Haptic Signal Reconstruction in eHealth Internet of Things

Abstract

With the haptic technology continuously enlarging the eHealth Industry Internet of Things (IIoT) ecosystem, haptic perception service which requires effective haptic signal reconstruction for immersive experience has become an indispensable function. However, the majority of existing haptic signal reconstruction methods are generally inefficient because of undergoing extremely complex operations or inefficient feature representations. To resolve this dilemma, this article proposes a long short-term memory-based force reconstruction network (LSTM-FRN) by designing a novel sparse attention module for low-latency reconstruction and a novel metric learning-based constraint for high-precision reconstruction, yielding an excellent tradeoff between the computational complexity and feature representation. To train our network, we construct a large-scale data set of synchronous needle motion signals and haptic signals in acupuncture needle insertion. Finally, we build an interactive needle insertion training system (HapAR-NITS) by integrating augmented reality (AR), the LSTM-FRN-based haptic reconstruction as well as a skill assessment subsystem. Comprehensive experiments demonstrate that the proposed multiple technologies enable our HapAR-NITS to achieve satisfying immersive experience and manipulation effects.