NeckCare: Preventing Tech Neck using Hearable-based Multimodal Sensing

TL;DR

NeckCare is a system that uses hearable sensors to detect and prevent tech neck by monitoring posture and distance from screens in real-time, achieving high accuracy and providing immediate user feedback.

Contribution

This work introduces a novel hearable-based multimodal sensing system for real-time tech neck detection and prevention, with high accuracy in posture classification and distance estimation.

Findings

Posture classification accuracy of 96% with IMU data

Posture classification accuracy of 99% with combined IMU and audio data

Millimeter-level accuracy in distance estimation even in noisy environments

Abstract

Tech neck is a modern epidemic caused by prolonged device usage and it can lead to significant neck strain and discomfort. This paper addresses the challenge of detecting and preventing tech neck syndrome using non-invasive ubiquitous sensing techniques. We present NeckCare, a novel system leveraging hearable sensors, including IMUs and microphones, to monitor tech neck postures and estimate distance form screen in real-time. By analyzing pitch, displacement, and acoustic ranging data from 15 participants, we achieve posture classification accuracy of 96% using IMU data alone and 99% when combined with audio data. Our distance estimation technique is millimeter-level accurate even in noisy conditions. NeckCare provides immediate feedback to users, promoting healthier posture and reducing neck strain. Future work will explore personalizing alerts, predicting muscle strain, integrating neck exercise detection and enhancing digital eye strain prediction.