The present disclosure relates to a method and system for recommending optimal ergonomic position for a user of a computing device by a recommendation system. The recommendation system receives user data from one or more data sources and extracts a profile of the user from a repository based on the user data. The recommendation system identifies one or more critical areas of the user, where each of the critical areas are associated with a plurality of pre-defined position parameters and also monitor the plurality of pre-defined position parameters of the user to determine corresponding values. The recommendation system compare the values of the plurality of pre-defined position parameters with predefined values of the pre-defined position parameters and identify deviations in one or more of the plurality of pre-defined position parameters based on the comparison and provide recommendations for correcting the deviations from the pre-defined position parameters to the user.

A system for physiological motion measurement is provided. The system may acquire a reference image corresponding to a reference motion phase of an ROI and a target image of the ROI corresponding to a target motion phase, wherein the reference motion phase may be different from the target motion phase. The system may identify one or more feature points relating to the ROI from the reference image, and determine a motion field of the feature points from the reference motion phase to the target motion phase using a motion prediction model. An input of the motion prediction model may include at least the reference image and the target image. The system may further determine a physiological condition of the ROI based on the motion field.

A metrology method for use in determining one or more parameters of a three-dimensional patterned structure, the method including performing a fitting procedure between measured TEM image data of the patterned structure and simulated TEM image data of the patterned structure, determining a measured Lamellae position of at least one measured TEM image in the TEM image data from a best fit condition between the measured and simulated data, and generating output data indicative of the simulated TEM image data corresponding to the best fit condition to thereby enable determination therefrom of the one or more parameters of the structure.

The disclosed technology includes image-based systems and methods for object tracking within an asset area. Some exemplary methods include receiving an indication of a first object entering an asset area and receiving data indicative of a plurality of captured images. The methods also include performing, by at least one processor, object classification of the first object based on one or more of the plurality of captured images. The methods further include determining a first object location of the first object based at least in part on the object classification, and outputting an indication of the first object location.

A device for detecting the surroundings of a vehicle and a method for detecting the surroundings, and a vehicle designed to carry out said method comprise a camera module, a camera control apparatus, an analysis unit and an illumination device. The illumination device is formed by a matrix headlight of the vehicle and is designed such that it can project a light pattern into the surroundings. The projected light pattern is imaged in the detection region of the camera module and the 3D position of measurement points formed by the light pattern in the surroundings is determined by the analysis unit. However, the illumination device projects the light pattern only into regions of the surroundings in which the analysis unit has ascertained, based on image data, a value that is critical for 3D position determination.

The arrangement is provided for monitoring state and sequence of movement in an aseptic work chamber of a containment standing in an installation room. At least one work glove projects into the work chamber, wherein the respective work glove is able to stretch up to a maximum grasping range in the three spatial axes in the work chamber. The arrangement comprises a tracking system, the recordings of which serve to continuously localize the at least one work glove in three dimensions and are stored in a computer unit. At least one three-dimensional or two-dimensional prohibited region, which may be adjoined by a warning region, is defined in the work chamber. Individual surface sections or the entire floor of the containment can be defined as a prohibited region. A prohibited region and possibly a warning region in front of this can be set up around machines which are installed in the work chamber and which constitute a danger for the operator. The coordinates of prohibited region and warning region are stored in the computer unit. The at least one work glove must not be used to intervene in the prohibited region and should not be used to intervene in the warning region.

This disclosure enables various technologies for analyzing behaviors of objects or with respect to objects based on stereo imageries thereof. For example, such analysis may be useful in enforcement of certain actions by objects or with respect to objects, surveillance of objects or with respect to objects, or other situations involving analyzing behaviors of objects or with respect to objects.

Provided are a device and method that calculate a predicted motion vector corresponding to a type and posture of a tracked subject, and generate a camera control signal necessary for capturing an image of a tracked subject. There are included a predicted subject motion vector calculation unit that detects a tracked subject of a previously designated type from a captured image input from an imaging unit and calculates a predicted motion vector corresponding to a type and posture of the detected tracked subject, and a camera control signal generation unit that generates, on the basis of the predicted motion vector calculated by the predicted subject motion vector calculation unit, a camera control signal for capturing an image of a tracked image of the tracked subject. By using a neural network or the like, the predicted subject motion vector calculation unit executes processing of detecting a tracked subject of a type designated from the captured image by a user, and predicted motion vector calculation processing.

In a motion analysis apparatus 101, a data input unit 205 acquires a first imaging result and a second imaging result. in the motion analysis apparatus 101, a skeleton recognition unit 206 recognizes skeleton positions of a subject using the first imaging. result acquired by the data input unit 205, and recognizes skeleton positions of the subject using the second imaging result acquired by the data input unit 205. A motion period extraction unit 403 extracts a period from a start of a motion to an end of the motion as a range of data for comparing skeleton feature points recognized by the skeleton recognition unit 206. The similarity calculation unit 401 compares skeleton feature points recognized for an input from a depth camera with skeleton feature points recognized for an input from an RGB camera to calculate similarities, and outputs a determination result based on the similarities.

Technologies are provided herein for modeling and tracking physical objects, such as human hands, within a field of view of a depth sensor. A sphere-mesh model of the physical object can be created and used to track the physical object in real-time. The sphere-mesh model comprises an explicit skeletal mesh and an implicit convolution surface generated based on the skeletal mesh. The skeletal mesh parameterizes the convolution surface and distances between points in data frames received from the depth sensor and the sphere-mesh model can be efficiently determined using the skeletal mesh. The sphere-mesh model can be automatically calibrated by dynamically adjusting positions and associated radii of vertices in the skeletal mesh to fit the convolution surface to a particular physical object.