Abstract:
Disclosed are a personalized exercise service providing method and an apparatus thereof. An personalized exercise service providing apparatus according to an example includes a user class identifier configured to identify user class of a user; an exercise data acquisitor configured to acquire exercise data of the user; and a model generator configured to generate a personalized exercise model of the user based on a standard exercise model corresponding to the determined user class and the acquired exercise data of the user to provide the most effective exercise model to each user through user recognition.
Abstract:
An apparatus and method for detecting an object using a multi-directional integral image are disclosed. The apparatus includes an area segmentation unit, an integral image calculation unit, and an object detection unit. The area segmentation unit places windows having a size of x*y on a full image having w*h pixels so that they overlap each other at their edges, thereby segmenting the full image into a single area, a double area and a quadruple area. The integral image calculation unit calculates a single directional integral image for the single area, and calculates multi-directional integral images for the double and quadruple areas. The object detection unit detects an object for the full image using the single directional integral image and the multi-directional integral images.
Abstract:
Disclosed herein are an apparatus and method for providing crosswalk pedestrian guidance based on an image and a beacon. The method for providing crosswalk pedestrian guidance based on an image and a beacon may include estimating a walking location based on a beacon signal corresponding to at least one traffic light and first-person view sensor information, analyzing a hazard factor around a pedestrian based on an image acquired from a camera corresponding to the traffic light, predicting a hazard around the pedestrian in combination by considering together the walking location, the hazard factor, and status information of the traffic light, and providing walking guidance to a pedestrian guidance terminal based on the predicted hazard around the pedestrian.
Abstract:
Disclosed herein are an apparatus for writing a motion script and an apparatus and method for self-teaching of a motion. The method for self-teaching of a motion, in which the apparatus for writing a motion script and the apparatus for self-teaching of a motion are used, includes creating, by the apparatus for writing a motion script, a motion script based on expert motion of a first user; analyzing, by the apparatus for self-teaching of a motion, a motion of a second user, who learns the expert motion, based on the motion script; and outputting, by the apparatus for self-teaching of a motion, a result of analysis of the motion of the second user.
Abstract:
Disclosed herein are an interaction apparatus and method. The interaction apparatus includes an input unit for receiving multimodal information including an image and a voice of a target to allow the interaction apparatus to interact with the target, a recognition unit for recognizing turn-taking behavior of the target using the multimodal information, and an execution unit for taking an activity for interacting with the target based on results of recognition of the turn-taking behavior.
Abstract:
Disclosed herein are an apparatus for writing a motion script and an apparatus and method for self-teaching of a motion. The method for self-teaching of a motion, in which the apparatus for writing a motion script and the apparatus for self-teaching of a motion are used, includes creating, by the apparatus for writing a motion script, a motion script based on expert motion of a first user; analyzing, by the apparatus for self-teaching of a motion, a motion of a second user, who learns the expert motion, based on the motion script; and outputting, by the apparatus for self-teaching of a motion, a result of analysis of the motion of the second user.
Abstract:
Disclosed herein is an apparatus and method for counting repetitive movements based on Artificial Intelligence. The method may include generating standard movement information that includes a key pose extracted from a demonstration movement image stream based on human skeleton information, which is a set of pieces of positional information of human joints, and major joint information in the key pose and counting repetitive movements depending on whether a user movement matches the standard movement information based on human skeleton information of a user movement image stream.
Abstract:
Disclosed herein are a cloud server, an edge server, and a method for generating an intelligence model using the same. The method for generating an intelligence model includes receiving, by the edge server, an intelligence model generation request from a user terminal, generating an intelligence model corresponding to the intelligence model generation request, and adjusting the generated intelligence model.
Abstract:
Disclosed herein are an apparatus and method for recommending federated learning based on recognition model tendency analysis. The method for recommending federated learning based on recognition model tendency analysis in a server device may include analyzing the tendency of a recognition model trained using reinforcement learning by each of multiple user terminals, grouping the multiple user terminals according to the tendency of the recognition model, and transmitting federated-learning group information including information about other user terminals grouped together with at least one of the multiple user terminals.
Abstract:
Disclosed herein are an apparatus and method for evaluating a human motion using a mobile robot. The method may include identifying the exercise motion of a user by analyzing an image of the entire body of the user captured using a camera installed in the mobile robot, evaluating the pose of the user by comparing the standard pose of the identified exercise motion with images of the entire body of the user captured by the camera of the mobile robot from two or more target locations, and comprehensively evaluating the exercise motion of the user based on the pose evaluation information of the user from each of the two or more target locations.