Silent speech interfaces (SSIs) promise private and noise-immune communication, but current solutions often sacrifice user comfort, mobility, or privacy. This paper introduces PPGSpeech, a novel SSI that overcomes these limitations by pioneering the use of photoplethysmography (PPG) acquired from a comfortable, necklace-style wearable device. Our core discovery is that subtle neck muscle movements during silent articulation induce distinct, measurable modulations in the underlying PPG signal. To harness this phenomenon, we developed a complete end-to-end system featuring (1) a custom neck-worn sensor for multi-wavelength PPG acquisition, (2) a deep learning pipeline that converts 1D PPG signals into 2D time-frequency images via Continuous Wavelet Transform (CWT) and classifies them using a lightweight CNN, and (3) a Pix2Pix GAN model to reconstruct audible speech from the captured signals. In a 16-participant study covering a vocabulary of 15 commands and four confounding actions, our user-dependent model achieved a recognition accuracy of 81.41% ± 9.74. Furthermore, our speech reconstruction achieved a Mean Opinion Score (MOS) of 3.48 and a Word Correct Rate (WCR) of 60.67%, demonstrating that the PPG signal is sufficiently rich to recover intelligible speech. By establishing the viability of neck-based PPG for silent speech, PPGSpeech offers a discreet, privacy-preserving, and continuously wearable paradigm for next-generation human-computer interaction.

Facial expressions are vital for effective communication, conveying emotions and health status. Traditional analysis methods, like manual annotations and geometric models, are labor-intensive and inadequate for complex situations. While vision-based approaches improve accuracy, they often struggle with environmental constraints and privacy concerns. Non-visual wearables offer flexibility but can be uncomfortable and power-hungry. To overcome these issues, we introduce IMUFace, an innovative earplug platform that uses inertial measurement units (IMUs) for real-time facial expression reconstruction. IMUFace captures facial motion data through IMUs in headphones and processes it with a deep learning model to estimate facial landmarks accurately. These predictions are then fitted to the FLAME model, creating realistic 3D facial animations. Compact and low-power, IMUFace represents a significant advancement in generating 3D facial animations for everyday use.