Cho Selena Y, Dibble Leland E, Fino Peter C
Mechanical Engineering Department, University of Utah, Salt Lake City, UT 84112.
Department of Physical Therapy and Athletic Training, University of Utah, Salt Lake City, UT 84112.
bioRxiv. 2025 May 4:2025.04.29.650670. doi: 10.1101/2025.04.29.650670.
Wearable devices offer objective mobility metrics for continuous monitoring but often focus on traditional measures like step count or gait speed. Other quantitative metrics such as head kinematics may provide valuable insights into mobility, balance, and sensory integration, given the head's central role in coordinating vestibular, ocular, and postural control. Yet, basic knowledge about capturing daily living head turns, including participant compliance, algorithms, normative data, and reliability, is not yet established. This study aimed to resolve this knowledge gap by capturing head and trunk movement kinematics over a 7-day period and to establish normative data in healthy adults. Participants (n = 24) wore head-mounted sensors for an average of 16.38 hours per day (SD = 4.43), completing 5,163 (SD = 1,466) head turns daily, with 72% occurring independently of trunk motion. Head turn amplitude (M = 58.18°, SD = 4.26°) was comparable to lumbar turns, while peak velocity was higher for head turns (M = 104.49°/s, SD = 12.08°/s). By the second day, all head turn metrics achieved excellent reliability (ICC > 0.9), supporting the feasibility of multi-day monitoring. Additionally, we examined the relationship between head motion and other mobility metrics and established recommendations for implementing similar protocols for capturing future studies, including the minimum number of days required for reliable data collection. Findings from this study provide a foundation for future multi-day continuous monitoring of head kinematics in both healthy and clinical populations.
可穿戴设备能够提供客观的活动指标以进行持续监测，但通常侧重于步数或步速等传统指标。鉴于头部在协调前庭、眼球和姿势控制方面的核心作用，其他定量指标，如头部运动学指标，可能会为活动能力、平衡能力和感觉统合提供有价值的见解。然而，关于捕捉日常生活中头部转动的基础知识，包括参与者的依从性、算法、标准数据和可靠性，尚未确立。本研究旨在通过在7天内捕捉头部和躯干运动学指标来填补这一知识空白，并建立健康成年人的标准数据。参与者（n = 24）每天平均佩戴头戴式传感器16.38小时（标准差 = 4.43），每天完成5163次（标准差 = 1466）头部转动，其中72%的转动独立于躯干运动。头部转动幅度（中位数 = 58.18°，标准差 = 4.26°）与腰部转动幅度相当，而头部转动的峰值速度更高（中位数 = 104.49°/秒，标准差 = 12.08°/秒）。到第二天，所有头部转动指标都达到了极佳的可靠性（组内相关系数>0.9），支持了多天监测的可行性。此外，我们研究了头部运动与其他活动指标之间的关系，并为实施类似方案以开展未来研究制定了建议，包括可靠数据收集所需的最少天数。本研究结果为未来在健康人群和临床人群中对头部运动学进行多天连续监测奠定了基础。
