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: Disclosed herein is a system and method for obtaining and generating motion capture datasets relating to various working activities for ergonomic risk assessment. An example system may comprise a computing device that obtains first data from multiple motion capture cameras, obtains second data from multiple visible light imaging sensors, calculates 3D positions of multiple reflective markers positioned on several subjects performing various working activities based on the first data, labels each marker to generate marker trajectories, performs gap filing and smoothing functions on the marker trajectories to generate global marker positions, transforms the global marker positions into a corresponding image coordinate system of each sensor to generate 3D pose data of the subjects at each sensor viewpoint, projects the 3D pose data into frames of the second data to generate 2D pose data, and generates a dataset comprising the second data, the 2D pose data, and the 3D pose data.

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: Disclosed herein is a system and method for obtaining and generating motion capture datasets relating to various working activities for ergonomic risk assessment. An example system may comprise a computing device that obtains first data from multiple motion capture cameras, obtains second data from multiple visible light imaging sensors, calculates 3D positions of multiple reflective markers positioned on several subjects performing various working activities based on the first data, labels each marker to generate marker trajectories, performs gap filing and smoothing functions on the marker trajectories to generate global marker positions, transforms the global marker positions into a corresponding image coordinate system of each sensor to generate 3D pose data of the subjects at each sensor viewpoint, projects the 3D pose data into frames of the second data to generate 2D pose data, and generates a dataset comprising the second data, the 2D pose data, and the 3D pose data.

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: 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 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: 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.