Assessment of Construction Workers’ Labor Intensity Based on Wearable Smartphone System

The workforce shortage is one of the significant problems in the construction industry. To overcome the challenges due to workforce shortage, various researchers have proposed wearable sensor-based systems in the area of construction safety and health. Although sensors provide rich and detailed information, not all sensors can be used for construction applications. This study evaluates the data quality and reliability of forearm electromyography (EMG) and inertial measurement unit (IMU) of armband sensors for construction activity classification. To achieve the proposed objective, the forearm EMG and IMU data collected from eight participants while performing construction activities such as screwing, wrenching, lifting, and carrying on two different days were used to analyze the data quality and reliability for activity recognition through seven different experiments. The results of these experiments show that the armband sensor data quality is comparable to the conventional EMG and IMU sensors with excellent relative and absolute reliability between trials for all the five activities. The activity classification results were highly reliable, with minimal change in classification accuracies for both the days. Moreover, the results conclude that the combined EMG and IMU models classify activities with higher accuracies compared to individual sensor models.

Section: Discussionmentioning

confidence: 74%

Section: Introductionmentioning

confidence: 99%

Data Quality and Reliability Assessment of Wearable EMG and IMU Sensor for Construction Activity Recognition

Bangaru

Wang

Aghazadeh

2020

“…Closely-related studies have explored automating the detection of diverse awkward postures through the use of raw sensor output from both wearable IMUs [ 5 , 6 , 7 ] and smart-device built-in IMUs [ 8 , 9 , 10 , 11 , 12 ]. Existing studies mainly focus on optimizing the models for analyzing motion data and accurately detecting targeted human activities/postures [ 13 ].…”

Section: Introductionmentioning

confidence: 99%

Wearable Inertial Measurement Unit Sensing System for Musculoskeletal Disorders Prevention in Construction

Zhao

Obonyo

Bilén

2021

Construction workers executing manual-intensive tasks are susceptible to musculoskeletal disorders (MSDs) due to overexposure to awkward postures. Automated posture recognition and assessment based on wearable sensor output can help reduce MSDs risks through early risk-factor detection. However, extant studies mainly focus on optimizing recognition models. There is a lack of studies exploring the design of a wearable sensing system that assesses the MSDs risks based on detected postures and then provides feedback for injury prevention. This study aims at investigating the design of an effective wearable MSDs prevention system. This study first proposes the design of a wearable inertial measurement unit (IMU) sensing system, then develops the prototype for end-user evaluation. Construction workers and managers evaluated a proposed system by interacting with wearable sensors and user interfaces (UIs), followed by an evaluation survey. The results suggest that wearable sensing is a promising approach for collecting motion data with low discomfort; posture-based MSDs risk assessment has a high potential in improving workers’ safety awareness; and mobile- and cloud-based UIs can deliver the risk assessment information to end-users with ease. This research contributes to the design, development, and validation of wearable sensing-based injury prevention systems, which may be adapted to other labor-intensive occupations.

“…As a result, sensor technology begins to gradually emerge in the field of construction supervision. These technology include RFID (radio frequency identification) technology for material management [8], Smartphone-based worker efficiency management technology [9], location monitoring system based on ultrawide band technology, RF (radio frequency) remote sensing, WIFI (wireless fidelity) [10,11,12], and tracking workers’ paths relying on video streams [13,14]. Although each assistive technology has its own advantages, there is still room for improvement in the sensing technology.…”

Section: Introductionmentioning

confidence: 99%

Safety Distance Identification for Crane Drivers Based on Mask R-CNN

Yang

Yuan

Zhang

et al. 2019

Self Cite

Tower cranes are the most commonly used large-scale equipment on construction site. Because workers can’t always pay attention to the environment at the top of the head, it is often difficult to avoid accidents when heavy objects fall. Therefore, safety construction accidents such as struck-by often occurs. In order to address crane issue, this research recorded video data by a tower crane camera, labeled the pictures, and operated image recognition with the MASK R-CNN method. Furthermore, The RGB color extraction was performed on the identified mask layer to obtain the pixel coordinates of workers and dangerous zone. At last, we used the pixel and actual distance conversion method to measure the safety distance. The contribution of this research to safety problem area is twofold: On one hand, without affecting the normal behavior of workers, an automatic collection, analysis, and early-warning system was established. On the other hand, the proposed automatic inspection system can help improve the safety operation of tower crane drivers.