Human-Robot Collaboration in the Workplace: Assessing the Impact on Employee Well-Being and Productivity

As the integration of robots into various industries becomes increasingly prevalent, understanding the nuanced effects on the workforce is imperative. This study investigates the implications of Human-Robot Collaboration (HRC) in the workplace, specifically focusing on its impact on employee well-being and productivity. Leveraging advanced deep learning methodologies, the proposed Long Short-Term Memory – Gated Recurrent Unit (LSTM-GRU) model is introduced as a novel approach for assessing these critical factors. Implemented in Python, the LSTM-GRU model serves as a powerful tool for assessing the multifaceted dynamics of HRC. The utilization of deep learning techniques, coupled with the LSTM-GRU architecture, allows for a more nuanced understanding of the intricate relationships between human and robotic collaborators in the workplace. The findings of this study contribute valuable insights for organizations seeking to optimize HRC integration, highlighting the significance of advanced computational approaches in gauging the impact on employee well-being and productivity. The Python implementation further enhances the model's accessibility, enabling researchers and practitioners to adopt and adapt this methodology in diverse workplace settings. The study employs a diverse set of performance metrics, including accuracy, precision, recall, and F1-Score, to comprehensively evaluate the effectiveness of the proposed model. The results reveal a remarkable accuracy of 99.11% for the LSTM-GRU model, showcasing its superior ability to capture nuanced temporal dependencies in comparison to existing methods such as Convolutional Neural Network(CNN), CNN-LSTM, and Bidirectional Long Short-Term Memory (BiLSTM). The proposed model's accuracy surpasses these methods by 3.23%, underscoring its robustness in accurately identifying and predicting aspects related to employee well-being and productivity.