Abstract: Disclosed herein is an image-based system configured to identify root causes of industrial ergonomic risks and their corresponding solutions. An example system comprises a computing device configured to encode an image of a worker performing a work task to generate an embedding vector, transmit the embedding vector to an image-grounded text decoder, while generating first tokens to instruct the decoder to generate a first sentence indicating a root cause of an ergonomic risk identified in the image, compute first relative sensitivity scores relating to the first tokens and extracted image features, generate second tokens of the first sentence based on the first relative sensitivity scores, while generating third tokens to instruct a text decoder to generate a second sentence indicating a solution to the ergonomic risk, calculate second relative sensitivity scores relating to the second and third tokens, and generate the first and second sentences accordingly.

Abstract: A system is disclosed for identifying industrial ergonomics risk root-causes and providing risk control actions. The system includes a processor configured to execute machine readable instructions to receive, from a computing device of a user, video of an individual performing a task and receive, from the computing device, textual information related to the task. The processor is also configured to process the textual information and the video of the individual performing the task through a deep learning framework to determine ergonomic risk root-causes. The processor is further configured to determine ergonomic risk control recommendations to mitigate the determined ergonomic risk root-causes. The processor transmits, to the computing device, information indicative of the ergonomic risk control recommendations and the ergonomic risk root-causes.

Abstract: Computing systems and methods are configured to structure unstructured document data. An example computing system is configured to receive an input in the form of a document containing unstructured data, determine a plurality of entities to be identified and retrieved from the input, analyze each entity to determine identification and retrieval computational techniques, group the plurality of entities into different categories based on the identification and retrieval computational techniques, generate multiple images of the input in different dot per inches, extract machine-readable texts from the multiple images of the input, conduct a text extraction for native PDF in the input, apply the identification and retrieval computational techniques to identify and retrieve the plurality of entities in different categories based at least upon the machine-readable texts extracted from the multiple images of the input and the native PDF in the input, and save processing results in at least one structured database.

Abstract: A system for identifying industrial ergonomics risk root-causes and providing risk control actions, comprising: a computing device configured to obtain video signals of a worker performing a job at a workplace; and a computing server system configured to receive and process the video signals to determine joint locations of the worker, calculate joint angles for each body region of the worker based on the joint locations, calculate, based on the joint angles, a risk score for each body region of the worker in a plurality of risk categories, calculate a risk rating for each body region of the worker based on the risk score, determine ergonomic risk root-causes for each body region of the worker based on the risk rating, and provide ergonomic risk control recommendations to mitigate the ergonomic risk root-causes.

Abstract: A system, comprising: a computing device configured to obtain video signals of a worker performing a hand-related job at a workplace; and a computing server system configured to receive and process the video signals to identify hand grips and wrist bending involved in the job, determine a hand grip type for each identified hand grip, obtain force information relating to each identified hand grip, determine neutral or hazardous wrist bending based at least upon the wrist bending and the hand grip force information, calculate a percent maximum strength for each identified hand grip, calculate frequencies and durations of each identified hand grip and wrist bending, and determine ergonomic risks of the hand-related job accordingly.

Abstract: Systems and methods to classify incident report documents are disclosed, comprising inputting, a first type data entry of a document into a deep neural network (DNN); encoding, via the DNN, the first type data entry to output a densely embedded contextual vector representing contents of the first type data entry; generating, a list containing ordered data from a second type data entry of the document; encoding, via a machine learning network, the ordered data into a sparse vector representation of the second type data entry; concatenating, the densely embedded contextual vector with the sparse vector representation to generate a representative vector of the document; and training a gradient-boosted classifier network by using as training inputs the representative vector and a label associated with the document to generate a classification of the document.

Abstract: Systems and Methods are disclosed herein for automatically indexing multiple informational fields in digital data records, the method comprising: identifying, based on rules defining target information fields, for each target field of the target information fields, at least one page in a digital data record comprising content related to the target field; extracting, for each target field, from the identified at least one page, at least one portion of text comprising the content; feeding, for each target field, a pre-processed version of the at least one portion of text into a machine learning (ML) model, wherein the ML model is trained on the target field; determining, for each target field, via the ML model trained on the target field, at least one candidate text comprising the content; and extracting, for each target field, the at least one candidate text.

