SOURCE-LINKED INTELLIGENCE

Forecasting and Preventing Human Errors

CORDIS · observation · Publication date unknown

s as soon as they occur or are even irreversible. In these cases, it is very important to recognize human errors before they occur. The goal of this project is therefore to develop methods based on artificial intelligence that forecast human errors from video data. We focus on erroneous and unintentional human actions and we aim to support humans to avoid them. In order to achieve this goal, we aim to solve three tasks jointly. We aim to develop methods that forecast human motion and intention with a very low latency such that unintentional actions can be recognized before they occur. Without the capability to interfere, however, even the best forecasting model does not prevent human errors. We therefore aim to develop a model that generates an auditory feedback if an error is forecast. The feedback, however, should not only warn humans, but also guide them such that they can successfully complete their intended action. Finally, we aim to model how humans will react to the feedback. We thus aim to develop a model that forecasts the motion of humans and objects they inter

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recordType

award

status

SIGNED

region

EU

value

1999629

unit

EUR