The present disclosure provides a system comprising a communication interface and computer for assigning a label to the biomolecule fingerprint, wherein the label corresponds to a biological state. The present disclosure also provides a sensor arrays for detecting biomolecules and methods of use. In some embodiments, the sensor arrays are capable of determining a disease state in a subject.

The present disclosure may be embodied as systems and methods for action recognition developed using a multimodal dataset that incorporates both visual data, which facilitates the accurate tracking of movement, and active acoustic data, which captures the micro-Doppler modulations induced by the motion. The dataset includes twenty-one actions and focuses on examples of orientational symmetry that a single active ultrasound sensor should have the most difficulty discriminating. The combined results from three independent ultrasound sensors are encouraging, and provide a foundation to explore the use of data from multiple viewpoints to resolve the orientational ambiguity in action recognition. In various embodiments, recurrent neural networks using long short-term memory (LSTM) or hidden Markov models (HMMs) are disclosed for use in action recognition, for example, human action recognition, from micro-Doppler signatures.

An apparatus and method determine movement information for a user that carries an accelerometer whilst moving. The apparatus receives acceleration data from the accelerometer defined relative to a frame of reference of the accelerometer. A transformation is determined and applied to the acceleration data or to data derived from the acceleration data to determine acceleration data is a user frame of reference that includes a user direction of travel and a side to side direction transverse to the user direction of travel. The acceleration data or data derived from the acceleration data is analyzed to determine a time period corresponding either to a user stride period or to a step period as the user is walking or running; and information about accelerations in the side to side direction are used to disambiguate whether the determined time period corresponds to the user stride period or to the user step period.

The present disclosure provides a system comprising a communication interface and computer for assigning a label to the biomolecule fingerprint, wherein the label corresponds to a biological state. The present disclosure also provides a sensor arrays for detecting biomolecules and methods of use. In some embodiments, the sensor arrays are capable of determining a disease state in a subject.

The present disclosure provides a system comprising a communication interface and computer for assigning a label to the biomolecule fingerprint, wherein the label corresponds to a biological state. The present disclosure also provides a sensor arrays for detecting biomolecules and methods of use. In some embodiments, the sensor arrays are capable of determining a disease state in a subject.

The present disclosure provides a system comprising a communication interface and computer for assigning a label to the biomolecule fingerprint, wherein the label corresponds to a biological state. The present disclosure also provides a sensor arrays for detecting biomolecules and methods of use. In some embodiments, the sensor arrays are capable of determining a disease state in a subject.

A computing system determines if an event has occurred. A first window is defined that includes a subset of a plurality of observation vectors modeled as an output of an autoregressive causal system. A magnitude adjustment vector is computed from a mean computed for a matrix of magnitude values that includes a column for each window of a plurality of windows. The first window is stored in a next column of the matrix of magnitude values. Each cell of the matrix of magnitude values includes an estimated power spectrum value for a respective window and a respective frequency. A second matrix of magnitude values is updated using the magnitude adjustment vector. Each cell of the second matrix of magnitude values includes an adjusted power spectrum value for the respective window and the respective frequency. A peak is detected from the next column of the second matrix of magnitude values.

An information-processing method is provided, including a step of specifying first data clusters to which the first data each belongs and second data clusters to which the second data each belongs; a step of extracting the first data belonging to one of the first data clusters and the second data acquired at a corresponding time as correspondent data; a step of totalizing the number of pieces of correspondent data included in each of the second data clusters and calculating a total value for each cluster for the second data; and a step of specifying the second data clusters which are transition destinations of the correspondent data in a subsequent time and counting the number of pieces of transition destination data which is the correspondent data transitioning from each of the second data clusters which are transition sources to the second data clusters which are the transition destinations.

The present disclosure provides a system comprising a communication interface and computer for assigning a label to the biomolecule fingerprint, wherein the label corresponds to a biological state. The present disclosure also provides a sensor arrays for detecting biomolecules and methods of use. In some embodiments, the sensor arrays are capable of determining a disease state in a subject.

Factors, other than external factors, that affect the state change of a system are correctly specified, even when an external factor having a strong correlation with the state change of the system exists and preprocessing is required in the time series of an analysis object. An analysis device 500 includes a time series acquisition unit 510, a feature-time series conversion unit 520, and an influence degree calculation unit 540. The time series acquisition unit 510 acquires a difference time series between the value of a target time series and a predicted value of the target time series generated on the basis of the value of a first description time series in a plurality of description time series, and a second description time series. The feature-time series conversion unit 520 extracts, for each of the second description time series, a feature quantity from the second description time series and converts the feature quantity into a feature time series. The influence degree calculation unit 540 calculates the influence degree of each of the second description time series on a change of the value of the difference time series on the basis of the feature time series of the second description time series and the difference time series.