KR-20260062132-A - ELECTRONIC DEVICE PREDICTING MUSCLE STRENGTH CONSIDERED THE CHARACTERISTICS OF EACH WORKER BASED ON ARTIFICIAL INTELLIGENCE

KR20260062132AKR 20260062132 AKR20260062132 AKR 20260062132AKR-20260062132-A

Abstract

An electronic device for predicting a worker's back muscle strength is disclosed. An electronic device according to the present disclosure includes a memory comprising an artificial intelligence model trained to output a predicted value for back muscle strength, and a processor that inputs state information comprising at least one of the subject's gender, forearm posture, and lifting height into the artificial intelligence model to obtain a predicted value for back muscle strength of the subject.

Inventors

이진원
주용완

Assignees

국립강릉원주대학교산학협력단

Dates

Publication Date: 20260507
Application Date: 20241025

Claims (3)

In an electronic device for predicting a worker's back muscle strength, Memory containing an artificial intelligence model trained to output predicted values for back muscle strength levels; and An electronic device comprising a processor that inputs state information, including at least one of the subject's gender, height, forearm posture, and lifting height, into the artificial intelligence model to obtain a predicted value of the subject's back muscle strength.
In claim 1, The above processor is, An electronic device that identifies an optimal workload and an optimal work environment for a subject based on the subject's status information and the subject's predicted value.
In claim 1, The above artificial intelligence model is, It includes a first artificial intelligence model based on a linear regression algorithm, a second artificial intelligence model based on a random forest algorithm, and a third artificial intelligence model based on a multilayer perceptron (MLP), and The above processor is, An electronic device that obtains a predicted value of a subject by performing an ensemble on a first output obtained by inputting state information into the first artificial intelligence model, a second output obtained by inputting state information into the second artificial intelligence model, and a third output obtained by inputting state information into the third artificial intelligence model.

Description

ELECTRONIC DEVICE PREDICTING MUSCLE STRENGTH CONSIDERED THE CHARACTERISTICS OF EACH WORKER BASED ON ARTIFICIAL INTELLIGENCE } The present disclosure relates to an electronic device, and more specifically, to an electronic device that predicts the back muscle strength of each worker based on worker-specific status information such as the worker's posture, height, gender, and position of lifting objects, and identifies an optimized workload and work environment according to the back muscle strength of each worker. Musculoskeletal disorders are common in various industries, including manufacturing, construction, services, nursing, and agriculture. According to a recent analysis by the Global Burden of Disease (GBD) study, approximately 1.71 billion people worldwide are affected by musculoskeletal disorders. These disorders are a major cause of years of health loss, which refers to the loss of healthy years due to disease and disability globally, and are known to account for approximately 149 million years, or 17% of the global total. Manual material handling (MMH) is a core component of manufacturing and service operations and often requires the direct physical input of workers. Such frequent and severe MMH is one of the major risk factors for occupational back pain. This type of activity is common among professionals such as miners, construction workers, baggage handlers, carpenters, nurses, and agricultural workers, and various risk factors associated with MMH, including the weight of objects, excessive force, awkward postures (e.g., asymmetry, hunched posture, kneeling posture), repetitiveness, and spatial placement (e.g., positioning of weights), are identified as major causes of musculoskeletal disorders. In particular, since back muscle strength is influenced by various factors during lifting tasks, it is essential to optimize strength and identify the factors affecting lower back pain. Identifying how back muscle strength changes in relation to gender, age, height, initial lifting height, and hand position can be effectively used to provide information for designing methods to prevent injuries in occupational settings and for personalized rehabilitation programs. Accordingly, there is a need for technology that predicts back muscle strength by considering individual characteristics in lifting movements, such as gender, posture, and the height at which the worker lifts the object. FIG. 1 is a drawing for explaining the configuration of an electronic device according to one embodiment of the present disclosure. FIG. 2 is a flowchart for explaining the operation of a processor according to one embodiment of the present disclosure. FIGS. 3 to 12 are drawings showing the mutual influence between various state information of a subject and the resulting influence on back muscle strength, and drawings showing a comparison between artificial intelligence models of various algorithms for effectively predicting back muscle strength values. Before specifically describing the present disclosure, the method of description in the specification and drawings is described. First, the terms used in this specification and claims have been selected based on general terms considering their functions in the various embodiments of this disclosure. However, these terms may vary depending on the intent of those skilled in the art, legal or technical interpretations, and the emergence of new technologies. Additionally, some terms have been arbitrarily selected by the applicant. Such terms may be interpreted according to the meanings defined in this specification; in the absence of specific definitions, they may be interpreted based on the overall content of this specification and common technical knowledge in the relevant field. In addition, the same reference numbers or symbols described in each drawing attached to this specification represent parts or components that perform substantially the same function. For convenience of explanation and understanding, the same reference numbers or symbols are used to describe different embodiments. That is, even if components having the same reference number are all depicted in multiple drawings, the multiple drawings do not imply a single embodiment. Additionally, in this specification and claims, terms including ordinal numbers, such as "first," "second," etc., may be used to distinguish between components. These ordinal numbers are used to distinguish identical or similar components from one another, and the meaning of the terms should not be limited by the use of such ordinal numbers. For example, the order of use or arrangement of components combined with such ordinal numbers should not be restricted by the number. If necessary, each ordinal number may be used interchangeably. In this specification, singular expressions include plural expressions unless the context clearly indicates otherwise. In this application, terms such as "comprising" or "consisting of" are intended to specify t