Non-verbal Hands-free Control for Smart Glasses using Teeth Clicks

TL;DR

This paper introduces STEALTHsense, a novel discreet control method for smart glasses using teeth clicks detected by accelerometers, achieving high accuracy and robustness in noisy environments.

Contribution

It presents a low-footprint neural network approach for teeth-click detection on smart glasses, demonstrating high accuracy and noise resilience.

Findings

Achieved 93% cross-person accuracy in teeth-click detection.

Effective in noisy environments, confirming real-world applicability.

Proposed model outperforms traditional methods in accuracy and efficiency.

Abstract

Smart glasses are emerging as a popular wearable computing platform potentially revolutionizing the next generation of human-computer interaction. The widespread adoption of smart glasses has created a pressing need for discreet and hands-free control methods. Traditional input techniques, such as voice commands or tactile gestures, can be intrusive and non-discreet. Additionally, voice-based control may not function well in noisy acoustic conditions. We propose a novel, discreet, non-verbal, and non-tactile approach to controlling smart glasses through subtle vibrations on the skin induced by teeth clicking. We demonstrate that these vibrations can be sensed by accelerometers embedded in the glasses with a low-footprint predictive model. Our proposed method, called STEALTHsense, utilizes a temporal broadcasting-based neural network architecture with just 88K trainable parameters and 7.14M Multiply and Accumulate (MMAC) per inference unit. We benchmark our proposed STEALTHsense against state-of-the-art deep learning approaches and traditional low-footprint machine learning approaches. We conducted a study across 21 participants to collect representative samples for two distinct teeth-clicking patterns and many non-patterns for robust training of STEALTHsense, achieving an average cross-person accuracy of 0.93. Field testing confirmed its effectiveness, even in noisy conditions, underscoring STEALTHsense's potential for real-world applications, offering a promising solution for smart glasses interaction.