Rapsodo Pte. Ltd.

St-ericsson Sa

Batuhan Okur

Lodiya Radhakrishnan Vijayanand

Boyi Zhang

Kelvin Yeo Soon Keat

Roshan Gopalakrishnan

Saurabh Garg

Evgeny Lipunov

Jin Xu

Nyan Myo Naing

Sayed Zeeshan Asghar

Xi Tao

Kelvin Soon Keat Yeo

Xu Jin

Charles John Razzell

Jerome Tjia

Tue Fatt David Wee

Hong Sair Lim

Osman Murat Teket

Yasar Burak Savak

Sivaprakash Natesan

Aditya Vishwanath

Lodiya Radhakrishnan Viyayanand

Xu Dejiang

Botan Yildirim

Temucin Som

Samuel Whitcomb

Josiah Jia Ern Tan

Burak Savak

A method includes receiving motion data of a user in an environment with respect to a plurality of instances of a first action by the user, determining a kinematic movement based on receiving the motion data, analyzing the kinematic movement using a neural network, obtaining a plurality of outcome types with respect to the first action of the user, correlating the kinematic movement with the at least one indication of the outcome type with respect to the first action, classifying an outcome of the first action as at least one of the plurality of outcome types, determining which of the kinematic movements of the user result in the at least one of the plurality of outcome types, and providing instructions to the user to alter the determined kinematic movements of the user that result in the at least one of the plurality of outcome types.

Kinematic analysis of user form

Sports training device

A method may include capturing image data associated with an object in a defined environment at one or more points in time. The method may include capturing radar data associated with the object in the defined environment at the same points in time. The method may include obtaining, by a machine learning model, the image data and the radar data associated with the object in the defined environment. The method may include pairing each image datum with a corresponding radar datum based on a chronological occurrence of the image data and the radar data. The method may include generating, by the machine learning model, a three-dimensional motion representation associated with the object that is associated with the image data and the radar data.

Self-organized learning of three-dimensional motion data

A system and method for determining a position an object moving along a trajectory crosses a target plane, by obtaining a plurality of images of an object moving along a trajectory in an environment, the plurality of images capturing the object at different positions along the trajectory, obtaining a plurality of images of an object moving along a trajectory in an environment, the plurality of images capturing the object at different positions along the trajectory, the plurality of images being obtained from two or more cameras positioned at different positions in the environment, each of the two or more cameras having a field of view (FOV) that includes the trajectory of the moving object and a target plane, the target plane being a plane that the moving object crosses during movement along the trajectory, each of the two or more cameras obtaining the images at a different timing.

System and method for determining a position an object crosses a target plane

A method may include receiving a group of images taken by a camera over time in an environment, in which the camera may be oriented within the environment to capture images of an object in a substantially same direction as a launch direction of the object, and the group of images including a first image and a second image. The method may further include: identifying a first position of the object in the first image; identifying a second position of the object in the second image; generating a flight vector based on the first position of the object and the second position of the object; and determining one or more flight parameters using the flight vector. Additionally, the method may include: generating a simulated trajectory of the object based on the flight parameters; and providing the simulated trajectory of the object for presentation in a graphical user interface.

Object trajectory simulation

Embodiments are disclosed for a range-gated imager. In some embodiments, a method comprises transmitting, with a multi-tone continuous wave (MTCW) radar, a radar signal comprising a first tone and a second tone, where the first tone and the second tone are separated by a frequency gap; receiving, with the MTCW radar, a return signal from a projectile impinged by the radar signal; detecting, with a measuring apparatus, a zero crossing of a phase difference between the first and second tones; and responsive to detecting the zero crossing, gating or triggering, by the measuring apparatus, an imager to capture an image of the projectile.

Range-gated imager

An example method to determine an object spin rate may include training a neural network with a set of initial data. The set of initial data may be generated based on a plurality of initial radar signals of a plurality of initial objects in motion. The method may include receiving a radar signal of a particular object in motion. The method may include converting the radar signal into an input vector. The input vector may include time and frequency information of the particular object in motion. The method may include providing the input vector as input to a trained neural network. The method may include determining a spin rate of the particular object in motion based on an analysis performed by the trained neural. The analysis may include analyzing the input vector including time and frequency information of the object in motion in view of the set of initial data.

