Huang Tinghuai, Li Meng, Huang Jianwei
Laboratory of Laser Sports Medicine, South China Normal University, Guangzhou, Guangdong, China.
Department of Gastroenterology, The Fifth Affiliated Hospital of Guangzhou Medical University, Guangzhou Medical University, Guangzhou, Guangdong, China.
Front Aging Neurosci. 2023 Feb 15;15:1119956. doi: 10.3389/fnagi.2023.1119956. eCollection 2023.
The occurrence of freezing of gait (FOG) is often observed in moderate to last-stage Parkinson's disease (PD), leading to a high risk of falls. The emergence of the wearable device has offered the possibility of FOG detection and falls of patients with PD allowing high validation in a low-cost way.
This systematic review seeks to provide a comprehensive overview of existing literature to establish the forefront of sensors type, placement and algorithm to detect FOG and falls among patients with PD.
Two electronic databases were screened by title and abstract to summarize the state of art on FOG and fall detection with any wearable technology among patients with PD. To be eligible for inclusion, papers were required to be full-text articles published in English, and the last search was completed on September 26, 2022. Studies were excluded if they; (i) only examined cueing function for FOG, (ii) only used non-wearable devices to detect or predict FOG or falls, and (iii) did not provide sufficient details about the study design and results. A total of 1,748 articles were retrieved from two databases. However, only 75 articles were deemed to meet the inclusion criteria according to the title, abstract and full-text reviewed. Variable was extracted from chosen research, including authorship, details of the experimental object, type of sensor, device location, activities, year of publication, evaluation in real-time, the algorithm and detection performance.
A total of 72 on FOG detection and 3 on fall detection were selected for data extraction. There were wide varieties of the studied population (from 1 to 131), type of sensor, placement and algorithm. The thigh and ankle were the most popular device location, and the combination of accelerometer and gyroscope was the most frequently used inertial measurement unit (IMU). Furthermore, 41.3% of the studies used the dataset as a resource to examine the validity of their algorithm. The results also showed that increasingly complex machine-learning algorithms had become the trend in FOG and fall detection.
These data support the application of the wearable device to access FOG and falls among patients with PD and controls. Machine learning algorithms and multiple types of sensors have become the recent trend in this field. Future work should consider an adequate sample size, and the experiment should be performed in a free-living environment. Moreover, a consensus on provoking FOG/fall, methods of assessing validity and algorithm are necessary. PROSPERO, identifier CRD42022370911.
步态冻结（FOG）现象常在中度至晚期帕金森病（PD）患者中出现，导致跌倒风险增加。可穿戴设备的出现为检测PD患者的FOG及跌倒情况提供了可能，且能以低成本方式进行高度验证。
本系统评价旨在全面综述现有文献，以确立用于检测PD患者FOG及跌倒的传感器类型、放置位置和算法的前沿情况。
通过标题和摘要筛选两个电子数据库，以总结使用任何可穿戴技术检测PD患者FOG及跌倒的研究现状。纳入标准要求论文为英文发表的全文文章，最后一次检索于2022年9月26日完成。若研究存在以下情况则被排除：（i）仅研究FOG的提示功能；（ii）仅使用非可穿戴设备检测或预测FOG或跌倒；（iii）未提供关于研究设计和结果的足够详细信息。从两个数据库共检索到1748篇文章。然而，根据标题、摘要和全文评审，仅有75篇文章被认为符合纳入标准。从选定的研究中提取变量，包括作者、实验对象细节、传感器类型、设备位置、活动、发表年份、实时评估、算法和检测性能。
共选择72项关于FOG检测的研究和3项关于跌倒检测的研究进行数据提取。研究人群（从1至131人）、传感器类型、放置位置和算法种类繁多。大腿和脚踝是最常用的设备放置位置，加速度计和陀螺仪的组合是最常使用的惯性测量单元（IMU）。此外，41.3%的研究使用数据集作为资源来检验其算法的有效性。结果还表明，越来越复杂的机器学习算法已成为FOG和跌倒检测的趋势。
这些数据支持可穿戴设备在检测PD患者的FOG及跌倒情况中的应用。机器学习算法和多种类型的传感器已成为该领域的最新趋势。未来的工作应考虑足够的样本量，且实验应在自由生活环境中进行。此外，对于引发FOG/跌倒的因素、评估有效性的方法和算法达成共识是必要的。国际系统评价前瞻性注册库，标识符CRD42022370911。
