Automated ergonomic risk monitoring using body-mounted sensors and machine learning

Nath, Nipun D.; Chaspari, Theodora; Behzadan, Amir H.

doi:10.1016/j.aei.2018.08.020

Cited by 95 publications

(67 citation statements)

References 31 publications

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“…Given the large amount of data collected by electronic devices and, in order to process such information quickly and autonomously, it is necessary to use postprocessing based on machine intelligence. Academic studies focusing on the classification of human activities/actions develop algorithms based on deep learning [25,107] and machine learning [28,93,108].…”

Section: Carryingmentioning

confidence: 99%

Worker 4.0: The Future of Sensored Construction Sites

Calvetti

Mêda²,

Gonçalves

et al. 2020

Buildings

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The digitalization of the construction industry (CI) has the aim—among others—to raise the bar of overall productivity. The craft workforce is very relevant on the overall value-chain. Therefore, a boost in this dimension impacts the entire sector. There is a gap in proper methodologies to measure and model productivity. Construction 4.0 novelties provide new approaches for its evaluation and progress. This communication presents a review of workforce productivity assessment and delivers methods focusing primarily on craft workers motion monitoring. Products and services opportunities from Construction 4.0 in the spectrum of craft workforce management include support by embedded sensors for data collection that allow near real-time monitoring. The work developed led to the systematization of a framework to standardize craft workers’ motion productivity. The craft workforce motion productivity framework, Worker 4.0, tenders nine processes integrated on a flowchart to streamline task processes assessment and mechanization level. It also sets up a two-handed/two-legged chart system to model craft workers’ activities and operations. The contributions to the body of knowledge are substantiated on the framework creation with the ability to model and assess craft workforce performance. This approach is meant to serve as base point for different stakeholders focusing on skills, efficiency, mechanization and productivity improvements.

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Section: Carryingmentioning

confidence: 99%

Worker 4.0: The Future of Sensored Construction Sites

Calvetti

Mêda²,

Gonçalves

et al. 2020

Buildings

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“…The first is one by self-assessment [6,14], where commonly the worker is asked to fill out a questionnaire or form indicating their level of exposure to diverse risk factors. However, previous studies indicate that this technique is not always reliable and might be biased [2,22].…”

Section: Ergonomic Assessment Methodsmentioning

confidence: 98%

“…Nowadays, motion data technologies enable precise measurement of postures and body movements. Inertial Measurement Units (IMUs) have been proved to be sufficient for the measurement of trunk inclination [31], and have been successfully implemented for other ergonomic analysis [21,22].…”

Section: Ergonomic Analysis Through Motion Datamentioning

confidence: 99%

“…This is defined as a group of inflammatory and degenerative disorders or injuries in the worker's inner body parts (e.g. muscles, nerves, tendons, joints, cartilages, and spinal discs) [22], pain and functional impairment follow a WMSD in both upper and lower body [4]. WMSDs mostly occur due to prolonged or repetitive execution of activities such as manual handling, heavy lifting, body twisting or working in awkward positions.…”

Section: Introductionmentioning

confidence: 99%

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Designing a web-based automatic ergonomic assessment using motion data

Padilla¹,

Glushkova²,

Menychtas³

et al. 2019

Proceedings of the 12th ACM International Conference on PErvasive Technologies Related to Assistive Environments

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Manual laborers from the industry sector are often subject to critical physical strain that lead to work-related musculoskeletal disorders. Lifting, poor posture and repetitive movements are among the causes of these disorders. In order to prevent them, several rules and methods have been established to identify ergonomic risks that the worker might be exposed during his/her activities. However, the ergonomic assessment though these methods is not a trivial task and a relevant degree of theoretical knowledge on the part of the analyst is necessary. Therefore in this paper, a web-based automatic ergonomic assessment module is proposed. The proposed module uses segment rotations acquired from inertial measurement units for the assessment and provides as feedback RULA scores, color visualisation and limb angles in a simple, intuitive and meaningful way. RULA is one of the most used observational methods for assessment of occupational risk factors for upper-extremity musculoskeletal disorders. By automatizing RULA an interesting perspective for extracting posture analytics for ergonomic assessment is opened, as well as the inclusion of new features that may complement it. For future work, the use of other features and sensors will be investigated for its implementation on the module.

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“…It must be noted that some features may not be useful as they do not contain value-adding information and thus can be discarded for further analysis. The main objective of this step is to select the most relevant and useful features that can be used to find any predefined patterns in the signal (Nath et al, 2018). A commonly used feature selection algorithm named Relief algorithm was applied to the dataset to identify the most distinguishable features.…”

Section: Feature Selectionmentioning

confidence: 99%

Automated Activity Identification for Construction Equipment Using Motion Data From Articulated Members

Rashid

Louis

2020

Front. Built Environ.

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Real-time monitoring and assessment of construction resources have always been a challenge due to the unique, dynamic, and complex nature of each construction site and operation. The ability to automatically classify activities performed by various equipment in real time can aid in making timely tactical operational decisions that can lead to increased fleet productivity, reduced time and cost of operations, and minimized idle times. Such endeavors have traditionally been performed manually through human observation, making it extremely labor and time-intensive. Meanwhile, the development of low-cost micro-electro-mechanical systems (MEMS) with rapidly evolving computing, networking, and storage capabilities, along with the advances in computational techniques such as machine learning present new opportunities in the real-time activity identification domain. Even though previous studies have shown promising results for equipment activity identification at limited levels of detail, they have a fundamental limitation in their reliance on the equipment vibration. Equipment vibration is highly dependent on factors that are extrinsic to the performance of an activity itself such as ground conditions, age and condition of equipment, and operator skill. This aspect of current methods necessitates the collection of training data from the specific equipment of interest and requires manual labeling of training data, thereby limiting its application across different types of equipment and operating conditions. This paper investigates the use of activity-specific equipment motions instead of vibration for activity identification. This approach is the first step toward the larger goal of generating training data automatically from virtual kinematic models of equipment in the future. This paper also provides an array of sensitivity analyses in order to determine the most appropriate parameters for implementing machine learning algorithms for equipment activity identification. A case study was performed using an excavator working on an earthmoving site that demonstrated a significant improvement in equipment activity identification results by utilizing inertial measurement unit (IMU) data of different articulated elements over previous efforts. The results of this paper indicate that more accurate results for activity identification can be obtained by using articulated equipment motion over vibration, which paves the way for the automatic generation of labeled training data in the future.

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Automated ergonomic risk monitoring using body-mounted sensors and machine learning

Cited by 95 publications

References 31 publications

Worker 4.0: The Future of Sensored Construction Sites

Worker 4.0: The Future of Sensored Construction Sites

Designing a web-based automatic ergonomic assessment using motion data

Automated Activity Identification for Construction Equipment Using Motion Data From Articulated Members

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