Abstract: A behavior recognition device acquires a video image in which a person is captured, and determines, by analyzing the acquired video image, whether or not an elemental behavior performed by the person is abnormal for each section that is obtained by dividing the video image. When the behavior recognition device determined that the elemental behavior is abnormal, the behavior recognition device extracts, from the acquired video image, the video image included in the section in which the elemental behavior is determined to be abnormal. The behavior recognition device transmits, in an associated manner, the extracted video image included in the section and a category of the elemental behavior that is determined to be abnormal.

Abstract: From plural observation features of a time series acquired by observing movements of a person, plural candidate segments are decided of a target action series containing plural respective actions expressing plural movements. Each of the plural candidate segments is divided into each action segment that is a time segment of the action, a likelihood corresponding to each of the plural actions computed for each of the action segments is normalized by action segment, and as an evaluation value a representative value is computed of normalized likelihood corresponding to each of the action segments selected from out of all of the respective action segments in the candidate segments based on an order of actions in the target action series. Being the target action series is determined in cases in which the evaluation value exceeds a common threshold.

Abstract: A hidden semi-Markov model includes plural second hidden Markov models each containing plural first hidden Markov models using types of movement of a person as states. The plural second hidden Markov models each use partial actions that are parts of actions determined by combining plural movements as states. In the hidden semi-Markov model observation probabilities are leant for each type of the movements of the plural first hidden Markov models using unsupervised learning. The learnt observation probabilities are fixed, and input first supervised data is augmented to give second supervised data, and transition probabilities of the movements of the first hidden Markov models are learned by supervised learning in which the second supervised data is employed. The learnt observation probabilities and transition probabilities are employed to build the hidden semi-Markov model that is a model for estimating segments of the partial actions.

Abstract: In a hidden semi-Markov model, observation probabilities for each type of movement of plural first hidden Markov models are learned using unsupervised learning. The learnt observation probabilities are fixed, input first supervised data is augmented so as to give second supervised data, and transition probabilities of the movements of the first hidden Markov models are learned by supervised learning in which the second supervised data is employed. The learnt observation probabilities and the learnt transition probabilities are used to build the hidden semi-Markov model that is a model for estimating segments of the actions. Augmentation is performed on the first supervised data by adding teacher information of the first supervised data to each item of data generated by at least one out of oversampling in the time direction or oversampling in feature space.

Abstract: An information processing apparatus acquires video image data that includes target objects including a person and an object, and specifies, by inputting the acquired video image data to a first machine learning model, a relationship between each of the target objects included in the acquired video image data. The information processing apparatus specifies, by using a feature value of the person included in the acquired video image data, a behavior of the person included in the video image data. The information processing apparatus predicts, by inputting the specified behavior of the person and the specified relationship to a probability model, a future behavior or a future state of the person.

Abstract: An information processing apparatus includes a processor that acquires measurement information measured by a device put on a task performer; detects a predetermined operation in the measurement information; extracts feature vectors from an operation section in which the predetermined operation has been detected; divides the feature vectors into clusters by division methods; obtains ratios at which the feature vectors are classified into the respective clusters in a predetermined time; performs learning by using the ratios as an input and using, as an output label, information indicating whether the task performer performs a predetermined task in the predetermined time; generates a task estimation model by determining weighting for the clusters based on a result of the learning; and estimates, by using the task estimation model, whether the task performer is in a process of performing the predetermined task.

Abstract: A guidance support method is disclosed. Target information is detected in space information stored in a storage device. A device position of a display device is measured in a space defined in the space information and a target position of a target defined in the target information. An icon position of an icon to display is determined at the display device and a movement of the icon based on the device position and the target position, which are measured. The icon moving as determined at the icon position displayed is displayed at the display device as determined. The icon position and the movement of the icon displayed at the display device is changed depending on a change of the device position.

Abstract: An information processing apparatus includes a processor that acquires measurement information measured by a device put on a task performer; detects a predetermined operation in the measurement information; extracts feature vectors from an operation section in which the predetermined operation has been detected; divides the feature vectors into clusters by division methods; obtains ratios at which the feature vectors are classified into the respective clusters in a predetermined time; performs learning by using the ratios as an input and using, as an output label, information indicating whether the task performer performs a predetermined task in the predetermined time; generates a task estimation model by determining weighting for the clusters based on a result of the learning; and estimates, by using the task estimation model, whether the task performer is in a process of performing the predetermined task.

Abstract: An information providing method includes: measuring a static object three-dimensionally in an environment, by a processor; sequentially measuring a position of a dynamic object in the environment, by the processor; transmitting measurement data of the static object measured to a predetermined information processing apparatus through a communication network; transmitting measurement data of the dynamic object measured to the information processing apparatus through a communication network; receiving support data rem the information processing apparatus based on an environment grasping image that is generated from the measurement data, of the static object and the measurement data of the dynamic object by the information processing apparatus; and presenting the received support data to the environment at predetermined timing, by the processor.

Abstract: An information providing method includes: measuring a static object three-dimensionally in an environment, by a processor; sequentially measuring a position of a dynamic object in the environment, by the processor; transmitting measurement data of the static object measured to a predetermined information processing apparatus through a communication network; transmitting measurement data of the dynamic object measured to the information processing apparatus through a communication network; receiving support data rem the information processing apparatus based on an environment grasping image that is generated from the measurement data, of the static object and the measurement data of the dynamic object by the information processing apparatus; and presenting the received support data to the environment at predetermined timing, by the processor.

Abstract: An information reading system includes a passive-type IC tag that identifies a device installed on side of an environment, a tag reader that reads identification information of the passive-type IC tag, a sensor unit that detects a touch operation performed with respect to the passive-type IC tag, and a reading control unit that, when the sensor unit detects a touch operation, activates the tag reader and controls timing of reading the passive-type IC tag.

Abstract: A content projection apparatus including: a memory, and a processor coupled to the memory and the processor configured to: obtain a range image of a space, detect a plane region in the range image of the space, determine an aspect ratio of each of a plurality of grids, into which the plane region is divided, based on a horizontal-to-vertical ratio of contents to be projected on the space, determine at least one specified grid whose distance from an outside of the plane region is the longest in the plurality of grids, and output information for projecting the contents in a position of one of the at least one specified grid of the space with a specified size that is determined based on the distance.

Abstract: A guidance support method is disclosed. Target information is detected in space information stored in a storage device. A device position of a display device is measured in a space defined in the space information and a target position of a target defined in the target information. An icon position of an icon to display is determined at the display device and a movement of the icon based on the device position and the target position, which are measured. The icon moving as determined at the icon position displayed is displayed at the display device as determined. The icon position and the movement of the icon displayed at the display device is changed depending on a change of the device position.

Abstract: A server device displays a graph indicating time-series variation in load calculated according to a category of an event acquired from schedule information in which events of at least one user have been registered, and displays information indicating a payment object or a body region or symptom related to the payment object extracted from registered payment data of the one user in a manner temporally associated with the graph.

Abstract: An information reading system includes a passive-type IC tag that identifies a device installed on side of an environment, a tag reader that reads identification information of the passive-type IC tag, a sensor unit that detects a touch operation performed with respect to the passive-type IC tag, and a reading control unit that, when the sensor unit detects a touch operation, activates the tag reader and controls timing of reading the passive-type IC tag.