US 12,111,982 B2
Systems and methods for real-time tracking of trajectories using motion sensors
Vivek Chandel, Noida (IN); and Avik Ghose, Kolkata (IN)
Assigned to TATA CONSULTANCY SERVICES LIMITED, Mumbai (IN)
Filed by Tata Consultancy Services Limited, Mumbai (IN)
Filed on Sep. 12, 2023, as Appl. No. 18/465,409.
Claims priority of application No. 202221063147 (IN), filed on Nov. 4, 2022.
Prior Publication US 2024/0160297 A1, May 16, 2024
Int. Cl. G06F 3/0346 (2013.01); G06F 1/16 (2006.01); G06F 3/01 (2006.01)
CPC G06F 3/0346 (2013.01) [G06F 1/163 (2013.01); G06F 3/014 (2013.01)] 15 Claims
OG exemplary drawing
 
1. A processor implemented method, comprising:
acquiring, via one or more hardware processors, a first sensor data and a second sensor data from a first sensor and a second sensor respectively, wherein the first sensor data and the second sensor data are associated with a user;
converting, via the one or more hardware processors, the first sensor data and the second sensor data into a first set of scaled sensor data and a second set of scaled sensor data;
calculating, via the one or more hardware processors, a delta value in a plurality of axis for each sensor value from the second sensor data based on a duration between a current sensor value and a previous sensor value;
generating, via the one or more hardware processors, a Euler Rodriguez matrix based on the calculated delta value in the plurality of axis for each sensor value from the second sensor data;
generating, via the one or more hardware processors, a plurality of feature vectors based on the first set of scaled sensor data, the second set of scaled sensor data, and the Euler Rodriguez matrix;
applying, via the one or more hardware processors, a windowing technique comprising a pre-defined window size on each of the plurality of feature vectors to obtain a plurality of windows, wherein each window amongst the plurality of windows comprises one or more associated feature vectors amongst the plurality of feature vectors; and
training, via the one or more hardware processors, one or more machine learning (ML) models based on a label annotated to each window amongst the plurality of windows, wherein the label indicates a stroke direction of data comprised in each window.