5 results about "Respiratory activity" patented technology

The present invention discloses a system and method for identifying phases of breathing activity and managing exercises. The system includes a wearable device configured to capture physiological data of user comprising acoustic sound signals associated with inhalation, exhalation, and breath holding, and inertial measurement unit (IMU) data including accelerometer, gyroscope, and magnetometer data. A database stores user data and trained models, including probabilistic breath phase, breath phase transition, posture, and stability models. A computing device, in network communication with the wearable device and the database, executes program modules to receive the physiological data, preprocess the sound and motion data from physiological data, and extract features therefrom. The sound data are processed into Mel-frequency cepstral coefficient features, and the motion data are processed into slope-based features. Using these features, the system predicts breath phases in real time, segments individual breath cycles, and determines transitions between inhalation, holding, and exhalation

The present invention relates to a method for determining the cardiac or respiratory activity of a subject, the method comprising: measuring and recording sample measurements over a period of time that are at least related to the kinematic characteristics or position of an accessory (2) worn on the head of the subject; and processing the sample measurements to determine the cardiac or respiratory activity.

The invention discloses a friction nanometer generator integrated on a mask and a breathing interaction application method of the friction nanometer generator, and belongs to the technical field of wearable intelligent equipment and human-computer interaction. The generator adopts a multi-layer flexible film structure, and comprises a positive triboelectric material polyacrylonitrile (PAN)-based nanofiber membrane, a negative triboelectric material polyvinylidene fluoride (PVDF)-based nanofiber membrane, a PET breathable spacer layer located between the positive triboelectric material polyacrylonitrile (PAN)-based nanofiber membrane and the negative triboelectric material polyvinylidene fluoride (PVDF)-based nanofiber membrane, and upper and lower copper microgrid electrodes attached to each other. After the structure is packaged, a power generation unit capable of being embedded into the mask is formed, and respiratory airflow is used for driving the friction electricity material to make periodic contact and separation, so that characteristic electric signals corresponding to respiratory activities are generated. The invention further provides a respiration interaction system, multiple respiration modes such as normal respiration and deep respiration are recognized in real time through a signal processing and mode recognition circuit by analyzing information such as electric signal frequency and amplitude, and interaction feedback is provided. Self-powered, wearable and non-invasive continuous respiration monitoring and intelligent interaction are achieved.

The invention relates to a method for determining an individual anaerobic threshold (61), the method comprising the steps of: a) Acquiring an input signal (10), comprising a respiratory activity signal of a subject (1) executing a physical exercise, b) Processing said input signal (10), comprising * A first processing stage (S1) during which an output signal (11) is determined (300) from the input signal (10), wherein the output signal (11) is indicative of a respiration and / or of a property of the respiration of the subject (1), * A second processing stage (S2) during which the individual anaerobic threshold (61) is determined from the output signal (11), the individual anaerobic threshold (61) being a time point during the physical exercise, or an associated value at this time point, characterized in that the processing of the input signal (10) comprises submitting (200) the input signal (10) to a machine learning module (20), wherein the machine learning module (20) comprises one or more trained neural networks (21), that execute the first and / or at least parts of the second processing stage (S1, S2).

This invention relates to the field of wearable medical monitoring technology, specifically a wearable chest monitoring belt for monitoring physiological signals. The belt includes: synchronously extracting tension and respiratory phase data and performing temporal registration to generate a tension quantization matrix with belt phase markings; identifying and clustering key tension peaks based on this matrix to establish a local deformation mapping template; continuously matching real-time data using the template to dynamically construct and iteratively update a spatiotemporal multidimensional tensor field model of the thoracic cavity; decoupling the background component and respiratory activity component from this model, inputting the activity component into a lung function state inference network, and generating lung function simulation parameters frame by frame; integrating the above information to synthesize a multimodal thoracic cavity physiological state description map; and adaptively controlling the data acquisition and processing link based on this map. This invention achieves dynamic spatiotemporal modeling of thoracic cavity respiratory deformation and continuous inference of lung function parameters.