Construction worker's awkward posture recognition through supervised motion tensor decomposition

Chen, Jiayu; Qiu, Jun; Ahn, Changbum R.

doi:10.1016/j.autcon.2017.01.020

Cited by 149 publications

(56 citation statements)

References 49 publications

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“…Chen et al trained an SVM algorithm with predefined posture classes to let the model discriminate postures in a set of activities. Considering different combinations of training parameters to obtain a high value of accuracy, they could not correctly classify squatting and stooping, even if they considered a dataset representing the whole kinematic chain [7]. The discrimination of unsafe biomechanical loads was better performed by the T-Ll dataset than the UpB dataset, where the obtained rate of accuracy might not have been enough to identify a non-ergonomic working posture.…”

Section: Discussionmentioning

confidence: 99%

“…The assumption of non-neutral postures during working activities is the main cause of injuries and accidents in a workplace [1][2][3][4][5][6]. Manual material handing (MMH) tasks expose workers to a high level of ergonomic hazards, which finds high correlation with the onset of work-related musculoskeletal disorders (WMSDs) [7][8][9][10][11]. Awkward postures have been identified as risk factors for the musculoskeletal system, especially in the construction field [12][13][14][15].…”

Section: Introductionmentioning

confidence: 99%

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Measuring Biomechanical Risk in Lifting Load Tasks Through Wearable System and Machine-Learning Approach

Conforti

Mileti

Prete

et al. 2020

Sensors

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Ergonomics evaluation through measurements of biomechanical parameters in real time has a great potential in reducing non-fatal occupational injuries, such as work-related musculoskeletal disorders. Assuming a correct posture guarantees the avoidance of high stress on the back and on the lower extremities, while an incorrect posture increases spinal stress. Here, we propose a solution for the recognition of postural patterns through wearable sensors and machine-learning algorithms fed with kinematic data. Twenty-six healthy subjects equipped with eight wireless inertial measurement units (IMUs) performed manual material handling tasks, such as lifting and releasing small loads, with two postural patterns: correctly and incorrectly. Measurements of kinematic parameters, such as the range of motion of lower limb and lumbosacral joints, along with the displacement of the trunk with respect to the pelvis, were estimated from IMU measurements through a biomechanical model. Statistical differences were found for all kinematic parameters between the correct and the incorrect postures (p < 0.01). Moreover, with the weight increase of load in the lifting task, changes in hip and trunk kinematics were observed (p < 0.01). To automatically identify the two postures, a supervised machine-learning algorithm, a support vector machine, was trained, and an accuracy of 99.4% (specificity of 100%) was reached by using the measurements of all kinematic parameters as features. Meanwhile, an accuracy of 76.9% (specificity of 76.9%) was reached by using the measurements of kinematic parameters related to the trunk body segment.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Measuring Biomechanical Risk in Lifting Load Tasks Through Wearable System and Machine-Learning Approach

Conforti

Mileti

Prete

et al. 2020

Sensors

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“…Despite being inexpensive and applicable to a wide range of workplace, this approach is disruptive in nature and timeconsuming (David, 2005).  Measurement method: Some measurement tools such as Inertial Measurement Units (IMUs) and Surface Electromyography (SEMG) Sensors are generally attached to workers' body to collect data (Alwasel, Elrayes, Abdel-Rahman, & Haas, 2017;Maxwell Fordjour Antwi-Afari, Li, Yu, & Kong, 2018), but construction workers' body movements of a few muscles can be recorded, and it is difficult to monitor the whole body (D. Chen et al, 2018;J. Chen, Qiu, & Ahn, 2017).…”

Section: Literature Reviewmentioning

confidence: 99%

Titreşime Maruz Kalan İnşaat İşçileri İçin Optimal Emeklilik Yaşı: Türkiye'de Bir Vaka Çalışması

