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Population-scale hand tremor analysis via anonymized mouse cursor signals
npj Digital Medicine ( IF 12.4 ) Pub Date : 2019-09-24 , DOI: 10.1038/s41746-019-0171-4
Ryen W. White , Eric Horvitz

Tremors are a common movement disorder with a spectrum of benign and pathological causes, including neurodegenerative disease, alcohol withdrawal, and physical overexertion. Studies of tremors in clinical practice are limited in size and scope and depend on explicit tracking of tremor characteristics by clinicians. Data drawn from small numbers of patients observed in short-duration sessions pose challenges for understanding the nature and distribution of tremors over a large population. Methods are presented to estimate hand tremors based on anonymized computer mouse cursor movement data collected from millions of users of a web search engine. To determine the feasibility of using this signal for the estimation of the prevalence of tremors over a large population, the characteristics of tremor-like movements are computed and compared against user data that can be interpreted as self-reports, the findings of published clinical studies, and a target selection study where participants self-report hand tremors and known causes. The results demonstrate significant alignment between estimated tremors and both self-reports and clinical findings. Those with cursor tremor events are more likely to report tremor-related search interests. Variations in cursor tremor quantity and cursor tremor frequency with demographics mirror those from clinical studies. Distributions of cursor tremor frequencies vary as expected for different medical conditions. Overall, the study finds evidence for the validity of harnessing anonymized mouse cursor motion as a population-scale tremor sensor for epidemiologic studies. Feasible future applications include opt-in services for screening and for monitoring the progression of illness.



中文翻译:

通过匿名鼠标光标信号进行人口规模的手部震颤分析

震颤是一种常见的运动障碍,具有一系列良性和病理性原因,包括神经退行性疾病,戒酒和过度劳累。在临床实践中,对震颤的研究受到大小和范围的限制,并且取决于临床医生对震颤特征的明确跟踪。在短期会议中观察到的少数患者的数据为理解大范围震颤的性质和分布提出了挑战。提出了基于从网络搜索引擎的数百万用户收集的匿名计算机鼠标光标移动数据来估计手震的方法。为了确定使用该信号估算大量人群震颤发生率的可行性,计算震颤样运动的特征,并将其与可以解释为自我报告,已发表的临床研究结果以及目标参与者选择的研究对象进行比较,参与者可以自我报告手部颤抖和已知原因。结果表明,估计的震颤与自我报告和临床发现之间存在显着的一致性。发生光标震颤事件的人更有可能报告与震颤相关的搜索兴趣。人口统计学上的光标震颤数量和光标震颤频率的变化反映了来自临床研究的变化。光标震颤频率的分布随不同医疗条件的变化而变化。总体而言,该研究发现了利用匿名鼠标光标运动作为流行病学研究中的人口规模震颤传感器的有效性的证据。

更新日期:2019-09-25
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