IMU based Smart Safety Hook for Fall Prevention at Construction Sites

Khan, Muhammad Basit; Khalid, Rabia; Anjum, Sharjeel; Khan, Numan; Park, Chan-Sik

doi:10.1109/tensymp52854.2021.9550944

Cited by 11 publications

(2 citation statements)

References 19 publications

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“…Another possibility is the strategic activation of alert devices placed in the construction environment [56][57][58][59][60], including the prospect of embedding the alerts in smart tools [61][62][63] or to convey feedback and alerts to the managerial supervisory team through dashboards and personalized texts or emails [64][65][66][67][68]. In one instance, a pulse oximetry sensor was integrated into a hard hat to protect construction workers from carbon monoxide poisoning [51].…”

Section: Efforts Directed Towards Safety Warningsmentioning

confidence: 99%

On-Body Placement of Wearable Safety Promotion Devices Based on Wireless Communication for Construction Workers-on-Foot: State-of-the-Art Review

Yadav¹,

Sadeghi²,

Kang³

2022

Sensors

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High auditory noise levels and limited visibility are often considered among the main factors that hinder seamless communication on construction sites. Many previous research studies have leveraged technology to overcome these obstacles and communicate using the hearing, sight and touch senses. However, the technological efficacy does not secure the users’ perceptivity of the wireless communication devices. Statistical data regarding the number of fatal accidents on construction sites have remained steady despite regular efforts. This study analyzed prior research on wearable safety promotion devices for personnel that move around the jobsite on foot. A seven-point checklist was utilized to shortlist prior studies (2005–2021) attempting to provide safety information wirelessly to the construction workers-on-foot. The reasoning behind various on-body placements was investigated along with the information conveyed using the three communication modalities. A novel communication network is also introduced to visualize the technical details. Lastly, limitations and future recommendations have been presented to gain insights about the factors that might affect the placement of the wearable safety promotion devices.

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Section: Efforts Directed Towards Safety Warningsmentioning

confidence: 99%

On-Body Placement of Wearable Safety Promotion Devices Based on Wireless Communication for Construction Workers-on-Foot: State-of-the-Art Review

Yadav¹,

Sadeghi²,

Kang³

2022

Sensors

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“…In recent years, AI has played a positive role in complex pattern recognition and solving real-world problems. , After the emergence of machine learning (ML) and deep learning (DL), researchers have employed these technologies in various fields such as healthcare, , construction, − bioinformatics, finance, , and agriculture . In the field of nanomaterials science, researchers have started to employ AI-based DL and ML technology for RHEED interpretation, such as employing statistically available methods with ML for RHEED interpretation. ,− For instance, authors used principal component analysis (PCA) and k -means clustering to analyze different types of perovskite films to understand their stoichiometry.…”

Section: Introductionmentioning

confidence: 99%

Rotation Error Detection of Gallium Nitride (GaN) Substrate in MBE Utilizing Ensemble Learning

Anjum¹,

Lee²,

Noh³

2023

Crystal Growth & Design

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A rotating substrate is essential for achieving highquality and uniform growth of gallium nitride (GaN) thin films in molecular beam epitaxy (MBE). By rotating the substrate during growth, the deposited material can be distributed more uniformly across the surface, resulting in a more uniform and defect-free film. However, the substrate may stop rotating during fabrication, and it is a challenge for engineers to detect the rotation of the substrate using information from reflection high-energy electron diffraction (RHEED). The current method for analyzing rotation using RHEED data is manual, time-consuming, and error-prone. Therefore, this research proposes an automated machine learning-based rotation error detection (RED) model to determine wafer rotation and then alert the engineers in case of an anomaly. The proposed approach consists of (1) ensemble learning models such as random forest classifier and extreme gradient boosting (XGBoost) for rotation error detection, (2) prepared novel intensity data from RHEED videos, (3) comparison to select the best ensemble learning model at a small scale, ( 4) training XGBoost at a large scale, (5) deployment in production using MLflow, (6) development of a REST application programming interface (API) for inference, and (7) integrated KakaoTalk API to generate and send a detailed alert message to engineers so that they can take appropriate actions in time. The experimental results achieved F1 scores for normal and rotational errors of 98.9 and 83.1%, respectively. The proposed method can make a valuable contribution to epitaxial wafer growth by enhancing production efficiency, improving product quality, and reducing costs.

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