The Ergonomic Association between Shoulder, Neck/Head Disorders and Sedentary Activity: A Systematic Review.

Background: Work-associated upper limb and neck disorders are common occupational disorders throughout the world. These disorders are usually observed more in workers who spend a long time sitting, referred to as sedentary activity (SA). The immediate and distorted risk of sedentary-related problems was considered high in Europe, Australia, and the United States. Even though mediation is convenient, it is likely to reduce office workers' risks of developing cervical and upper body pain due to sedentary work. This systematic review addresses risk factors and evaluates the relationship between SA and upper body disorders in office workers (i.e., shoulder and neck/head). Methods: PubMed, Scopus, and Web of Science were searched for articles published between January 2010 and August 2021 in the English language. The three keywords "sedentary," "upper body elements," and "work" (and their derivatives) were searched to identify studies and carry out this systematic review. The articles were searched so that all three keywords or at least a derivation of each keyword should appear. Findings. Of the 40 articles that met the enclosure criteria, 32 studies examined the association of SA and upper body elements during both office and computer work. However, three articles were evaluated in the sit-stand work environment, and in the remaining five studies, one was evaluated during teaching, two during hospital work, and two during mixed working conditions. Conclusions: Research related to SA focuses mainly on extended risk factors, but there was no focus on other aspects, such as muscle and tendon contractions. As there is a convincing connection between SA and the upper body, our close examination identifies the need to institutionalize a system for collecting, analyzing, and describing the impact and short-term effects of SA on the upper body. Additionally, some suggestions were made to minimize the risk in a sedentary working environment.

Prediction framework for upper body sedentary working behaviour by using deep learning and machine learning techniques.

Public health experts and healthcare professionals are gradually identifying sedentary activity as a population-wide, pervasive health risk. The purpose of this paper is to propose a method to identify the changes in posture during sedentary work and to give feedback by analysing the identified posture of the upper body, i.e. hands, shoulder, and head positioning. After capturing the image of the human pose, pre-processing of the image takes place with a bandpass filter, which helps to reduce the noise and morphological operation, which is used to carry out the process of dilation, erosion and opening of an image. To predict the results easily with the use of texture feature extraction, it helps to extract the image's feature. Then, accuracy is predicted by using the deep neural network techniques, to predict the result accurately. After prediction and analysis, the feedback system is developed to alert individuals through the alarm system. The proposed method is formulated by using DNN for prediction in the MATLAB software tool. The results show accuracy, sensitivity and specificity of the prediction using a deep neural network are 97.2%, 88.7% and 99.1%. The proposed method is compared with the existing methods SVM, Random Forest and KNN algorithms. The accuracy, sensitivity and specificity of the existing algorithms are SVM with 77.6%, 57.4 and 97.8%; Random Forest with 80.6%, 63.7% and 97.5%; and KNN with 65.8%, 61.2%, and 95.1%. This concept helps to prevent the impact of sedentary activity on fatal and non-fatal cardiovascular and musculoskeletal diseases, respectively.