Background Assessment of essential tremor is often done by a trained clinician who observes the limbs during different postures and actions and subsequently rates the tremor. While this method has been shown to be reliable, the inter- and intra-rater reliability and need for training can make the use of this method for symptom progression difficult. Many limitations of clinical rating scales can potentially be overcome by using inertial sensors, but to date many algorithms designed to quantify tremor have key limitations. Methods We propose a novel algorithm to characterize tremor using inertial sensors. It uses a two-stage approach that 1) estimates the tremor frequency of a subject and only quantifies tremor near that range; 2) estimates the tremor amplitude as the portion of signal power above baseline activity during recording, allowing tremor estimation even in the presence of other activity; and 3) estimates tremor amplitude in physical units of translation (cm) and rotation (°), consistent with current tremor rating scales. We validated the algorithm technically using a robotic arm and clinically by comparing algorithm output with data reported by a trained clinician administering a tremor rating scale to a cohort of essential tremor patients. Results Technical validation demonstrated rotational amplitude accuracy better than ±0.2 degrees and position amplitude accuracy better than ±0.1 cm. Clinical validation revealed that both rotation and position components were significantly correlated with tremor rating scale scores. Conclusion We demonstrate that our algorithm can quantify tremor accurately even in the presence of other activities, perhaps providing a step forward for at-home monitoring.

中文翻译：

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使用惯性传感器量化特发性震颤 - 算法验证

背景 对特发性震颤的评估通常由训练有素的临床医生完成，他们观察不同姿势和动作的肢体，然后对震颤进行评级。虽然这种方法已被证明是可靠的，但评估者之间和内部的可靠性以及培训的需要可能使得使用这种方法来治疗症状进展变得困难。使用惯性传感器可以克服临床评定量表的许多局限性，但迄今为止，许多旨在量化震颤的算法都存在关键局限性。方法我们提出了一种使用惯性传感器来表征震颤的新颖算法。它采用两阶段方法：1）估计受试者的震颤频率，并仅量化该范围附近的震颤；2) 将震颤幅度估计为记录期间高于基线活动的信号功率部分，即使存在其他活动也可以进行震颤估计；3) 以平移 (cm) 和旋转 (°) 的物理单位估计震颤幅度，与当前的震颤评级量表一致。我们使用机械臂在技术上验证了该算法，并通过将算法输出与训练有素的临床医生对一组特发性震颤患者进行震颤评级量表报告的数据进行比较，在临床上验证了该算法。结果 技术验证表明，旋转幅度精度优于 ±0.2 度，位置幅度精度优于 ±0.1 厘米。临床验证表明，旋转和位置分量均与震颤评定量表得分显着相关。结论 我们证明，即使存在其他活动，我们的算法也可以准确地量化震颤，这或许为家庭监测迈出了一步。