Provided is a video-based method and system for accurately estimating heart rate and facial blood volume distribution, and the method mainly comprises the following steps: firstly, carrying out face detection of video frame containing human face, and extracting face image sequence and face key position points sequence in time dimension; secondly, compressing these sequence of face image and face key position points to obtain the facial signals in time dimension; thirdly, estimating facial blood volume distribution by facial signals mentioned in third step; finally, estimating heart rate values by using model based on deep learning technology and the spectrum analysis method respectively, then fusing the estimation results by Kalman filter to promote the accuracy of heart rate estimation.

A multi-sensor system (100) having a plurality of dental sensors (101-S; 101-M) for arrangement in an oral cavity, including at least one slave dental sensor (101-S) for acquiring measurement data in an oral cavity with a transmitting device (103) for transmitting the measurement data; and a master dental sensor (101-M) with a receiving device (109-M) for receiving the measurement data from the slave dental sensor (101-S).

A method used for determining at least one of an administration method or a dosage of a drug that can be administered to a patient, the method executed by a processor and including: acquiring electrocardiogram data indicating an electrocardiogram of a patient who is given the drug with the administration method and the dosage; determining, on the basis of the electrocardiogram data, whether a waveform abnormality that can be evaluated with an electrocardiogram may have occurred in the patient; and outputting information about advisability of modifying at least one of the administration method or the dosage on the basis of a result of the determining.

An illustrative optical measurement system may include a primary controller; a plurality of secondary controllers communicatively coupled to the primary controller; and a plurality of modules, each module included in the plurality of modules comprising: a light source configured to emit light directed at a target, and a plurality of detectors configured to detect photon arrival times for the light after the light is scattered by the target; wherein: the plurality of modules is divided into a plurality of module subsets, and each module subset included in the plurality of module subsets is communicatively coupled to a respective secondary controller included in the plurality of secondary controllers.

A system and method for efficiently transmitting signals from a sensing device to a remote device are disclosed. The sensing device includes at least one sensor for sensing information. The sensed information could be associated with heart activity. The sensing device smooths the sensed signal and then calculates the difference between the smoothed sensed signal and a locally stored baseline signal to determine a differential signal, which can be transmitted as a smaller packet of data than the full sensed signal. The remote device receives information associated with the differential signal and uses a local copy of the baseline signal to reconstruct the original smoothed sensed signal.

A method of collecting physiological parameter data of a monitored subject comprises measuring a biosignal from which the physiological parameter is deducible, including noise; converting the noisy measured biosignal to a vector having different frequency components with corresponding magnitude coefficients; discarding select frequency components with coefficients below a prescribed threshold; and communicating the reduced vector to a computing device for processing to deduce the physiological parameter. A method of processing physiological parameter data comprises receiving a measured biosignal with electromagnetic interference noise; obtaining from the noisy measured biosignal representative data using a machine learning algorithm; and determining the physiological parameter from the representative data. A system for monitoring a physiological parameter comprises a wearable sensor configured to measure a biosignal and to remove noise from the measured signal, and a portable computing device configured to receive a transmitted signal from the sensor and to determine the physiological parameter therefrom.

A method of analyzing a multidimensional image tensor containing a plurality of images comprises: performing imaging scans of a subject imaging data; generating the multidimensional image tensor from the imaging data; determining a spatial basis tensor containing basis images based on the multidimensional image tensor; determining a temporal basis tensor containing basis functions for a temporal dimension based on the multidimensional image tensor; determining a core tensor that relates the spatial basis tensor to the temporal basis tensor; pre-multiplying the core tensor and the temporal basis tensor to produce a modified temporal basis tensor; storing the spatial basis tensor and the modified temporal basis tensor; and generating an image by multiplying at least (i) at least a portion of the spatial basis tensor and (ii) at least a portion of the modified temporal basis tensor.

There is provided a system, method and device for dynamically compressing an actigraphy signal at a source device. The method comprises receiving the actigraphy signal related to a user's physical activity from an accelerometer sensor on the source device and for compressing the actigraphy signal by determining regions of interest in the actigraphy signal to capture, said compressing performed by: computing a rapid change factor value indicating a drastic change in movement activity in said actigraphy signal, said rapid change factor computed based on determining a spurious free dynamic range of a second order difference signal of the actigraphy signal and subsequently determining the step size of the actigraphy signal, the step size indicating the interval with which the actigraphy signal instantaneously changes its value from one sample to another; automatically scanning the second order difference signal to locate samples in the second order difference signal having a value greater than the rapid change factor value, said located samples defining primary segment boundaries; -extracting frames of the encoded actigraphy signal between two consecutive primary segment boundaries and discarding outlying regions of the encoded actigraphy signal; and —outputting only the extracted frames representing a compressed actigraphy signal to an external computing device for subsequent processing.

Disclosed are a method and apparatus for processing physiological signals. The method is inclusive of steps of obtaining the physiological signals; grouping the physiological signals based on their sampling frequencies and/or generation mechanisms, so as to acquire grouping results; and compressing, based on the grouping results, each group of physiological signals.

A module for a brain-computer interface includes means for measuring neuronal activity in at least a portion of a population of neurons; means for stimulating at least the portion of the population of neurons; and means for local processing adapted to pre-analyze measured neuronal activity. A hub for a brain-computer interface includes means for powering a for the brain-computer interface by an alternating current (AC), wherein a charge transmitted in a cycle of the AC is below a tissue-damage threshold. A brain-computer interface comprises the module and the hub. A method for interfacing a brain and a computer includes measuring neuronal activity in at least a portion of a population of neurons; pre-analyzing, locally at a module for measuring and stimulating the portion of the population of neurons, measured neuronal activity; and receiving, at a hub for the brain-computer interface, a pre-analysis of the measured neuronal activity.