Comparative Analysis of Deep-Learning Approaches for Automatic Recognition of Awkward Postures in Construction Environments through Wrist-Worn Biosensors

Construction workers frequently face the risk of adopting awkward work postures, which can lead to work-related musculoskeletal disorders. Many existing solutions using wearable sensors suffer from intrusiveness and the need for multiple sensor attachments. This study proposes a novel method for automatic recognition of awkward postures using wristband biosensors and deep learning algorithms. Physiological data from ten subjects were collected, processed, and used to train the models. Long short-term memory (LSTM), bidirectional long short-term memory (Bi-LSTM), and one-dimensional convolutional neural network (1D-CNN) models were compared. The Bi-LSTM model achieved the highest accuracy at 95.09%, followed by the LSTM model with 91.49%, and the 1D-CNN model with 89.50%. The study also conducted a comprehensive analysis of the impact of diverse signal combinations and time windows on posture recognition, providing valuable insights. The findings expand the use of physiological signals for safety enhancement, specifically in recognizing awkward postures. This study contributes to wearable sensor-based posture recognition, ultimately enhancing the health and safety of construction workers.