ANN-based automated scaffold builder activity recognition through wearable EMG and IMU sensors

Abstract

Construction worker activity recognition is essential for worker performance and safety assessment. With the development of wearable sensing technologies, many researchers developed kinematic sensor-based worker activity recognition methods with considerable accuracy. However, the limitations of the previous studies remain at the challenge of using smartphones for practical implementation, fewer classified activities, and limited recognized motions and body parts. This study proposes an ANN-based automated construction worker activity recognition method that can recognize complex construction activities. The proposed methodology discusses data acquisition, data fusion, and artificial neural network (ANN) model development. A case study of scaffold builder activities was investigated to validate the proposed methodology's feasibility and evaluate its performance compared to other existing methods. The results show that the proposed model can recognize fifteen scaffold builder activities with an accuracy of 94% with 0.94 weighted precision, recall, and F1 Score.