The embodiments of the present disclosure are for a signal processing circuit. The signal processing circuit includes an analog circuit. The analog circuit is used for processing an initial signal it receives. The initial signal includes a target signal and a noise signal. The analog circuit includes a first processing circuit and a second processing circuit. The first processing circuit is used to increase a ratio of the target signal to the noise signal, and output a first processed signal. The second processing circuit is used to amplify the first processed signal. A gain multiple of the second processing circuit to the first processed signal varies with a frequency of the first processed signal. The first processing circuit includes a common mode signal suppression circuit used to suppress a common mode signal in the initial signal, a lowpass filter circuit, and a high-pass filter circuit.

A device may obtain raw heartbeat data associated with a plurality of wavelength channels. The device may generate, based on a feature vector transformation, a plurality of feature vectors, each corresponding to a respective one of the plurality of wavelength channels. The device may identify a set of selected feature vectors, from the plurality of feature vectors, based on a plurality of squares of correlation coefficients, each associated with a respective pair of the plurality of feature vectors. The device may generate, based on a principal component analysis, an average feature vector of the set of selected feature vectors. The device may determine initial heartbeat cycle data based on the average feature vector. The device may correct heartbeat cycle gaps in the initial heartbeat cycle data in order to determine final heartbeat cycle data.

Aspects of the invention include a method including collecting, by a processor, physiological data from a user in an environment and a sound waveform from the user's environment. The method detects and labels as a potential annoyance, by the processor, a set of potential annoyance data based on the collected physiological data and the sound waveform. The method decomposes, by the processor, the sound waveform into a first sound waveform segment associated with the set of potential annoyance data and a second sound waveform segment not associated with the set of potential annoyance data. The method predicts, by the processor, that the potential annoyance is an actual annoyance. The method filters and modifies, by the processor, the first sound waveform segment associated with the actual annoyance and provides, by the processor, the second sound waveform segment not associated with the actual annoyance to the user.

A signal measurement circuit comprises: a sensor electrode layer connected via a sensor cable to a measurement amplifier circuit; an active shielding layer, which runs along a side of the sensor electrode layer that faces away from the patient; and a first insulating layer that runs between the sensor electrode layer and the active shielding layer. The sensor electrode layer and the active shielding layer are embodied to be electrically conductive.

The present invention relates to a device, system and method for determining pulse pressure variation of a subject. To enable more reliably determining pulse pressure variation of a subject the device comprises a signal input (11) configured to obtain an input signal representing a hemodynamic signal of the subject, a processor (12) configured to process the input signal and compute a pulse pressure variation and a signal output (13) configured to output the computed pulse pressure variation. The pulse pressure variation is computed by deriving a pulse height signal from the input signal, deriving a pulse height baseline and a de-trended pulse height signal from the pulse height signal as the ratio between the difference between extrema of the de-trended pulse height signal and the respective value of the pulse height baseline signal, and computing the pulse pressure variation from the de-trended pulse height signal and the pulse height baseline.

In some embodiments, an electric field generator includes a differential oscillator that oscillates at a nominal frequency. The electric field generator is connected to a differential antenna that radiates an electric field. A differential detector measures a frequency of the generated electric field as the electric field interacts with a body (such as a human body) in a reactive near-field region of the electric field. For each of one or more internal components of the body, a computation unit determines a respective periodic behavior in the measured frequency indicative of movement of the internal component. The computation unit also computes, for each of the one or more internal components of the body, a respective rate of movement (such as a heart rate or a respiration rate) of the internal component according to the respective periodic behavior in the measured frequency.

In some embodiments, an electric field generator generates an electric field at a nominal frequency and a nominal amplitude. The electric field generator is connected to an antenna that radiates the electric field. A detector measures a frequency and an amplitude of the generated electric field as the electric field interacts with a body (such as a human body) in a reactive near-field region of the electric field. For each of one or more internal components of the body, a computation unit determines a respective periodic behavior in the measured frequency corresponding to movement of the internal component. The computation unit also computes, for each of the one or more internal components, a respective rate of the movement of the internal component based on the determined respective periodic behavior in the measured frequency. A gain control circuit adjusts the nominal amplitude according to the measured amplitude.

Disclosed are devices, systems and methods for in vivo monitoring of localized environment conditions within a patient user by measuring analytes, including glucose, oxygen, and/or other analytes. In some aspects, a sensor device includes a wafer-based substrate, at least one electrochemical sensor two-electrode contingent including a working electrode and a reference electrode on the substrate and configured to detect a target analyte in a body fluid when the sensor device is deployed within a subject's body, where the working electrode is functionalized by a chemical layer configured to facilitate a reaction involving the target analyte that produces an electrical signal; and an electronics unit in communication with the electrochemical sensor electrode contingent to transmit the electrical signal to an external processor.

Conventionally, a neuronal controller located inside the central nervous system governing the maintenance of the upright posture of the human body is designed from a control system perspective using proportional-integral-derivative (PID) controllers, wherein human postural sway is modeled either along a sagittal plan or along a frontal plane separately resulting in limited insights on intricacies of a governing neuronal controller. Also, existing neuronal controllers using a reinforcement learning (RL) paradigm are based on complex actor-critic on-policy algorithms. Analyzing human postural sway is critical to detect markers for progression of balance impairments. The present disclosure facilitates modelling the neuronal controller using a simplified RL algorithm, capable of producing postural sway characteristics in both sagittal and frontal plane together. The Q-learning technique of the RL paradigm is employed for learning an optimal state-action value (Q-value) function for a tuneable Markov Decision Process (MDP) model.

A method for generating PPG signals may include photographing a skin surface to generate fragment video data; extracting fragment frame images from the fragment video data; performing image quality detection on the fragment frame images to generate an image quality detection result; when the image quality detection result indicates an up-to-standard image, performing one-dimensional red and green light signal extraction on the fragment frame images to generate first red and green signals; performing signal band-pass filtering preprocessing on the first red and green signals to generate second red and green signals; performing maximum frequency difference determination on the second red and green signals to generate a first determination result; when the first determination result indicates an up-to-standard signal, performing signal-to-noise ratio determination on the second red and green signals to generate a second determination result; and when the second determination result indicates an up-to-standard signal, generating a first PPG signal.