A proactive workers' safety risk evaluation framework based on position and posture data fusion

Chen, Hainan; Luo, Xiaowei; Zhuang, Zheng; Ke, Jinjing

doi:10.1016/j.autcon.2018.11.026

Cited by 50 publications

(26 citation statements)

References 27 publications

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“…This is so far the most reliable process, as it guarantees an appropriate ground truth reference. However, the performance of algorithmic processes (e.g., evaluation of body postures or near-miss fall detections) was typically validated against visual observations of video recordings [ 69 ] or the ground truth that was provided by experts in the field [ 78 ], and therefore potentially biasing the accuracy of the respective method. As regards the use of commercially available MoCap solutions, a comparison was made of their limitations, advantages and applicability to industrial applications ( Table 4 ) while the accuracy of off-the-shelf MoCap systems has been also extensively reviewed by van der Kruk and Reijne [ 82 ].…”

Section: Discussionmentioning

confidence: 99%

Motion Capture Technology in Industrial Applications: A Systematic Review

Menolotto

Komaris

Tedesco

et al. 2020

Sensors

187

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The rapid technological advancements of Industry 4.0 have opened up new vectors for novel industrial processes that require advanced sensing solutions for their realization. Motion capture (MoCap) sensors, such as visual cameras and inertial measurement units (IMUs), are frequently adopted in industrial settings to support solutions in robotics, additive manufacturing, teleworking and human safety. This review synthesizes and evaluates studies investigating the use of MoCap technologies in industry-related research. A search was performed in the Embase, Scopus, Web of Science and Google Scholar. Only studies in English, from 2015 onwards, on primary and secondary industrial applications were considered. The quality of the articles was appraised with the AXIS tool. Studies were categorized based on type of used sensors, beneficiary industry sector, and type of application. Study characteristics, key methods and findings were also summarized. In total, 1682 records were identified, and 59 were included in this review. Twenty-one and 38 studies were assessed as being prone to medium and low risks of bias, respectively. Camera-based sensors and IMUs were used in 40% and 70% of the studies, respectively. Construction (30.5%), robotics (15.3%) and automotive (10.2%) were the most researched industry sectors, whilst health and safety (64.4%) and the improvement of industrial processes or products (17%) were the most targeted applications. Inertial sensors were the first choice for industrial MoCap applications. Camera-based MoCap systems performed better in robotic applications, but camera obstructions caused by workers and machinery was the most challenging issue. Advancements in machine learning algorithms have been shown to increase the capabilities of MoCap systems in applications such as activity and fatigue detection as well as tool condition monitoring and object recognition.

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Section: Discussionmentioning

confidence: 99%

Motion Capture Technology in Industrial Applications: A Systematic Review

Menolotto

Komaris

Tedesco

et al. 2020

Sensors

187

View full text Add to dashboard Cite

show abstract

“…They proposed a principle fusing position and posture for evaluating the safety risks of construction worker behavior. (33) Awolusi et al evaluated the potential applications of wearable sensing devices and the IoT for the continuous collection, analysis, and monitoring of construction worker safety metrics to mitigate safety hazards and health risks on construction sites. (34) They reviewed wearable sensors and systems that can be used for physiological monitoring, environmental sensing, proximity detection, and location tracking of a wide range of construction hazards and vital signals to provide early warning signs of safety issues to construction workers.…”

Section: Process Monitoring In On-site Assembly Phasementioning

confidence: 99%

Process Monitoring with Support of IoT in Prefabricated Building Construction

Yuan¹,

Ye²,

Lin³

2021

Sensors and Materials

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This paper focuses on how to use IoT technologies to monitor the project implementation process in prefabricated building (PB) construction, which will help optimize the construction schedule and ensure the quality and safety of PB construction. Although IoT monitoring can obtain a large amount of data during construction, the challenge is how to convert these data into intelligent information needed for feasible decision-making. Therefore, we need to clarify the key factors requiring monitoring during construction and how to use these data to make decisions. In this paper, we analyze the whole process of PB construction, comprising five phases: pre-construction, off-site manufacturing, delivery, on-site assembly, and finishing. Through mathematical model formulation to discover the key factors that affect the construction implementation process, we clarify the monitoring data that need to be obtained by IoT technologies. Using these monitoring data, it is possible to make judgments on process abnormalities, promptly give alarms, make proactive adjustments, and optimize the construction implementation process, thus achieving the goal of ensuring project quality and construction safety.

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“…10). The cost of each beacon ranges from 10 to 20 euros [17] and is dependent on the size of the area which needs to be monitored. The deployment plan of the BLE beacons is created using software for covering each building location with a range of at least three beacons by restricting each beacon's signal strength to 5 meters.…”

Section: Data Collection and Pre-processingmentioning

confidence: 99%

Understanding Occupant Behaviors in Dynamic Environments using OBiDE framework

Arslan¹,

Cruz²,

Ginhac

2019

Building and Environment

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Occupants' movements and presence are fundamental and the prerequisites for any type of occupant behaviors' understanding which tells whether a building location is occupied, the number of occupants or an occupant with a specific profile in a certain location. Numerous studies have been conducted over the past few decades to model occupant behaviors stochastically for an improved understanding of their activities for different facility management applications. Despite many research efforts to model dynamic behaviors of building occupants, the understanding of their behaviors by incorporating the contextual information linked to the evolution of the building environment is still not adequately explored. The contextual information linked to locations in dynamic environments changes often over time in terms of position, size, properties and relationships with the environment. This changing building environment affects the occupants' movements and presence inside the facility which ultimately degrades the process of inferring their accurate activities based on the location context. Henceforth, the evolving building information is required to be mapped with occupant movements for an improved understanding of their changing behaviors. To fill this research gap, a framework named 'Occupant Behaviors in Dynamic Environments' (OBiDE) is designed for providing a 'blueprint map' to integrate existing DNAS (Drivers, Needs, Systems, Actions) ontology (i.e. a scheme to model occupant behaviors) with our semantic trajectory enrichment model to better understand the occupant behaviors by tracking the dynamicity of building locations. The proposed framework extends the usability of DNAS by providing a centralized knowledge base that holds the movements of occupants with relevant historicized contextual information of the building environment to study occupant behaviors for different facility management applications.

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A proactive workers' safety risk evaluation framework based on position and posture data fusion

Cited by 50 publications

References 27 publications

Motion Capture Technology in Industrial Applications: A Systematic Review

Motion Capture Technology in Industrial Applications: A Systematic Review

Process Monitoring with Support of IoT in Prefabricated Building Construction

Understanding Occupant Behaviors in Dynamic Environments using OBiDE framework

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