A detected signal of a heartbeat sensor and a detected signal of an inertial sensor are received by a processor. The processor estimates a heart rate in a case where an exercise intensity obtain from the detected signal of the inertial sensor is equal to or more than a threshold value, based on a relation between the exercise intensity and a heart rate obtained from the detected signal of the heartbeat sensor during a period in which the exercise intensity obtained from the detected signal of the inertial sensor is less than the threshold value.

Systems and methods for monitoring and treating patients with heart failure are discussed. The system may receive patient atrioventricular (AV) conduction characteristic under different heart rates or patient conditions. Stimulation parameters including stimulation timing parameters may be stored in a memory. The system may include a stimulation control circuit configured to determine a parameter update schedule indicating a timing at which to update stimulation parameter using patient AV conduction characteristic, and dynamically update at least a portion of the stored set of stimulation parameters at the determined parameter update schedule. For a specified heart rate or heart rate range, a stimulation parameter may be selected from the set of the stimulation parameters for use during cardiac stimulation.

A frame storage stores an image obtained by imaging a region of at least part of the body of a user. A vital sign signal detector detects a signal sequence of a vital sign that cyclically varies from plural imaged regions of the body of the user by using captured images of a predetermined number of frames stored in the frame storage. A correlation calculator obtains the correlation between the signal sequences of the vital sign detected from the respective imaged regions of the body. An identity determining section determines whether or not the respective imaged regions of the body belong to the same user based on the correlation between the signal sequences of the vital sign detected from the respective imaged regions of the body.

In accordance with an example embodiment, a body-weight sensing scale includes cardio-based physiological sensing circuitry to detect heart characteristics of a user, and provide outputs indicative of the detected heart characteristics. A processor circuit is arranged with the cardio-based physiological sensing circuitry to process data to provide a noise-reduced cardiogram signal which characterizes functionality/health of the user's heart.

A method for detecting movements of a plurality of points (P) of a surface (21), comprising a measuring step during which an incident ultrasonic wave is emitted into the air towards the surface and an ultrasonic wave reflected into the air by the surface (21) is detected. During the measuring step, each measuring point is illuminated by the incident ultrasonic wave at a multiplicity of angles of incidence, and the reflected ultrasonic wave is detected by a network of receiving transducers (3) comprising a plurality of ultrasonic receiving transducers (3a). The movements of the surface are determined at a measuring point by determining a delay and/or a phase shift between two beam-forming signals for said measuring point.

Methods and system are provided that identify motion data associated with consistent electrical and mechanical behavior for a region of interest of the heart. The methods and systems acquire electrical cardiac signals indicative of physiologic behavior of at least a portion of the heart over a plurality of cardiac cycles. The methods and systems acquires motion data indicative of mechanical behavior of a motion sensor over the plurality of cardiac cycles to form a motion data collection, the motion data indicative of mechanical behavior of the region of interest when the motion sensor is in contact with the region of interest. The designating ectopic beats within the cardiac cycles may be based on the electrical cardiac signals, the ectopic beats producing electrically inconsistent (EI) data within the motion data collection. The methods and systems identify mechanically inconsistent (MI) data within the motion data collection based on irregular changes in the motion data. The methods and systems remove at least a portion of the EI and MI data from the motion data collection based on the designating and identifying operations to form an electrically/mechanically consistent (EMC) motion data collection.

A method of extracting heart information from a body micro-movement is provided. The method includes a face tracking step, a frame difference averaging step, smoothing filtering step, and a sliding peak detection step.

Methods and systems are provided for obtaining cleaned sequences showing trajectories of movement of a center of gravity and for estimating a biometric information pattern or value of a target. One of the methods includes removing noises from initial sequences showing trajectories of movement of a center of gravity to obtain the cleaned sequences. Another one of the methods includes reading cleaned sequences of the target into a memory, extracting features from the cleaned sequences, and estimating a biometric information pattern or value of the target from the extracted features, using a classification or regression model of biometric information patterns or values. The biometric information pattern may be a pattern derived from respiratory or circulatory organs of a target.

Cardiac dyssynchrony of a patient may be evaluated based on electrical activity of a heart of the patient and corresponding chest wall motion of the patient sensed via an external accelerometer. In one example, an acceleration signal indicative of the chest wall motion is generated by an external accelerometer positioned on the chest wall of the patient. A processor of a diagnostic device integrates the acceleration signal to generate a velocity signal and temporally correlates the velocity signal and an electrical cardiac signal. The processor determines a time delay between a deflection of the electrical cardiac signal indicating ventricular electrical activation and a subsequent greatest peak of the velocity signal. The time delay may indicate a degree of electromechanical delay of the left ventricle. In some examples, the processor generates an output indicative of a cardiac dyssynchrony status based on the time delay.

A system includes a sensor configured to sense first and second physiological signals produced by a source; and a processing device communicatively coupled to the sensor. The processing device is configured to: receive the first and second physiological signals; determine a first value of a signal characteristic; determine a second value of the signal characteristic; access a scaling map having scaling vectors, and each scaling vector having at least one signal characteristic correction value; determine a scaled first value and a scaled second value based on a first scaling vector and a second scaling vector, respectively; and predict a physiological event based on the scaled first value of the signal characteristic and the scaled second value of the signal characteristic.