US 11,670,323 B2
Systems and methods for detecting impairment of an individual
Rahul Kushwah, Toronto (CA); Sheldon Kales, Toronto (CA); Nandan Mishra, Noida (IN); Himanshu Ujjawal Singh, Malviya Nagar (IN); and Saurabh Gupta, Jalaun (IN)
Assigned to PredictMedix Inc., Toronto (CA)
Filed by PredictMedix Inc., Toronto (CA)
Filed on Jun. 4, 2020, as Appl. No. 16/892,369.
Claims priority of provisional application 62/858,422, filed on Jun. 7, 2019.
Prior Publication US 2020/0387696 A1, Dec. 10, 2020
Int. Cl. G06V 40/20 (2022.01); G06T 7/00 (2017.01); G10L 25/48 (2013.01)
CPC G06V 40/20 (2022.01) [G06T 7/0002 (2013.01); G06T 7/97 (2017.01); G10L 25/48 (2013.01)] 14 Claims
OG exemplary drawing
 
1. A method for detecting impairment of an individual, the method comprising operating a processor to:
receive at least two images associated with the individual, wherein a first image depicts a first portion of the individual and a second image depicts a second portion of the individual, the second portion of the individual being different from the first portion of the individual, and the first image being different from the second image;
identify at least one feature in each image of the at least two images, wherein the first image comprises a first feature type and the second image comprises a second feature type, the second feature type being different from the first feature type;
for each feature identified in each image:
generate an intensity representation for a region of that image associated with the feature, the intensity representation corresponding to a frequency distribution of pixel intensities for pixels in the region and each pixel in the region is associated with a heat intensity of the feature;
select at least one impairment analytical model related to the feature;
apply the at least one impairment analytical model to the intensity representation to determine a respective at least one impairment likelihood;
determine a feature reliability indicator for the feature, the feature reliability indicator representing a level of reliability of that feature for determining impairment of the individual;
determine a confidence level for each of the at least one impairment likelihood based on the feature reliability indicator and characteristics associated with the corresponding at least one impairment analytical model and that feature; and
define the impairment of the individual based on the at least one impairment likelihood and the respective confidence level generated from the at least one feature in each image of the at least two images.