Patent ID: 11922358
Assignee: VELOCITYEHS HOLDINGS, INC.
Field: Computer technology (Electrical engineering)
Classification: CPC G | IPC G

Claim 16:
17. A non-transitory computer readable medium storing computer executable instructions fora system deployed in a cloud-based communication network, the instructions being configured for:
obtaining, by a processor of a computing device deployed within a cloud-based communication network, video signals from at least one of a passive range sensor or active depth sensor;
receiving, by a computing server system deployed within the cloud-based communication network, the video signals from the computing device;
disassembling the video signals into image frames;
processing each image frame of the image frames using a deep learning model to derive estimate joint locations and corresponding joint angles for each body region in each image frame, wherein the processing comprises:
extracting feature maps from the image frames by processing the image frames through a baseline convolutional neural network (baseline CNN);
generating, by each stage of a multi-stage convolutional neural network (multi-stage CNN), a set of representations of pairwise relationships between body regions, by processing each feature map of the feature maps by the multi-stage CNN, wherein each subsequent stage of the multi-stage CNN uses, for the generating, a previous set of representations from a previous stage of the multi-stage CNN,; and
generating at least one estimate of a relationship between the body regions, by a final stage of the multi-stage CNN, wherein the final stage uses outputs of at least one previous stage of the multi-stage CNN to generate the at least one estimate, wherein the at least one estimate comprises an estimate of at least one of joint angles in the body regions, joint locations in the body regions, or skeletal points in the body regions;

generating, a risk score for at least one category of a plrality of risk categories, by the computing server system, based at least upon the estimate of the joint angles;
generating, by the computing server system, a risk rating for each body region of the body regions based on the risk score for each body region of the body regions in selected risk categories of the plurality of risk categories; and
determining, based on the risk rating, by the computing server system, ergonomic risk root-causes for each body region of the body regions based on the risk rating.