Towards An Eeg Based Mental Workload Evaluation Method For Construction Workers' Hmd Ar Use

Augmented reality (AR), with its strong ability to enhance how a user interacts with the digital model and the real environment, has great potential to optimize the conventional construction process. Not only the design and planning phase decisions, but also the on-site job accuracy and efficiency can be improved through AR. Furthermore, the development and application of head mounted devices (HMD) provide the workers a new hands-free method to interact with digital model on the site. However, human factor aspects of HMD AR use in construction, such as human computer interaction (HCI) optimization, hardware clumsiness, and safety monitoring are still not well studied. Hence, evaluating the user's mental and physical workload can be an effective way to understand how a worker might react to this new construction mode. Since it is a new attempt to monitor the mental workload during the HMD AR use with construction activities, in this paper, we reviewed the previous studies in mental workload evaluation, and introduced a possible approach to study this problem. Different types of mental workload evaluating methods such as electroencephalogram (EEG), and NASA-TLX were discussed based on their pros and cons in this field.