Detection and estimation of spin

A method for calibrating a camera without the decomposition of camera parameters into extrinsic and intrinsic components is provided. Further, there is provided a method for tracking an object in motion comprising capturing one or more image frames of an object in motion, using one or more calibrated cameras that have been calibrated according to a calibration method that generates and uses a respective transformation matrix for mapping three-dimensional (3D) real world model features to corresponding two-dimensional (2D) image features. The tracking method further comprises determining, using a hardware processor, motion characteristics of the object in motion based on the captured one or more image frames from each one or more calibrated cameras, the determining of the motion characteristics based on implicit intrinsic camera parameters and implicit extrinsic camera parameters of the respective transformation matrix from each respective one or more calibrated cameras.

System and method for calibrating a camera and object tracking system using a calibrated camera

A stump device may include a first image-capturing sensor configured to couple to at least one stump of a wicket positioned at a bowling end of a cricket field and capture image data of an initial motion of a cricket ball. The stump device may also include a second image-capturing sensor configured to couple to at least one stump of the wicket and capture image data of a trajectory and a flight path of the cricket ball. The stump device may additionally include a first radar sensor configured to couple to at least one stump of the wicket and capture radar data describing one or more initial launch parameters of the cricket ball. The stump device may include a second radar sensor configured to couple to at least one of the stumps of the wicket and capture radar data describing one or more movement parameters of a bowler.

Stump device for feature estimation of cricket games

A cricket sensor may include one or more first image-capturing sensors configured to capture image data of a pitching motion of a bowler and image data of an initial motion of a cricket ball at a bowling end of a cricket field. The cricket sensor may include one or more second image-capturing sensors configured to capture image data of a trajectory and a flight path of the cricket ball towards a batting end of the cricket field. The cricket sensor may also include one or more first radar sensors configured to capture radar data describing one or more initial launch parameters of the cricket ball related to the trajectory and the flight path of the cricket ball towards the batting end of the cricket field.

Feature estimation of a cricket game

A method may include obtaining sensor data from one or more activity sensors, each of the activity sensors being coupled to a respective area of a sports user. The method may include obtaining image data of the sports user and each of the activity sensors coupled to the sports user. The method may include identifying, by a machine learning module and based on the sensor data and the image data, a respective muscle associated with each respective area to which the activity sensors are coupled. The method may include identifying movement of the sports user based on the sensor data from the activity sensors and the identified body part. The method may include analyzing the identified movement of the sports user including evaluating a body posture of the sports user, identifying one or more movement patterns of the sports user, and/or performing an injury assessment for the sports user.

Intelligent analysis and automatic grouping of activity sensors

An example method to determine an object spin rate may include receiving a radar signal of a particular object in motion. The method may further include converting the radar signal into an input vector. The method may also include providing the input vector as input to a neural network. The neural network may include access to a set of initial data that has been generated based on multiple initial radar signals of multiple initial objects in motion. The method may further include determining a spin rate of the particular object in motion based on an analysis performed by the neural network of the input vector including time and frequency information of the particular object in motion in view of the set of initial data. The analysis may include comparing one or more elements of the input vector to one or more elements of the set of initial data.

A method including detecting an object within a field of view of a radar using a radar signal; tracking movement of the object through the field of view of the radar; triggering a camera to capture a plurality of images of the object based on the movement of the object; detecting the object in the plurality of images; combining data of the radar signal with data of the camera to estimate a position of the object; identifying a radar signal track generated by the motion of the object based on the combined data; and estimating a trajectory of the object based on identifying the radar signal track.

Radar and camera-based data fusion

Systems and methods are disclosed to measure a PPG signal. In some embodiments, a method may include capturing a plurality of frames of a subject; tracking the position of a region of interest of the subject in each of the plurality of frames; creating a first time series signal, a second time series signal, and third time series signal corresponding with respective color channels of the plurality of frames; normalizing the first, second, and third time series signals, combining the normalized first time series signal, the normalized first time series signal, and the normalized first time series signal into a combined signal; creating a spectral signal from the combined signal; and extracting the PPG signal from the spectral signal.

Growing community of inventors

Singapore, Singapore

Batuhan Okur