Yıldızel¹,

Çöğürcü²,

Uzun³

et al. 2019

European Journal of Science and Technology

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Öz Titreşimler yani; ses dalgaları, belirli bir süre içinde belirli sayıda tekrar eden dalgalardır. Ses dalgalarından farkı, sesin hava yoluyla iletilmesidir, titreşim ise vücudun katı kısımlarını kullanır. Titreşimler, operatörlerin el ve kollarına ve hatta tüm yapılarına bina operasyonlarında kullanılan makine ve ekipmanlardan geçer. Titreşimler birçok rahatsızlığa neden olur. Özellikle inşaat sektöründe, yüksek frekanslı titreşim yayıcı makina ve ekipmanlarda çalışan işçilerde ciddi sağlık sorunları gözlenmektedir. Bu sağlık sorunlarından en yaygın olanı orta ve uzun vadede ciddi mesleki ve bazı sağlık sorunlarına neden olan titreşimli bir sendromdur. Titreşim sendromu hastaları özellikle inşaat işlerinde fonksiyonel, sosyal ve psikolojik sakatlık yaşarlar. Bu nedenle, bu mesleklerin sigortaları ve amortismanları diğer çalışanlardan farklı olmalıdır. Bu çalışmada inşaat işçilerinin titreşim sendromu risk sınıflamaları yapılmıştır. Sıkı kavanoz açma zorluğu, el kavrama zayıflığı ve parmak rengi değişiklikleri çalışanların genel sorunları arasında öne çıkmaktadır. Ankete göre parmak soğukluğu ve parmak uyuşukluğu daha az ortaya çıkmaktadır. Bu sonuca göre gerekli önlemler ve yasal mevzuat önerileri sunuldu. Titreşime maruz kalan inşaat işçilerinin emeklilik yaşı için Taguchi metoduna dayalı bir optimizasyon yapıldı. İnşaat işçilerinin çalışma sürelerinin analiz sonuçlarına göre yeniden incelenmesi gerektiği sonucuna varılmıştır. İlgili mevzuat, her meslek için önerilen emeklilik yaşı uyarınca güncel tutulabilir.

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“…Therefore, the on-site construction activity is one of the critical resources contributing to the construction project performance, and its effective control and management is always considered as a key to success [1]. By tracking workers-on-foot and construction heavy equipment, near-misses, collisions and safety risks can be prevented and alleviated [2], dangerous and awkward postures can be detected and alarmed [3], productivity can be measured in a timely and quantitative manner [4][5][6], etc.…”

Section: Introductionmentioning

confidence: 99%

“…Nowadays, commercial IMU is usually made up of a tri-axis gyroscope and a tri-axis accelerometer (6-axis IMU), and a tri-axis magnetometer (9-axis IMU), enabling the measurement of acceleration, angular velocity, and magnetic field [11]. Scholars have applied this technology to detect awkward postures to prevent musculoskeletal disorders [3,12,13], near-misses and hazards are also recognized automatically and analyzed to assess the potential risks [14].…”

Section: Introductionmentioning

confidence: 99%

A Low-Cost and Smart IMU Tool for Tracking Construction Activities

Yang¹,

Wang

et al. 2019

Proceedings of the International Symposium on Automation and Robotics in Construction (IAARC)

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Real-time activity monitoring is becoming one of the most significant technologies on construction sites because it can be applied to a variety of management problems, such as productivity formula and safety monitoring. However, current monitoring technologies are limited to recognizing postures in an ideal environment rather than dealing with the ambient occlusion and people-intensive situations on real construction sites. Therefore, this study develops a low-cost, non-intrusion and portable tool system in order to trace and track construction activities on complex and crowed construction sites. This system is composed of wireless sensors and a smart algorithm. Each sensor consists of an inertial measurement unit, a communication sensor (bluetooth low energy sensor) and several environmental sensors, which broadcasts the identification, acceleration, palstance and environmental measurements at a constant frequency. Since the dimension of the sensor is only 20 x 15 x 2 mm, it can be easily attached or screwed on to the hand tools as well as integrated with power tools. When a laptop or cell phone receives from these sensors, the construction activities are derived by the artificial intelligence algorithm in a timely manner, providing an visual posture monitoring as well as an automatic record of project progress. In the end, practical experiments of a concrete vibrator and a hammer prove the feasibility and effectiveness of the proposed IMU-based tool tracing and tracking system.

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Construction worker's awkward posture recognition through supervised motion tensor decomposition

Cited by 149 publications

References 49 publications

Measuring Biomechanical Risk in Lifting Load Tasks Through Wearable System and Machine-Learning Approach

Measuring Biomechanical Risk in Lifting Load Tasks Through Wearable System and Machine-Learning Approach

Titreşime Maruz Kalan İnşaat İşçileri İçin Optimal Emeklilik Yaşı: Türkiye'de Bir Vaka Çalışması

A Low-Cost and Smart IMU Tool for Tracking Construction Activities

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