A method of determining a fitness level of user with a physiological sensor. The method includes measuring a physiological value of the user with the physiological sensor, correlating the measured physiological value into a measurement of the user's respiratory rate and tidal volume, calculating a second respiratory rate value using the measured tidal volume, calculating a breathing efficiency (BE) ratio based on a comparison of the user's measured respiratory rate and the calculated second respiratory rate value, correlating the calculated BE ratio to a predetermined threshold, and assigning a classification to the user based on the calculated BE ratio. The classification is indicative of the user's respiratory function performance.

Described are systems for beds that can include sensors for sensing physical phenomena in an environment surrounding a bed, a display for outputting information about the environment, the bed, and a sleeper, and a controller communicably coupled to the sensors. The controller can receive the sensed physical phenomena from the sensors, analyze the physical phenomena to determine at least one of environmental, sleep, and health metrics of a sleeper in the bed, and determine, based on at least one of the environmental, sleep, and health metrics of the sleeper, control signals to modify the environment surrounding the bed. The controller can also output, at the display, the environmental, sleep, and health metrics of the sleeper. The controller can also transmit the control signals to a second controller in order to engage a home automation device. The physical phenomena can include ambient sound, ambient light, ambient CO2 concentration, and/or ambient temperature.

A healthcare apparatus includes a ballistocardiogram (BCG) sensor configured to sense a ballistocardiogram signal of a subject, a camera configured to acquire a color facial image, and a processor configured to detect a region of interest (ROI) from the color facial image, to detect a first color image of a forehead area to acquire a first black and white image, to detect a second color image of a cheek area to acquire a second black and white image, to apply the first and second black and white images to a predetermined trained algorithm model to output a remote photoplethysmography (rPPG) signal waveform of the subject, to calculate a first heart rate from the BCG signal waveform, to calculate a second heart rate from the remote PPG signal waveform, and to output a heart rate of the subject based on the first heart rate and the second heart rate.

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.

Hearing aid system comprising at least one hearing aid is provided. The hearing aid system further comprising an input unit for receiving an input sound signal from an environment of the hearing aid user and providing at least one electric input signal representing said input sound signal, an output unit for providing at least one set of stimuli perceivable as sound to the hearing aid user based on processed versions of said at least one electric input signal, a processing unit connected to said input unit and to said output unit and comprising signal processing parameters to provide said processed versions of said at least one electric input signal, at least one photoplethysmogram (PPG) sensor configured to provide a PPG signal of the hearing aid user, and a listening effort determination unit configured to provide at least one PPG morphology parameter value based at least on the PPG signal, compare the at least one PPG morphology parameter value with at least one corresponding reference PPG morphology parameter value and determine a morphology comparison measure, and determine a listening effort of the hearing aid user. A hearing aid is further provided.

A system, device and method are provided for continuously generating more than 0.01 watt of electrical energy by harnessing mechanical or kinetic energy from a reciprocating motion of a user's torso during breathing. The reciprocating motion causes reciprocating lateral and medial translation of two chambers of the device housing. That reciprocating lateral and medial translation rotates a gear, which in turn, drives a dynamo to produce electrical energy. Since the conversion from the mechanical energy of the spinning rod to power is direct, the energy-conversion efficiency may be up to 90% or higher. The device may further comprise one or more charging means for providing electrical energy to one or more peripheral electronic devices. In some embodiments, additional integrated functions may include auxiliary energy storage, backup energy, emergency power, mini-MP3 player, data recorder, GPS interface, miniature video recorder, speaker, abdomen muscle exercise, timing, Bluetooth interface, and heath information analyzer.

The present invention relates to a system for real-time determination of a local strain of a lung during artificial ventilation. The system comprises a device for electrical impedance tomography (EIT), which device is configured to capture an electrical impedance distribution along at least one two-dimensional section through a human thorax, and further comprises a device for assigning the captured electrical impedance distribution, which device is configured to divide the captured electrical impedance distribution at different times during the artificial ventilation into a multiplicity of EIT pixels and to assign a specific value of the electrical impedance at a specific time to a specific EIT pixel.

Embodiments disclosed herein improve digital stethoscopes and their application and operation. A first method detects of a respiratory abnormality using a convolution. A second method counts coughs for a patient. A third method predicts a respiratory event based on a detected trend. A fourth method forecasts characteristics of a future respiratory event. In a fifth embodiment, a base station is provided for a digital stethoscope.

An implantable medical device is disclosed. A housing includes a hollow body forming a first electrode on an outer surface with end caps affixed to opposite ends, one end cap forming a second electrode. A microcontroller circuit is provided and includes a microcontroller operable under program instructions stored within a non-volatile memory device. An analog front end is interfaced to the electrodes to sense electrocardiographic signals. A transceiver circuit is operable to wirelessly communicate with an external data device. The program instructions define instructions to continuously sample the electrocardiographic signals into the non-volatile memory device and to offload the non-volatile memory device to the external data device. A receiving coil and a charging circuit are operable to charge an onboard power source for the microcontroller circuit.

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.