Deep encoder–decoder network based data-driven method for impact feedback rendering on head during earthquake

Abstract

In safety training simulators, realistic haptic feedback is essential to make people construct accurate situation awareness through experiencing. In this regard, this paper presents a new and innovative system that provides the haptic experience of falling objects on user’s head during an earthquake. Special focus was on the accurate reproduction of impact feedback when various objects fall on the head. To this end, we propose a novel data-driven approach. This approach first collects 3-axis acceleration signals during real collision under several impact velocities. Afterward, 3D acceleration data is abstracted to a 1D acceleration profile using our novel max–min extraction approach. The impact signal for an arbitrary velocity is interpolated using a deep convolutional bidirectional long short-term memory encoder–decoder model. Rendering hardware is also implemented using high performance voice-coil vibrotactile actuator. Numerical and subjective evaluations are carried out to evaluate the performance of the proposed approach.Kindly check and confirm the edit made in the title.I confirm the edit is okay.Please confirm if the author names are presented accurately and in the correct sequence (given name, middle name/initial, family name). Authors Given name: [Joolekha Bibi] Last name: [Joolee], Given name: [Mohammad Shadman] Last name: [Hashem]. Also, kindly confirm the details in the metadata are correct.Yes, the author names are presented accurately and in the correct sequence.