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| Main Authors: | , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2503.12762 |
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| _version_ | 1866915200582549504 |
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| author | Chhaglani, Bhawana Seefeldt, Alan |
| author_facet | Chhaglani, Bhawana Seefeldt, Alan |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_12762 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | NeckCheck: Predicting Neck Strain using Head Tracker Sensors Chhaglani, Bhawana Seefeldt, Alan Human-Computer Interaction 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. |
| title | NeckCheck: Predicting Neck Strain using Head Tracker Sensors |
| topic | Human-Computer Interaction |
| url | https://arxiv.org/abs/2503.12762 |