Among movement disorders, essential tremor is by far the most common, as much as eight times more prevalent than Parkinson’s disease. Although these two conditions differ in their presentation and course, clinicians do not always recognize them, leading to common misdiagnoses. Proper and early diagnosis is important for receiving the right treatment and support. In this paper, the development of a portable and reliable tremor classification system based on a wearable device, enabling clinicians to differentiate between essential tremor and Parkinson’s disease-associated one, is reported. Inertial data were collected from subjects with a well-established diagnosis of tremor, and analyzed to extract different sets of relevant spectral features. Supervised learning methods were then applied to build several classification models, among which the best ones achieved an average accuracy above 90%. Results encourage the use of wearable technology as effective and affordable tools to support clinicians.

中文翻译：

---

基于低功率可穿戴设备的原发性震颤和帕金森氏震颤的分类

在运动障碍中，到目前为止，原发性震颤是最常见的，是帕金森氏病的八倍。尽管这两种情况的表现和病程不同，但临床医生并不总是能识别出它们，从而导致常见的误诊。正确和早诊断对于获得正确的治疗和支持很重要。在本文中，报道了一种基于可穿戴设备的便携式可靠震颤分类系统的开发，该系统使临床医生能够区分原发性震颤和帕金森氏病相关的震颤。从具有公认的震颤诊断的受试者中收集惯性数据，并进行分析以提取不同组的相关光谱特征。然后，采用有监督的学习方法来建立几个分类模型，其中最好的达到90％以上的平均准确度。结果鼓励将可穿戴技术用作支持临床医生的有效且负担得起的工具。