Methods, computer systems, and computer readable media are provided for promoting positive activity patterns for users and facilitate long-term adherence to the activity patterns, such as by providing alerts or electronic reminders to ambulate in a fashion that is responsive to an individual's actual activity patterns and behaviors and compatible with routine activities in the workplace and home. In particular, embodiments of the present invention are directed to measuring physical activity patterns during the waking hours of a human, and in some embodiments continuously measuring these activity patterns; automatically ascertaining whether the patterns exhibit sufficient frequency and variability of activity such as confers certain health benefits; and if the patterns do not manifest such features, to adaptively provide sensible reminders at irregular within-day intervals such as are likely to establish healthy patterns of ambulation and other light activity.

A blood pressure prediction method includes acquiring a data source identifier and original data; performing a data preprocessing operation on the original data; performing normalized filtering on a first-class PPG original signal to generate a standard PPG data sequence; when the data source identifier is a first-class PPG original signal identifier, performing baseline drift removal and normalized filtering on a second-class PPG original signal to generate a standard PPG data sequence; when the data source identifier is a second-class PPG video identifier, performing video quality detection and normalized signal conversion on third-class video data to generate a standard PPG data sequence; obtaining a CNN model identifier; and selecting a corresponding CNN model to predict blood pressure on the standard PPG data sequence according to the data source identifier and/or the CNN model identifier.

Systems, methods and implantable devices configured to provide cardiac resynchronization therapy and/or bradycardia pacing therapy. A first device located in the heart of the patient is configured to receive a communication from a second device and deliver a pacing therapy in response to or in accordance with the received communication. A second device located elsewhere is configured to determine an atrial event has occurred and communicate to the first device to trigger the pacing therapy. The second device may be configured for sensing the atrial event by the use of vector selection and atrial event windowing, among other enhancements. Exception cases are discussed and handled as well.

A method for transmitting compressed brainwave physiological signals is provided and including detecting a plurality of brainwave physiological signals of a subject, and generating an electroencephalography based on a time sequence of the plurality of brainwave physiological signals; splitting the electroencephalography into a plurality of sub-images based on the time sequence; using a plurality of static feature tags and a plurality of dynamic displacement tags stored in a brainwave database to identify at least one static feature tag and a plurality of associated dynamic displacement tags based on the time sequence according to the plurality of sub-images; generating at least one superimposed group tag, the superposed group tag is used to integrate the identified static feature tag and the associated dynamic displacement tag according to the time sequence; and transmitting the identified static feature tag, the associated dynamic displacement tag, and the superimposed group tag to a remote cloud system according to the time sequence.

A method for context-aware self-calibration includes measuring for a plurality of time segments, at least one feature of at least one biosignal or each of at least one channel. Each biosignal is created in response to a user imagining an intended direction for each time segment. An object is moved along an actual decoded direction determined by an output of a decoder configured to correlate for each time segment the at least one feature to the intended direction. The decoder self-calibrates to minimize for each time segment, an error between the actual decoded direction, and the intended direction inferred subsequent to the respective time segment.

Embodiments of the present systems and methods may provide a non-invasive system to measure and integrate behavioral and cognitive features enabling early detection and progression tracking of degenerative disease. For example, a method of detecting neurodegenerative disease may comprise measuring functioning of at least one of the motor system, cognitive function, and brain activity of a subject during everyday life and analyzing the gathered at least one motor system data, cognitive function data, and brain activity data of the subject.

The present disclosure presents arrhythmia analysis systems and methods. One such method comprises processing an acquired ECG waveform signal to remove noise artifacts and form a denoised ECG waveform signal; processing the denoised ECG waveform signal to remove low quality segments and form a high quality ECG waveform signal; analyzing the high quality ECG waveform to detect a presence of a beat-independent ventricular arrhythmia; processing the denoised ECG signal to extract beat (R-peak) locations corresponding to QRS complexes from the denoised ECG signal; analyzing the denoised ECG signal to detect a presence of a beat-dependent ventricular arrhythmia based on the extracted beat (R-peak) locations; analyzing the denoised ECG signal to detect a presence of one or more supraventricular arrhythmias based on the extracted beat locations of the ECG signal; and outputting a report containing one or more arrhythmias detected by the analyzing steps. Other methods/systems are also provided.

A thermal camera system is used to derive cardiac and respiratory signals or information via extraction of cardiac and/or respiratory signals from a thermal signal at a region of interest (ROI), by a peak picking analysis of the thermal signal, or via analysis of a wavelet transform of the thermal signal.

A pulse measuring device includes: an image acquisition unit that acquires plural pieces of time-series photographed image data obtained by photographing a living body; an image processor that generates plural pieces of transformed image data corresponding to the plural pieces of photographed image data by performing a multiple resolution analysis on each of the plural pieces of photographed image data a plurality of times, each of the plural pieces of photographed image data being decomposed into a high-resolution component and a low-resolution component on the multiple resolution analysis; and a pulse measuring unit that calculates a feature quantity indicating luminance of a predetermined area in each of the plural pieces of transformed image data, calculates a variation period of the feature quantity by analyzing time-series data of the feature quantity, and calculates a pulse of the living body based on the variation period of the feature quantity.

In some embodiments, the present invention provides an exemplary inventive system that includes: an apparatus to record: individual's brain electrical activity, a physiological parameter of the individual, and iii) an environmental parameter; a computer processor configured to perform: obtaining a recording of the electrical signal data; projecting the obtained recording of electrical signal data onto a pre-determined ordering of a denoised optimal set wavelet packet atoms to obtain a set of projections; normalizing the particular set of projections of the individual using a pre-determined set of normalization factors to form a set of normalized projections; determining a personalized mental state of the individual by assigning a brain state; determining a relationship between: the physiological parameter, the environmental parameter, and the personalized mental state; generating an output, including: a visual indication, representative of the personalized mental state, and) a feedback output configured to affect the personalized mental state of the individual.