NeckCare: Preventing Tech Neck using Hearable-based Multimodal Sensing
Bhawana Chhaglani, Alan Seefeldt
TL;DR
NeckCare addresses tech neck by integrating IMU data and acoustic distance sensing through ubiquitous hearables to monitor neck posture and screen distance in real time. The system employs a sensor-fusion approach using pitch and microphone-based ToF measurements to classify five postures with up to 99% accuracy and achieve millimeter-level distance estimation, validated on data from 15 participants. The work demonstrates a privacy-preserving, environment-independent solution that provides immediate feedback and outlines future enhancements such as personalized alerts and muscle-load estimation. Overall, NeckCare offers a practical path toward widespread, non-invasive posture monitoring and posture-driven interventions to reduce neck strain and digital eye strain in real-world work settings.
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.