Background: Postural instability is one of the most troublesome motor symptoms of Parkinson’s Disease (PD). It impairs patients’ quality of life and results in high risk of falls. The aim of this study is to provide a reliable tool for the automated assessment of postural instability. Methods: Data acquisition was performed on 42 PD patients and 7 young healthy subjects. They were asked to keep a quiet stance position for at least 30 s while wearing a waist-mounted smartphone. A total number of 414 features was extracted from both time and frequency domain, selected based on Pearson’s correlation, and fed to an optimized Support Vector Machine. Results: The implemented model was able to differentiate patients with mild postural instability from those with severe postural instability and from healthy controls, with 100% accuracy. Conclusion: This study demonstrated the feasibility of using inertial sensors embedded in commercial smartphones and proposed a simple protocol for accurate postural instability scoring. This tool can be used for early detection of PD motor signs, disease follow-up and fall prevention.

中文翻译：

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安静姿态下基于智能手机的帕金森氏病患者姿势稳定性评估

背景：姿势不稳是帕金森氏病（PD）最麻烦的运动症状之一。这会损害患者的生活质量，并导致跌倒的高风险。这项研究的目的是为姿势不稳定性的自动评估提供可靠的工具。方法：对42名PD患者和7名年轻健康受试者进行数据采集。要求他们在佩戴腰挂式智能手机时保持安静的姿势至少30 s。从时域和频域中提取了总数为414的特征，基于Pearson的相关性进行选择，并将其输入到优化的支持向量机中。结果：所实施的模型能够以100％的准确度将轻度姿势不稳的患者与严重姿势不稳的患者以及健康对照区分开。结论：这项研究证明了使用嵌入在商用智能手机中的惯性传感器的可行性，并提出了一种简单的协议来进行准确的姿势不稳定性评分。该工具可用于PD运动征兆的早期检测，疾病随访和预防跌倒。