The present disclosure is directed to a non-intrusive, integrated system comprising an umpire bot for automatically monitoring, umpiring, scoring, analytics, learning and coaching for players while eliminating need for human umpires and scorers. The automated umpire bot with intelligent telescopic function monitors, cognitively recognizes and captures movements from all equipment's, analyses them, moves up and down and even avoid ball collision travelling towards it. The non-intrusive real time system captures all the game moments right from players initiation, toss of coin, commencement of game, monitoring field positions, keeping scores, umpiring decisions, overs, valid/in-valid deliveries, validating balls per over, wickets, catches, boundaries, sixes and displaying scores and statistics all throughout the game.

A processing system having at least one processor may detect a first object in a first video of a first user and detect a second object in a second video of a second user, where the first video and the second video are part of a visual communication session between the first user and the second user. The processing system may further detect a first action in the first video relative to the first object, detect a second action in the second video relative to the second object, detect a difference between the first action and the second action, and provide a notification indicative of the difference.

Implementations generally perform robust multi-view multi-target action recognition using reconstructed 3-dimensional (3D) poses. In some implementations, a method includes obtaining a plurality of videos of a plurality of subjects in an environment, where at least one target subject of the plurality of subjects performs one or more actions in the environment. The method further includes tracking the at least one target subject across at least two cameras. The method further includes reconstructing a 3-dimensional (3D) model of the at least one target subject based on the plurality of videos and the tracking of the at least one target subject. The method further includes recognizing the one or more actions of the at least one target subject based on the reconstructing of the 3D model.

An exercise method and an exercise equipment are provided. The exercise method includes: determining an exercise guiding video according to a selected music input/audio signal, wherein the exercise guiding video includes a first exercise guiding video and/or a second exercise guiding video, the first exercise guiding video is a live video automatically generated according to the selected music input/audio signal, the second exercise guiding video is a video previously recorded according to the selected music input/audio signal; generating CGA and special-effect/animated feedbacks corresponding to the music information/audio signal and instruction/cuing in the exercise guiding video; playing the exercise guiding video, the CGA, the special-effect/animated feedbacks and the selected music input/audio signal on a display and computing device; receiving user performance data; displaying interactive feedback data on the display and computing device, according to a result obtained by matching the user performance data with music information/audio signal analyzed from selected music input/audio signal.

Disclosed herein are an apparatus and a method for estimating the behavior of a user based on an image converted from sensing data. The apparatus for estimating the behavior of a user based on an image converted from sensing data includes memory for storing at least one program, and a processor for executing the program, wherein the program performs acquiring sensing data measured by one or more behavior measurement devices worn by the user, converting sensing data of the user obtained for a predetermined time period into images, and estimating the behavior of the user from the images of the user based on a pre-trained model.

A method including providing a sensor device including one or several sensors. The sensor device is arranged to perform at least one high-power type measurement and at least one low-power type measurement and includes at least one image sensor arranged to depict a person by a measurement of said high-power type. Each of the low-power type measurements over time requires less electric power for operation as compared to each of the high-power type measurements. The method includes detecting a potential presence of the person using at least one of said low-power type measurements. The method includes producing, using one of the high-power type measurements, an image depicting a person and detecting a presence of the person based on im-age analysis of the image. The method includes detecting, using at least one of the low-power type measurements, a maintained presence of the person. The method includes failing to detect a maintained presence of the person.

A sports training aid comprising a body unit, attachable to a person's body or the person's sports implement, is provided with a positioning sensor module; a feedback stimulator; and a processor. The sports training aid is configured to provide instantaneous feedback on motion faults of a studied sports motion, and the body unit is intended to be attached to a person's body (or a person's sports implement) at a representative location, the location being bound to travel a path representative of the studied sports motion, and the positioning sensor module comprises acceleration sensors and gyro sensors. The processor is configured to determine a still position corresponding to an event wherein the body unit is determined to be still, to keep track of the sensor module's movements relative to the still position, and to activate the feedback stimulator in real time, upon detection of a sports motion fault.

A system that measures a swing of equipment (such as a bat or golf club) with inertial sensors, and analyzes sensor data to create a rotational profile. Swing analysis may use a two-lever model, with a body lever from the center of rotation to the hands, and an equipment lever from the hands to the sweet spot of the equipment. The rotational profile may include graphs of rates of change of the angle of the body lever and of the relative angle between the body lever and the equipment lever, and a graph of the centripetal acceleration of the equipment. These three graphs may provide insight into players' relative performance. The timing and sequencing of swing stages may be analyzed by partitioning the swing into four phases: load, accelerate, peak, and transfer. Swing metrics may be calculated from the centripetal acceleration curve and the equipment/body rotation rate curves.

A method, computer program product, and computing system for proactive encounter scanning is executed on a computing device and includes obtaining encounter information of a patient encounter. The encounter information is proactively processed to determine if the encounter information is indicative of one or more medical conditions and to generate one or more result set. The one or more result sets are provided to the user.

A model learning device provided with: an error-added movement locus generation unit for adding an error to movement locus data for action learning that represents the movement locus of a subject and to which is assigned an action label that is information representing the action of the subject, and thereby generating error-added movement locus data; and an action recognition model learning unit for learning a model, using at least the error-added movement locus data and learning data created on the basis of the action label, by which model the action of some subject can be recognized from the movement locus of the subject. Thus, it is possible to provide a model by which the action of a subject can be recognized with high accuracy on the basis of the movement locus of the subject estimated using a camera image.