Biomechanical Analysis of Manual Material Handling Tasks on Scaffold

Bangaru, Srikanth Sagar; Chao, Wang; Aghazadeh, Fereydoun

doi:10.1061/9780784482438.072

Cited by 1 publication

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“…Before the start of the experiment, all the participants were given enough time to familiarize themselves with the tools to eliminate systematic bias, which occurs due to learning effects [ 49 ]. The participants were asked to warm up their bodies before the start of the session, and enough rest was provided between the trials to prevent injuries and fatigue [ 50 ]. Once the armband was worn on the body and synced with the computer, a two minute settling time was considered before the start of the experiment to prevent the rotational drift.…”

Section: Methodsmentioning

confidence: 99%

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

Bangaru

Wang

Aghazadeh

2020

Sensors

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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.

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Section: Methodsmentioning

confidence: 99%