The present disclosure provides wearable apparatus and method for calculating drift-free plantar pressure parameters for gait monitoring of an individual. Most conventional techniques use different kind of sensors placed in in-sole based wearable apparatus but are costly and not effective in calculating accurate plantar pressure parameters. The disclosed wearable apparatus uses off-the shelf piezoelectric sensors that are widely available in market with less cost. The drift-free plantar pressure parameters are calculated using drift-free static pressure data obtained by numerically integrating acquired dynamic sensor data from the piezoelectric sensors, using a LiTCEM correction mechanism. A 6-DOF Inertial Measurement Unit (IMU sensor) helps in isolating zero-pressure duration indicating when a foot of the individual is in air during a stride, while obtaining the drift-free static pressure data. The disclosed wearable apparatus calculate the drift-free plantar pressure parameters for long duration and facilitates monitoring walking patterns of the individual.

In order to improve the noise suppression in a video image stream 3 of a medical image recording system, the video image stream including a sequence of frames, it is provided that an image processing unit 5 of the image recording system analyses the video image stream 3 continuously in real time and determines at least one variability between successive image pixels of the frames, for example of spatially adjacent image pixels of frames and/or of image pixels of a plurality of the frames corresponding to one another spatially and temporally, in order, on the basis of the variability determined, to set at least one parameter of a noise suppression subsequently applied to the video image stream 3. As a result, the noise suppression can be adapted continuously to a current recording situation.

An electrocardiogram waveform analysis apparatus includes: a waveform input section to which an electrocardiogram waveform is input; an application range setting section configured to set an application range of a filter in the input electrocardiogram waveform; a filtering section configured to apply a filtering process to the application range of the filter; a beat detector configured to detect beats from the electrocardiogram waveform that has undergone the filtering process; and an output section configured to perform an outputting process based on the detected beats.

A wearable monitoring device includes a band configured to at least partially encircle a portion of the body of a subject and at least one optical emitter and at least one optical detector attached to the band. The band includes a generally cylindrical outer body portion and a generally cylindrical inner body portion secured together in concentric relationship. The inner body portion includes light transmissive material and has outer and inner surfaces. A layer of cladding material is near the inner body portion inner surface, and a plurality of windows are formed in the cladding material that each serve as a light-guiding interface to the body of the subject. The plurality of windows are circumferentially spaced apart from each other.

An apparatus for detecting a body component according to an example embodiment includes: a sensor configured to detect a bio-signal of an object according to a contact pressure that gradually changes between the object and the sensor; and a processor configured to determine a time point, at which an amplitude of an alternating current (AC) component of the bio-signal is maximum or a slope of a direct current (DC) component of the bio-signal is maximum, and to detect a body component of the object based on the determined time point.

Systems and methods for filtering time-varying data for filtering and extracting a predicted physiological signal. A method including: segmenting the time-varying data into temporal windows; using a trained filter machine learning model, predicting an error for each prediction of the physiological signal for each window of time-varying data, the filter machine learning model trained using physiological signal predictions based on training time-varying data and known values of the physiological signal for the training time-varying data; discarding each window of time-varying data when the predicted error for such window is greater than a threshold; and where the window of time-varying data is not discarded, outputting at least one of the window of time-varying data and the predicted error for each prediction of the physiological signal.

A blood oxygen detection method, including: obtaining at least two red light signals, at least two infrared signals, and a green light signal; determining red light direct current data and a component signal of a red light alternating current signal based on the at least two red light signals; determining infrared direct current data and a component signal of an infrared alternating current signal based on the at least two infrared signals, where the component signal includes an arterial signal; determining red light alternating current data based on the component signal of the red light alternating current signal and the green light signal; determining infrared alternating current data based on the component signal of the infrared alternating current signal and the green light signal; and determining blood oxygen saturation based on the red light direct current data, the red light alternating current data, and the infrared direct current data.

A medical apparatus includes a shaft, an expandable frame, a membrane, a diagnostic electrode, a reference electrode, and a processor. The shaft is configured for insertion into an organ of a patient. The expandable frame is coupled to a distal end of the shaft. The diagnostic electrode, which is disposed on an external surface of the expandable frame, is configured to sense diagnostic signals when in contact with tissue. The reference electrode is disposed on a surface of the expandable frame directly opposite the diagnostic electrode, wherein the reference electrode is electrically insulated from the tissue and is configured to sense interfering signals.

A device for detecting spatial differences in fluid level changes in a tissue of a patient may include a support structure for securing the device to a body part of the patient, a processing element operably connected to the support structure, a wireless networking interface operably connected to the support structure and in communication with the processing element and an external computing device via a network, a first transmission module operably connected to the support structure and in communication with the processing element, a second transmission module and a third transmission module operably connected to the support structure and in communication with the processing element. When activated, the first transmission module transmits a first time varying magnetic field through the tissue of the patient. The second and third transmission modules, which are spatially separated from one another, receive first and second versions, respectively, of the first time varying magnetic field.

In one embodiment, a method includes receiving cardiac signal segments responsively to electrical activity sensed by a first sensing electrode in contact with tissue, injecting the cardiac signal segments into a cable, which extends to a recording apparatus, the cable outputting corresponding noise-added cardiac signal segments responsively to noise acquired in the cable, training an artificial neural network to at least partially compensate for electrical noise that will be added to signals in the cable responsively to the received cardiac signal segments and corresponding noise-added cardiac signal segments, receiving a cardiac signal responsively to electrical activity sensed by a second sensing electrode, applying the trained artificial neural network to the cardiac signal yielding the cardiac signal with noise-compensation, which at least partially compensates for noise, which is not yet in the cardiac signal but will be added in the cable, and outputting the cardiac signal with noise-compensation via the cable.