A research paper titled “A Wideband Multimodal Flexible Sensor Integrating Vertical Graphene and Sea Urchin-Like Nanoparticles for Post-Stroke Rehabilitation” was published in Advanced Materials.

Stroke is a leading cause of long-term disability worldwide, with post-stroke aphasia significantly impairing communication and social interaction. Traditional rehabilitation devices are often bulky, expensive, and impractical for daily use, particularly in speech recovery, where accessible and effective solutions remain limited. To address this challenge, this study introduces a portable and wearable sensor system for stroke-induced aphasia rehabilitation. The proposed sensor integrates a flexible, ultrasensitive, and durable dual-sensor system comprising an Ag-MnO2-based sea-urchin-like nanopar-ticle pressure sensor to detect high-frequency vocal vibrations and a vertical graphene/polydimethylsiloxane (VGr/PDMS) strain sensor to capture low-frequency muscular movements. The sensors, integrated into a flexible circuit, employ an encoder-cycle-consistent generative adversarial networks (Cy-cleGAN) model that recognizes users’ intent and recovers voice, significantly reducing dependency on large-scale labelled datasets. Experimental results demonstrate accurate intent recognition with accuracies for certain commands exceeding 95%. The reconstructed speech exhibits improved naturalness based on objective and perceptual evaluations, highlighting potential clinical utility in enhancing daily communication and interaction for stroke survivors.

Geng Zhong is the first author. This work was supported by funding from the Shenzhen Science and technology Program and the Shenzhen Overseas talent Program. Paper link:

https://advanced.onlinelibrary.wiley.com/doi/abs/10.1002/adma.202508206

Figure 1. A neural network is trained using the amplitude envelope of acoustic signals and sensor data for speech recognition and reconstruction.