Abstract: A computing device of a system is deployed within a communication network for processing environmental, social, and governance (ESG) reports. The computing device is configured to obtain an ESG report of an entity, receive a question in natural language regarding information contained in or inferable from the ESG report, convert the ESG report into page-wise pieces of text, determine an answer to the question based at least upon the page-wise pieces of text of the ESG report, determine a location of a snippet related to the answer in the page-wise pieces of text of the ESG report, generate a bounding box through which the snippet is highlighted in a page containing the snippet, crop the page based on the bounding box, and display a cropped image of the page.

Abstract: A method for determining a hand force and a ground reaction force for a musculoskeletal body of a subject includes obtaining video data for the musculoskeletal body during an action taken by the subject, generating, for each frame of the video data, three-dimensional pose data for the subject based on a three-dimensional skeletal model, and determining the hand force and the ground reaction force based on the three-dimensional pose data. Determining the hand force and the ground reaction force includes implementing a reconstruction of the hand force and the ground reaction force based on the three-dimensional pose data. The method additionally includes applying the three-dimensional pose data, the estimate for the ground reaction force, and the estimate of the hand force, to a neural network or other model to optimize the estimate of the hand force and the estimate of the ground reaction force.

Abstract: A system for identifying industrial ergonomics risk root-causes and providing risk control actions, comprising: a computing device configured to obtain video signals of a worker performing a job at a workplace; and a computing server system configured to receive and process the video signals to determine joint locations of the worker, calculate joint angles for each body region of the worker based on the joint locations, calculate, based on the joint angles, a risk score for each body region of the worker in a plurality of risk categories, calculate a risk rating for each body region of the worker based on the risk score, determine ergonomic risk root-causes for each body region of the worker based on the risk rating, and provide ergonomic risk control recommendations to mitigate the ergonomic risk root-causes.

Abstract: Systems and methods for automated indexing and extraction of information in digital documents are disclosed. A method may comprise identifying a page containing targeted information; inputting an image of the page into a visual machine learning network (visual ML), wherein the visual ML is trained to recognize text associated with the targeted information in an image; identifying by the visual ML, a section of the image that contains the targeted information; inputting the digital document, and coordinates of the section into an extraction module; and extracting the targeted information by the extraction module from the section.

Abstract: Systems and Methods are disclosed herein for automatically indexing multiple informational fields in digital data records, the method comprising: identifying, based on rules defining target information fields, for each target field of the target information fields, at least one page in a digital data record comprising content related to the target field; extracting, for each target field, from the identified at least one page, at least one portion of text comprising the content; feeding, for each target field, a pre-processed version of the at least one portion of text into a machine learning (ML) model, wherein the ML model is trained on the target field; determining, for each target field, via the ML model trained on the target field, at least one candidate text comprising the content; and extracting, for each target field, the at least one candidate text.

Abstract: A system for identifying industrial ergonomics risk root-causes and providing risk control actions, comprising: a computing device configured to obtain textual information describing a series of tasks of a job and forces being exerted during the series of tasks; and a computing server system configured to receive and process the textual information to generate a set of textual entry to correspond to a unique identifier of the job, identify nouns and verbs in the set of textual entry via natural language processing techniques, perform dependency parsing and part-of-speech tagging to associate each identified verb in the set of textual entry with a root noun in order to identify action-object pairs and unpaired actions, determine ergonomic risk root-causes based at least upon the action-object pairs and the unpaired actions in the set of textual entry, and provide ergonomic risk control recommendations to mitigate the ergonomic risk root-causes.

Abstract: Systems and methods for automated indexing and extraction of information in digital documents are disclosed. A method may comprise selecting a page number of a digital document to identify a page containing targeted information; inputting an image of the page into a visual machine learning network (visual ML), wherein the visual ML is trained to recognize text associated with the targeted information in an image; identifying by the visual ML, a section of the image that contains the targeted information; inputting the page number, the digital document, and coordinates of the section into an extraction module; and extracting the targeted information by the extraction module from the section.