A computer implemented method includes accessing a multivariate time series set of samples collected by multiple biological sensors sensing a first biological function over a first period of time, dividing the data set into windows, calculating statistical dependencies between the samples of the timeseries data collected by each sensor, generating a relationship matrix as a function of the statistical dependencies, and transforming the relationship matrix to generate a first feature vector for each window of time that captures the statistical dependencies amongst the sensors.

A device may receive historic temporal data identifying events associated with a system, and may perform block bootstrapping of the hierarchical time series data, based on a hyperparameters, to generate blocks of data points of the historic time series data. The device may process the blocks of data points, with a plurality of different machine learning models, to calculate predictions, and may apply weights to the predictions to generate weighted predictions. The device may aggregate the weighted predictions to generate aggregated predictions, and may apply final weights to the aggregated predictions to generate weighted aggregated predictions. The device may aggregate the weighted aggregated predictions to generate a final prediction, and may perform one or more actions based on the final prediction.

The present disclosure addresses the technical problem of information loss while representing a physiological signal in the form of symbols and for recognizing patterns inside the signal. Thus making it difficult to retain or extract any relevant information which can be used to detect anomalies in the signal. A system and method for anomaly detection and discovering pattern in a signal using morphology aware symbolic representation has been provided. The system discovers pattern atoms based on the strictly increasing and strictly decreasing characteristics of the time series physiological signal, and generate symbolic representation in terms of these pattern atoms. Additionally the method possess more generalization capability in terms of granularity. This detects discord/abnormal phenomena with consistency.

Introduced here are multi-channel light sources able to produce a broad range of electromagnetic radiation. A multi-channel light source (also referred to as a “multi-channel emitter”) can be designed to produce visible light and/or non-visible light. For example, some embodiments of the multi-channel light source include illuminant(s) capable of emitting electromagnetic radiation within the visible range and illuminant(s) capable of emitting electromagnetic radiation in a non-visible range, such as the ultraviolet range or infrared range. By capturing images in conjunction with the visible and non-visible light, additional information on the ambient scene can be gleaned which may be useful, for example, during post-processing.

A method of identifying a historical alert that is similar to an alert associated with a detected deviation from an operational state of a device includes receiving feature data including time series data for multiple sensor devices associated with the device and receiving an alert indicator for the alert. The method includes processing a portion of the feature data that is within a temporal window associated with the alert indicator to generate feature importance data for the alert. The feature importance data includes values indicating relative importance of each of the sensor devices to the alert. The method also includes identifying one or more historical alerts that are most similar, based on the feature importance data and stored feature importance data, to the alert.

Systems and methods of the present disclosure enable movement recognition and tracking by receiving movement measurements associated with movements of a user. The movement measurements are converted into feature values. An action recognition machine learning model having trained action recognition parameters generates, based on the feature values, an action label representing an action performed during an action-related interval. An activity recognition machine learning model having trained activity recognition parameters generates, based on the action label, an activity label representing an activity performed during an activity-related interval, where the activity includes the action. A task recognition machine learning model having trained task recognition parameters generates, based on the action label and the activity label, a task label representing a task performed during a task-related interval, where the task includes the activity and action. An activity log is updated based on the action label, the activity label, and the task label.

According to one embodiment, an information processing device includes: a divider configured to divide time series data of an objective variable into a plurality of first sections based on values of the objective variable; a model generator configured to generate, based on time series data of an explanatory variable and the time series data of the objective variable, a plurality of prediction models in which the explanatory variable and the objective variable are associated, for the plurality of first sections; a selector configured to select a first section from the plurality of first sections based on at least one of the time series data of the explanatory variable and the time series data of the objective variable; and a predictor configured to predict the value of the objective variable by using the prediction model generated for the selected first section.

Systems for studying motion are provided. A computing device [10] has a processor [20], an accelerometer [11], a gyroscope [12], a magnetometer [13] and storage/computer readable media [30] in communication with one another. The computing device [10] can sense, classify, qualify and/or quantify real-time motion data of a moving target against classified initial motion data in a motion library [32]. Motion data is processed as particularized units of motion. The computing device [10] may use machine learning algorithms for “training” and “learning.” The computing device [10] can be used in many industries, including the fitness industry, where computing device [10] can be used with wearable technology.

In a method and medical imaging apparatus for depiction of medical imaging data, medical imaging data of the examination object are acquired over a period of time, and an artifact parameter is established, which characterizes artifacts that occur as a result of breathing of the examination object during the period of time. The medical imaging data are displayed on a display screen together with a depiction of the artifact parameter.

There is provided an information processing apparatus including: a housing that has an inflow/outflow part through which gas flows in and out; a barometer sensor that is arranged inside of the housing and detects atmospheric pressure; and a processing unit that processes information. The processing unit senses pressing of a user on the basis of an atmospheric pressure change inside of the housing detected by the barometer sensor.