A system including sensors configured to provide physiological markers of a developer and a controller configured provide information indicative of a user experience to the developer while receive signals from the sensors. The controller is configured to utilize cognitive analysis determine developer emotion responses as the developer receives the user experience. The controller compares a developer emotion classification with a user emotion classification of a user as the user generated the user experience. The system generates a prioritized backlog to identify points where emotion responses between user and developer are in common, or where emotion responses between user and developer differ.

A method including: for each image forming part of a plurality of images showing an individual, applying processing to the image so as to produce a biometric template relating to the individual; calculating an average template constituting an average of the biometric templates produced; modifying a chosen image from the plurality of images into a modified image, the modification being adapted so that an error between the average template and a biometric template produced by applying the processing to the modified image is smaller than an error between the average template and the biometric template that has been produced by applying the processing to the chosen image.

To participate in a health incentive program, a customer of a health-related business such as a health insurance company or medical group entity may use a physiological measurement device to report health-related activity to the business. However, a customer may report fraudulent activity to the business by allowing another party to use the physiological measurement device. In embodiments, an electronic device, a collection and validation server, and a physiological measurement device may execute methods to ensure that the user of the physiological measurement device has identified themselves properly to the business. In embodiments, the electronic device and the physiological measurement device may produce sets of physiological measurement data that are compared to determine that a user of the electronic device is the same as the user of the physiological measurement device. The electronic device may produce a set of physiological measurement data using video and image processing techniques.

Coherent light (e.g., laser light) is emitted into a cranium through an optical fiber. A tissue sample (e.g., red blood cells, blood vessels, brain tissue) within the cranium diffuses the coherent light. Different tissue sample motion quantities generate different coherent light interference patterns. An image of a coherent light interference pattern is captured with an image sensor coupled to an optical element. The speckle contrast of the image quantifies coherent light interference pattern. A waveform of sequentially captured speckle contrast values over time has characteristics that reflect intracranial blood flow health. If waveform characteristics indicate poor or questionable intracranial blood flow heath, a notification message is displayed, played, or otherwise transmitted.

A driver availability detection device includes an information acquisition unit to acquire information related to an occupant and a threshold setting unit to set an abnormal-state determination threshold for estimating the abnormal state of the occupant on a basis of an abnormal state score obtained by inputting the information related to the occupant acquired by the information acquisition unit to a machine learning model within a first threshold setting time.

A device implementing a system for machine-learning based gesture recognition includes at least one processor configured to, receive, from a first sensor of the device, first sensor output of a first type, and receive, from a second sensor of the device, second sensor output of a second type that differs from the first type. The at least one processor is further configured to provide the first sensor output and the second sensor output as inputs to a machine learning model, the machine learning model having been trained to output a predicted gesture based on sensor output of the first type and sensor output of the second type. The at least one processor is further configured to determine the predicted gesture based on an output from the machine learning model, and to perform, in response to determining the predicted gesture, a predetermined action on the device.

A biological information acquisition device includes a detection-value acquisition unit to acquire a detection value from a non-contact biometric sensor, a vital measurement unit to measure a vital sign of a target person (TP) using the detection value, an image-data acquisition unit to acquire image data indicating an image captured by a camera, an image processing unit to perform at least one of a state estimation process of estimating a state of the target person, an attribute estimation process of estimating an attribute of the target person, or a personal identification process of identifying the target person by performing image processing on the image captured including the target person, and a parameter setting unit to set a parameter in measuring the vital sign in accordance with a result of the image processing.

A method may involve receiving fingerprint sensor data from a fingerprint sensor system, detecting, according to the fingerprint sensor data, a presence of a digit on an outer surface of the apparatus in a fingerprint sensor system area; determining, according to the fingerprint sensor data, a digit force or a digit pressure of the digit on the outer surface of the apparatus; and making, according to the fingerprint sensor data, a time threshold determination. The time threshold determination may involve determining whether a length of time during which the digit force exceeds a threshold digit force or during which the digit pressure exceeds a threshold digit pressure is greater than or equal to a threshold length of time. The method may involve determining, based at least in part on the time threshold determination, whether to enable one or more emergency response functions of the apparatus.

A method and device for using ECG signals for biometric authorization that includes a machine-learning based signal processing approach for significantly removing noise signals from ECG signals being used. The present invention further includes a probability-based additional approach for further enhancing the signal relative to signal segments falsely identified as an actual ECG signal.

An information processing apparatus, including: a feature point detecting unit configured to detect a feature point from an image including biological information obtained via a sensor unit; and a feature value extracting unit configured to extract a feature value that characterizes the feature point based on a peripheral image including the feature point.