Tech neck, a growing musculoskeletal concern caused by prolonged poor posture during device use, has significant health implications. This study investigates the relationship between head posture and muscular activity in the upper trapezius muscle to predict muscle strain by leveraging data from EMG sensors and head trackers. We train a regression model to predict EMG envelope readings using head movement data. We conduct preliminary experiments involving various postures to explore the correlation between these modalities and assess the feasibility of predicting muscle strain using head worn sensors. We discuss the key research challenges in sensing and predicting muscle fatigue. The results highlight the potential of this approach in real-time ergonomic feedback systems, contributing to the prevention and management of tech neck.

翻译：科技颈是一种因长时间使用电子设备时姿势不良而日益严重的肌肉骨骼问题，具有显著的健康影响。本研究通过整合肌电传感器与头部追踪器的数据，探究头部姿势与上斜方肌肌肉活动之间的关系，以预测肌肉劳损。我们训练了一个回归模型，利用头部运动数据预测肌电包络读数。通过涉及多种姿势的初步实验，我们探索了这些模态之间的相关性，并评估了使用头戴式传感器预测肌肉劳损的可行性。我们讨论了在感知和预测肌肉疲劳方面的关键研究挑战。研究结果凸显了该方法在实时人体工程学反馈系统中的潜力，有助于预防和管理科技颈问题。