New Delhi: Researchers at IIT Madras have developed a method to determine whether a worker has the mental capacity to handle a crisis in a factory or other high-stress workspace, by measuring their brainwaves.
According to a study published in the Journal of Computers and Computer Engineering, the researchers found that an electroencephalogram (EEG), a technique that measures brain activity, can measure the cognitive workload of human operators in a chemical plant control room.
Cognitive workload refers to the level of mental effort expended by an individual to perform a task. If a person has a high cognitive workload, they are prone to commit errors.
Our thoughts and activities are driven by electrical signals between the cells in the brain, and variations in these signals are a signature of a person’s current mental state.
According to the team, such measurements may help assess the capability of a worker to respond to an emergency in real-time, which can help prevent accidents and mishaps on a factory floor.
“As much as 70 per cent of the industrial accidents are a result of a human error,” Rajagopalan Srinivasan, a professor at IIT Madras who led the study, told ThePrint.
He added that such errors do not only depend on the skill of the worker but also their mental state and sharpness at that time.
EEG-based technology can help improve training
For the research, the team affixed sensors to the heads of six participants and had them perform eight tasks each.
The tasks involved monitoring a typical industrial section for any disturbances that may lead to accidents. If appropriate decisions and actions were not taken within a limited time frame, it would lead to an accident in the simulation.
The researchers found that the disturbance increased the participants’ cognitive workload, and it reduced only if the correct decision was made.
“After extracting the EEG data, we conduct some analytics using a software we have developed,” Srinivasan said.
The EEG-based approach can provide information about the cognitive workload of operators during training, which can be used to improve the training process. It can also provide targeted cues during learning, to improve the overall effectiveness of the training.
This research is just one of the methods the team is developing to assess whether a person is fit to handle a high-risk situation.
“Many of the companies today are cutting down on their workforce, or reducing the extent of training they give to workers. As a result, the workload of the employees is very high, and they are often stressed,” Srinivasan said.
“We are interested in enabling such workers to be able to perform better. We have developed a broad range of such technologies,” he added.
One of them also involves tracking the eye movements of workers, especially in jobs that require focusing on information on a screen such as air traffic handlers or plants that are operated from a control room.
“Any screen will have multiple pieces of information. With eye-tracking, we can tell which information the eye is focussing on. The part of the screen that the eyes focus on will indicate whether a person is thinking along the right lines,” Srinivasan said.
Measuring brainwaves will complement such technologies that his team is working on.
“We are now trying to develop testbeds so that we can now try out this technology on professionals. So far we have only tested this on volunteer students,” he said.