Automation in Construction

Deep learning-based networks for automated recognition and classification of awkward working postures in construction using wearable insole sensor data

Maxwell Fordjour Antwi-Afari, Yazan Qarout, Randa Herzallah, Shahnawaz Anwer, Waleed Umer, Yongcheng Zhang, Patrick Manu

Department of Civil Engineering, College of Engineering and Physical Sciences, Aston University, Birmingham, United Kingdom


Awkward working postures, Deep learning networks, Wearable insole pressure system, Work-related musculoskeletal disorders, Work-related risk recognition


Among the numerous work-related risk factors, construction workers are often exposed to awkward working postures that may lead them to develop work-related musculoskeletal disorders (WMSDs). To mitigate WMSDs among construction workers, awkward working posture recognition is the first step in proactive WMSD prevention. Several researchers have proposed wearable sensor-based systems and machine learning classifiers for awkward posture recognition. However, these wearable sensor-based systems (e.g., surface electromyography) are either intrusive or require attaching multiple sensors on workers' bodies, which may lead to workers' discomfort and systemic instability, thus, limiting their application on construction sites. In addition, machine learning classifiers are limited to human-specific shallow features which influence model performance. To address these limitations, this study proposes a novel approach by using wearable insole pressure system and recurrent neural network (RNN) models, which automate feature extraction and are widely used for sequential data classification. Therefore, the research objective is to automatically recognize and classify different types of awkward working postures in construction by using deep learning-based networks and wearable insole sensor data. The classification performance of three RNN-based deep learning models, namely: (1) long-short term memory (LSTM), (2) bidirectional LSTM (Bi-LSTM), and (3) gated recurrent units (GRU), was evaluated using plantar pressure data captured by a wearable insole system from workers on construction sites. The experimental results show that GRU model outperforms the other RNN-based deep learning models with a high accuracy of 99.01% and F1-score between 93.19% and 99.39%. These results demonstrate that GRU models can be employed to learn sequential plantar pressure patterns captured by a wearable insole system to recognize and classify different types of awkward working postures. The findings of this study contribute to wearable sensor-based posture-related recognition and classification, thus, enhancing construction workers' health and safety.

Moticon's Summary

The construction industry faces significant health and safety challenges, with work-related musculoskeletal disorders (WMSDs) being a leading cause of non-fatal injuries. This study proposes a novel approach using Moticon sensor insoles combined with deep learning networks to automatically recognize and classify awkward working postures, which are major risk factors for WMSDs. Plantar pressure data were collected from construction workers performing five different awkward postures. Three types of recurrent neural networks (RNNs) were evaluated: Long-Short Term Memory (LSTM), Bidirectional LSTM (Bi-LSTM), and Gated Recurrent Units (GRU). The GRU model achieved the highest classification accuracy of 99.01%, outperforming the other models. This approach offers a reliable, non-intrusive method for real-time monitoring of ergonomic risks, providing valuable insights for improving worker safety and reducing the incidence of WMSDs in the construction industry.

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