A medical system including processing circuitry configured to receive a cardiac signal indicative of a cardiac characteristic of a patient from sensing circuitry and configured to receive a glucose signal indicative of a glucose level of the patient. The processing circuitry is configured to formulate a training data set including one or more training input vectors using the cardiac signal and one or more training output vectors using the glucose signal. The processing circuitry is configured to train a machine learning algorithm using the formulated training data set. The processing circuitry is configured to receive a current cardiac signal from the patient and determine a representative glucose level using the current cardiac signal and the trained machine learning algorithm.

Each accessory device in a set of accessory devices may establish a respective communication link between the accessory device and an ear-wearable device. A particular accessory device in the set of accessory devices may receive data via the communication link between the particular accessory device and the ear-wearable device. The data comprise information generated based on sensor signals from sensors that monitor a user of the ear-wearable device. The accessory devices perform a health monitoring activity based on the data.

Apparatus, systems, and methods for monitoring a sensor module mounted in a sensor platform, wherein the sensor platform includes an adhesive side and a pocket, wherein the pocket is designed to receive the sensor module, to facilitate sensing by the sensor module of physiological attributes, and to allow insertion and removal of the sensor device from the pocket.

A system for operating third party proprietary software on a medical monitoring device operating native proprietary software and a system for obtaining compatible third party proprietary software for operation on the monitoring device.

An Internet of Thing (IoT) device includes a body with a processor, a camera and a wireless transceiver coupled to the processor.

A system for treating and/or monitoring a patient includes a patient physiological parameter monitoring patch and a companion device. The patient physiological parameter monitoring patch including an energy harvesting module, an energy storage module, a sensor module and a communication module. The energy harvesting module harvesting energy from one or more ambient sources, the energy being storable in the energy storage module and usable by one or more components of the patient physiological parameter monitoring patch. The sensor module senses one or more physiological parameters of the patient and the communication module can transmit the sensed data. The companion device can receive the sensed physiological parameters and can send the same to a remote device or store the same.

A jacket that includes a first portion, a second portion, and an interface. The interface is movably coupled to the first portion and the second portion. The first portion comprises multiple medical modules. The multiple medical modules include at least one medical sensor and a physiological signal conduit for transferring a physiological signal from an inner side of the first portion to an exterior side of the first portion. The first and second portions are configured to be detachably coupled to a mobile phone. When the jacket is at a closed position then the first portion, the second portion and the interface define an inner space that is configured to receive the mobile phone and the first and second portions contact opposite sides of the mobile phone. When the jacket is at an open position, then only one of the first and second portions contacts the mobile phone.

There is provided information processing apparatuses, information processing methods, programs, and information processing systems capable of reinforcing the reinforcement target behavior without causing the user's consciousness. A quest is presented for urging a user to execute a reinforcement target behavior, and when the user executes the reinforcement target behavior upon the request, an electroencephalogram is measured, emotion is estimated on the basis of the electroencephalogram, and when the number of times that dominant emotion having the highest emotion score among emotion of the emotion estimation results is detected exceeds a predetermined number of times, an avatar is changed corresponding to the emotion that is detected as the dominant emotion. The present disclosure can be applied to long-term feedback technology.

Provided are a method and device for predicting a user state according to an embodiment of the present invention. The method for predicting a user state according to an embodiment of the present invention comprises the steps of: acquiring first biometric data for a plurality of users; fine-tuning a prediction model on the basis of the first acquired biometric data and a fixed learning parameter; outputting a predicted user state using a fine-tuned prediction model by inputting a second biometric data for predicting the user state for predicting the user state for at least one user, wherein the fixed learning parameter is extracted on the basis of a first model that is different from the prediction model and trained to predict a user state for the plurality of users by inputting the first biometric data for the plurality of users.

Concepts and technologies disclosed herein are directed to a smart automated external defibrillator (“AED”). According to one aspect of the concepts and technologies disclosed herein, the AED can present a menu that includes a plurality of modes. The plurality of modes can include a first responder mode, an Internet of Things (“IoT”) mode, and a general use mode. The AED can receive, via an input component, a selection, from the menu, of a mode from the plurality of modes. In response to the selection, the AED can configure a network connectivity component in accordance with a setting specified in the mode.