KIDS：睡眠案例研究中基于运动学的活动检测和分割
KIDS: kinematics-based (in)activity detection and segmentation in a sleep case study
摘要：
睡眠行为和床上运动包含了关于人的神经生理健康的丰富信息，并与总体健康和生活质量有直接联系。标准的临床实践依赖于多导睡眠图进行睡眠评估;然而，它是侵入性的，在不熟悉的环境中进行，并且需要训练有素的人员。微创传感器技术（如活动记录仪）已经取得了进展，但临床验证引起了对其可靠性和精度的担忧。此外，该领域缺乏广泛接受的算法，所提出的方法从原始信号或特征阈值到数据饥饿的分类模型，其中许多是医务人员不熟悉的。本文提出了一种在线贝叶斯概率框架，用于基于临床意义的关节运动学的客观（中）活动检测和分割，由定制的可穿戴传感器测量。通过基于降维的预处理实现了运动时间序列的直观三维可视化，提供了对临床监测和诊断潜在有用的开箱即用的框架可解释性。该框架分别在姿势变化检测和不活动分割任务中获得了高达99.2%的$F_1$分数和0.96 Pearson相关系数。这项工作为可靠的基于家庭的睡眠期间运动分析铺平了道路，这将有助于以患者为中心的纵向护理计划。
英文摘要：
Sleep behaviour and in-bed movements contain rich information on the neurophysiological health of people, and have a direct link to the general well-being and quality of life. Standard clinical practices rely on polysomnography for sleep assessment; however, it is intrusive, performed in unfamiliar environments and requires trained personnel. Progress has been made on less invasive sensor technologies, such as actigraphy, but clinical validation raises concerns over their reliability and precision. Additionally, the field lacks a widely acceptable algorithm, with proposed approaches ranging from raw signal or feature thresholding to data-hungry classification models, many of which are unfamiliar to medical staff. This paper proposes an online Bayesian probabilistic framework for objective (in)activity detection and segmentation based on clinically meaningful joint kinematics, measured by a custom-made wearable sensor. Intuitive three-dimensional visualisations of kinematic timeseries were accomplished through dimension reduction based preprocessing, offering out-of-the-box framework explainability potentially useful for clinical monitoring and diagnosis. The proposed framework attained up to 99.2\% $F_1$-score and 0.96 Pearson's correlation coefficient in, respectively, the posture change detection and inactivity segmentation tasks. The work paves the way for a reliable home-based analysis of movements during sleep which would serve patient-centred longitudinal care plans.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
