SPECTRE: a deep learning network for posture recognition in manufacturing

Ciccarelli, Marianna; Corradini, Flavio; Germani, Michele; Menchi, Giacomo; Mostarda, Leonardo; Papetti, Alessandra; Piangerelli, Marco

doi:10.1007/s10845-022-02014-y

Cited by 15 publications

(12 citation statements)

References 40 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Currently, work-related musculoskeletal disorders (WRMSDs) are the most prevalent occupational health problem worldwide [ 1 ], and they are also the most common in the European Union (EU) [ 2 , 3 ], affecting three out of five workers [ 4 ]. Indeed, it is considered a public health problem [ 5 ] with multifactorial causes [ 6 , 7 ], resulting from the complex interaction between individual, biomechanical, organisational and psychosocial risk factors [ 6 , 8 ].…”

Section: Introductionmentioning

confidence: 99%

“…WRMSDs affect individuals in all aspects of their lives—as well as companies, society and the economy [ 2 , 9 , 10 ]—as they result in higher healthcare costs, reduced productivity, increased absenteeism, lower job satisfaction, and reduced physical, psychological, and social well-being of workers [ 7 , 11 ]. In fact, some disorders have a lasting impact on the worker’s life, limiting them in their daily activities and preventing their return to work due to permanent disability caused by pain and decreased functionality [ 5 ].…”

Section: Introductionmentioning

confidence: 99%

“…Prolonged inappropriate working postures, tasks with high physical demands, repetitive and meticulous work gestures, and an intense work pace with few breaks have been reported as the main causes of WRMSDs [ 3 , 8 , 9 ]. Additionally, the demographic change in the workforce [ 12 ] has led to an increase in the number of older workers, which may have also contributed to the increase in WRMSDs [ 1 , 2 ].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Wearables for Monitoring and Postural Feedback in the Work Context: A Scoping Review

Figueira,

Silva,

Costa

et al. 2024

Sensors

View full text Add to dashboard Cite

Wearables offer a promising solution for simultaneous posture monitoring and/or corrective feedback. The main objective was to identify, synthesise, and characterise the wearables used in the workplace to monitor and postural feedback to workers. The PRISMA-ScR guidelines were followed. Studies were included between 1 January 2000 and 22 March 2023 in Spanish, French, English, and Portuguese without geographical restriction. The databases selected for the research were PubMed®, Web of Science®, Scopus®, and Google Scholar®. Qualitative studies, theses, reviews, and meta-analyses were excluded. Twelve studies were included, involving a total of 304 workers, mostly health professionals (n = 8). The remaining studies covered workers in the industry (n = 2), in the construction (n = 1), and welders (n = 1). For assessment purposes, most studies used one (n = 5) or two sensors (n = 5) characterised as accelerometers (n = 7), sixaxial (n = 2) or nonaxialinertial measurement units (n = 3). The most common source of feedback was the sensor itself (n = 6) or smartphones (n = 4). Haptic feedback was the most prevalent (n = 6), followed by auditory (n = 5) and visual (n = 3). Most studies employed prototype wearables emphasising kinematic variables of human movement. Healthcare professionals were the primary focus of the study along with haptic feedback that proved to be the most common and effective method for correcting posture during work activities.

show abstract

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Wearables for Monitoring and Postural Feedback in the Work Context: A Scoping Review

Figueira,

Silva,

Costa

et al. 2024

Sensors

View full text Add to dashboard Cite

show abstract

“…Solutions search processes are the most popular and successfully implemented in automated production. Visual control by computer vision has great potential for creating a unified digital industry in diferent field: metallurgy, automotive industry, mechanical engineering, machine tool industry [1][2][3][4][5][6][7]. Detection of defects on the surface at the initial stages of production allows you to reduce costs and save time during production [8][9][10][11][12].…”

Section: Introductionmentioning

confidence: 99%

An approach for recognizing surface defects in products with a complex helical shape

Pivkin,

Yazev,

Grechishnikov

et al. 2023

Optoelectronic Imaging and Multimedia Technology X

View full text Add to dashboard Cite

A key functional role is served by the helical surfaces of carbide end mills that can be manufactured during diamond grinding wheel. Localized changes in the form of the helical surface can be caused by abrasion, high pressure, and grinding wheel wear. Therefore, it is extremely important to measure the physical samples of products with a helical surface according to the criterion of profile accuracy, rake angle and core diameter. A specialized inspection machine in reflected light can be used to obtain images across the helical groove. Manually extracting a number of defects from photos takes time. Using defect recognition algorithms, effective and quick quality control of a ground helical surface can be established. As a result, effective surface quality control can be achieved in the machine tool industry. In this study, an innovative approach to determine a defect's shape and location as well as an algorithm for removing it are presented. Both of these approaches are integrated into the technological process used to manufacture products with helical surfaces. With the goal to recognized create suggestions for image analysis using different image levels, the suggested approach provides logically smoothing histograms and limiting contrast as an image pre-processing, based on an analysis of images with useful and faulty parts. Achieved successful extraction of areas of adhesive, diffusion, abrasion and chips from the image through post-processing. The article presents a new approach to recognizing adhesive and diffusion defects on the helical surface of a mill after grinding. When developing this approach, it was revealed that areas with alternating profile changes are most susceptible to the formation of defects under conditions of increased heating of the working area, and specialized inductors for searching for defects in localized areas according to the criterion of pixel brightness intensity were proposed.

show abstract

“…Over the past few years, detecting focal zones in images has generated sustained interest among the engineering design and computer vision community. Many vision-based methods have been proposed using features [40], models [41][42][43][44], and general image processing framework. The applied experimental approaches in engineering areas still remains a specialized task due to the certain specifics of each task.…”

Section: Introductionmentioning

confidence: 99%

New indicators and standards for measuring of the end mill's helical groove by image processing

Pivkin,

Ershov,

Grechishnikov

et al. 2023

Optical Metrology and Inspection for Industrial Applications X

View full text Add to dashboard Cite

The studies will be carried out using optical metrology methods on a Walter Helicheck inspection machine in reflected light and a number of images were stored to form a statistical sample. Established new indicators and criteria for grinding efficiency based on image processing of the helical groove of the end mill. As a result, recommendations for the selection of optical control techniques were made for the first time at the intermediate stage of technological preparation for production, in real time, and after processing. In this work, for the first time, we prove the possibility of determining the camera displacement pith distance during continuous scanning of the profile of a helical surface in a radial section, the measurement accuracy and recreating a three-dimensional model of the object. As a result of the work of the new algorithm using the Haar-wavelet with new indicators, it was established that the actual one is located inside the focal zone, which proves the possibility of applied application of the method of monitoring the shape of helical flute of end mills using computer vision. The measurement accuracy of the helical flute increased from 4 to 12% along its profile.

show abstract

SPECTRE: a deep learning network for posture recognition in manufacturing

Cited by 15 publications

References 40 publications

Wearables for Monitoring and Postural Feedback in the Work Context: A Scoping Review

Wearables for Monitoring and Postural Feedback in the Work Context: A Scoping Review

An approach for recognizing surface defects in products with a complex helical shape

New indicators and standards for measuring of the end mill's helical groove by image processing

Contact Info

Product

Resources

About