Classification of drivers’ workload through electrocardiography
Date of Issue2016
School of Electrical and Electronic Engineering
With the development of technology, many kinds of attractive functions such as answering phone call, playing games, and listening to music have been added to car system in order to make driving more convenient and interesting. Meanwhile, the number of car accidents is increasing due to these extra distracting functions. It is obvious that these extra distracting functions can affect human workload of drivers. Therefore, studying human workload of drivers using these functions is very important to safety driving. This final year project aims to classify human workload during driving by two methods and to recommend one of them as better method. The two methods are classification by ECG and by SWAT. This project is divided into two parts: the first part is to collect the required data by conducting an experiment by a partly automatic driving system; the second part is to analyze the data, classify the human workload, and test the accuracy of classification by ELM to find the better method. Finally, the analysis showed more than 97% accuracy of detecting levels of workload by ECG classification. This means that our project is successful and ECG data is a better way to classify human workload.
Final Year Project (FYP)
Nanyang Technological University