Electronic devices and methods are disclosed. First and second electronic devices include processors, which implement one or more methods, including receiving sensor data, determining an exercise starting timepoint based on first sensor data, estimating an exercise posture of a user based on second sensor data, estimating a pattern of change in distance between each of the plurality of external electronic devices, indicated by changes in the relative position between each the plurality of external electronic devices over a time period of the determined exercise starting timepoint, based on third sensor data, and generating exercise information based on the estimated exercise posture and the estimated pattern of change in distance between the plurality of external electronic devices.

Nano-node sunscreen described herein enables significantly improved health monitoring and treatment of sun-related issues by utilizing internal (in-body) mechanisms and information and external mechanisms and information.

A physical area network for detecting head injuries described herein enables significantly improved cranial health monitoring and treatment by utilizing internal (in-body) mechanisms and information and external mechanisms and information.

Wirelessly networked systems of implantable and non-implantable medical devices with networking protocols, software, and hardware that allow for communications and energy transfer between different the medical devices (free standing, implants and wearables) using ultrasonic waves. The networks and methods of use are used to construct cardiac pacing, deep brain stimulation, and neurostimulation networks based on ultrasonic wide band technology.

A system may obtain a set of features characterizing a segment of inertial measurement unit (IMU) data generated by an IMU of an ear-wearable device. The system may apply a machine learning model (MLM) that takes the features characterizing the segment of the IMU data as input. The system may determine, based on output values produced by the MLM, whether a user of the ear-wearable device has potentially been subject to physical abuse. The system may then perform an action in response to determining that the user of the ear-wearable device has potentially been subject to physical abuse.

The invention relates to a communication method for communicating monitoring data between a monitoring device (4) and processing means (6), wherein the monitoring device (4) is configured for receiving the monitoring data. The method comprises the steps of sending a byte frame to the processing means (6), storing the byte frame, ordering the byte frame by separating the frame into respiratory rate bytes, heart rate bytes and additional bytes, and repeating the previous steps with a predetermined frequency until the processing means have received a heart rate data set, a respiratory rate data set, and an additional data set. The processing means create heart rate information from the heart rate data set and create respiratory rate information from the respiratory rate data set and display them to a user.

Systems and methods for assessing, monitoring, or theranosing a condition or disorder based on a comparison of limb stability for one or more limbs of a subject from a baseline. The method includes placing two or more inertial measurement sensors on the limbs of the subject, acquiring baseline limb excursion data from the inertial measurement sensors while a patient is performing at least one of a static balance activity and a dynamic balance activity by tracking the relative displacement of the respective two or more inertial measurement sensors; acquiring post-injury limb excursion data after an injury from the inertial measurement sensors while a patient is performing at least one of a static balance activity and a dynamic balance activity; and determining the activity clearance index as a function of a comparison of the baseline limb excursion data compared to the post-injury limb excursion data.

A computer network implemented system for improving the operation of one or more biofeedback computer systems is provided. The system includes an intelligent bio-signal processing system that is operable to: capture bio-signal data and in addition optionally non-bio-signal data; and analyze the bio-signal data and non-bio-signal data, if any, so as to: extract one or more features related to at least one individual interacting with the biofeedback computer system; classify the individual based on the features by establishing one or more brain wave interaction profiles for the individual for improving the interaction of the individual with the one or more biofeedback computer systems, and initiate the storage of the brain waive interaction profiles to a database; and access one or more machine learning components or processes for further improving the interaction of the individual with the one or more biofeedback computer systems by updating automatically the brain wave interaction profiles based on detecting one or more defined interactions between the individual and the one or more of the biofeedback computer systems. A number of additional system and computer implemented method features are also provided.

A sensor including electrodes, a control module and a physical layer module. The electrodes are configured to (i) attach to a patient, and (ii) receive a first electromyographic signal from the patient. The control module is connected to the electrodes. The control module is configured to (i) detect the first electromyographic signal, and (ii) generate a first voltage signal. The physical layer module is configured to: receive a payload request from a console interface module or a nerve integrity monitoring device; and based on the payload request, (i) upconvert the first voltage signal to a first radio frequency signal, and (ii) wirelessly transmit the first radio frequency signal from the sensor to the console interface module or the nerve integrity monitoring device.

Techniques are disclosed for adjusting a transmission frequency of a physiological monitoring unit, which includes a capture unit and a receiver unit. The capture unit and the receiver unit are wirelessly connected together and are configured to exchange data on the transmission frequency by means of a programming unit.