A monitoring system a user activity sensor to determine patterns of activity based upon the user activity occurring over time.

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.

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.

A smart multi-modal telehealth IoT system for respiratory analysis. Such a system includes a body area sensor network comprised of meshed wireless sensor nodes and advanced machine learning techniques. The system may be used to remotely diagnose a user's respiratory illness and monitor their health.

A method for time-synchronizing waveforms from different patient monitors that does not require devices to have high-precision synchronized clocks or to be coupled to a triggering synchronization signal generator. Comparable signals may be obtained from different devices either by placing selected sensors from the devices in the same locations, or by filtering signals from one device to obtain a signal comparable to signals from another device. Filtering may for example transform waveforms into independent components and identify a component that matches a signal from another device. The comparable signals may then be transformed into frequency variation curves, such as time intervals between peak values, to facilitate detection of the time shift between the signals. Cross correlation of the frequency variation curves may be used to locate the precise time shift between the signals. Use of frequency variation curves may be more robust than directly comparing and correlating the original signals.

A method and system are disclosed for use in monitoring/screening/diagnosing sleep or wake state of a subject or patient. The method generally includes monitoring the patient's activity during one or more sleep sessions comprising a plurality of intervals known as epochs. The sleep/wake state of the subject is determined during each epoch of the session using actigraphy data obtained during the monitoring session. The actigraphy data provides information about the activity of a patient during an epoch. The sleep or wake state is determined based on a ratio of the activity count during an epoch to the activity count during a preceding epoch. If the ratio is greater than a first activity threshold, then a “wake” indication may be provided by, for example, the system. Alternatively, or additionally, a “wake” indication may be determined if the activity count during the epoch is greater than a threshold.

The invention relates to a control method for a neuroprosthetic device, allowing to monitor and reduce pathological tremors in users via the stimulation of the peripheral muscles and modulation of the afferent pathways.

The electronics module (100) comprises a housing (101) comprising an opening (17). A processor (109). A flexible electronics structure (500) comprising a flexible substrate on which an electronics component 105 is provided. The electronics component (105) is communicatively connected to the processor 109. The flexible substrate extends through the opening (17) in the housing (101) such that the electronics component (105) is located at least partially outside of the housing (101). The processor is located within the housing.

An apparatus configured for application to a surface of a body, the apparatus comprising: an array of mechanomyography sensors spatially distributed across a substrate, each mechanomyography sensor configured to detect mechanomyography signals from the body to which the apparatus is applied; and a pressure bias system configured to provide a variation in contact pressure of the mechanomyography sensors to the body surface to receive mechanomyography signals at different levels of applied contact pressure.

An apparatus configured for application to a surface of a body, the apparatus comprising: an array of mechanomyography sensors spatially distributed across a substrate, each mechanomyography sensor configured to detect mechanomyography signals from the body to which the apparatus is applied; and a pressure bias system configured to provide a variation in contact pressure of the mechanomyography sensors to the body surface to receive mechanomyography signals at different levels of applied contact pressure.