality of patients from a healthcare information system; accessing nutritional requirement data describing a nutritional requirement for the plurality of patients; determining a difference between a nutritional requirement for a patient of the plurality of patients based on the nutritional requirement data, an amount of a nutrient administered to the patient, and the nutrition administration information; and integrating the nutrition administration information for the patient, the medical information for the patient, and the nutritional requirement data for the patient, for determining a nutritional adjustment for administering the amount of the nutrient to the patient based on the difference.

An energy balancing system scales portion sizes of user-selected meal recipes to control a digital scale to prompt the user to measure scaled quantities of meal ingredients to prepare scaled meals. Physical activity tracking devices provide data to generate physical activity energy expenditures for energy burned by physical activity. The physical activity energy is added to a basal metabolic rate of energy expenditure that is a function of sex, weight, height, and age. The total energy expended are scaled down for a weight-loss goal to obtain the total recommended energy. A recipe portion-size optimizer adjusts scaling factors for each recipe so that the total recommended energy is met by the scaled meals. Amounts of nutrients for the recipes can also be scaled by the scaling factors to match recommended nutrient amounts when the meal recipe optimizer generates the scaling factors using an over-determined linear system optimizer.

Systems and methods to determine a risk factor related to dehydration and thermal stress of a subject are disclosed. Exemplary implementations may: generate output signals, by one or more sensors worn on a body of a subject, conveying information related to one or more of location of the subject, motion of the subject, temperature of the subject, cardiovascular parameters of the subject; store information related to the subject; obtain the output signals; determine in an ongoing manner, from the output signals, values of a water loss metric that correlates with estimated percentage of bodyweight of the subject lost in water; obtain heat index information for a contextual environment surrounding the subject; determine in an ongoing manner, from the output signals, values of an exertion metric that correlates with exertion of the subject due to work; and determine in an ongoing manner values for an aggregated risk factor of the subject.

Method, device and system for providing consistent and reliable glucose response information to physiological changes and/or activities is provided to improve glycemic control and health management.

A wearable device for tracking swim activities of a user is provided. The wearable device may include one or more sensors configured to generate sensor data, and based on the sensor data, the wearable device may determine swim metrics such as swim stroke count, swim stroke type, swim lap count, and swim speed. The determined swim metrics may be filtered based on one or more swim periods during which the user is likely to have been swimming. The wearable device may determine such swim periods based on the sensor data and/or the determined swim metrics.

An amount of glycemic dysfunction associated with mis-timing (e.g., delay) of meal boluses based on replay analysis is determined. The amount of dysfunction of historical or estimated bolusing as compared to an optimally timed bolus based on the replay analysis is quantified and visualized. Inferences may be made about diabetes meal management regarding inputs from a patient.