Assessing inertial measurement unit locations for freezing of gait detection and patient preference.
Assessing inertial measurement unit locations for freezing of gait detection and patient preference.
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DOI：
10.1186/s12984-022-00992-x
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发表时间：
2022-02-13
影响因子：
5.1
通讯作者：
Bronte-Stewart H
中科院分区：
文献类型：
--
作者：
O'Day J;Lee M;Seagers K;Hoffman S;Jih-Schiff A;Kidziński Ł;Delp S;Bronte-Stewart H
步态冻结是帕金森病的一种常见症状，表现为偶发发作，患者的脚突然感到粘在地上。惯性测量单元（imu）有望实现家庭监测和个性化治疗，但对于用于检测步态冻结的imu的数量和位置缺乏共识。本研究的目的是在冻结步态检测性能和患者偏好的背景下评估IMU装置。16名帕金森病患者接受了关于传感器偏好的调查。来自7名帕金森病患者的原始IMU数据，佩戴了多达11个传感器，用于训练卷积神经网络来检测步态冻结。使用来自不同传感器集的数据训练的模型进行技术性能评估；确定最佳技术组和最小IMU组。通过比较模型和人类决定的冻结时间百分比和冻结事件数量来评估临床效用。最佳技术组包括三个imu（腰椎和双脚踝，AUROC = 0.83），均被评为高度可穿戴。最小IMU组包括单个踝关节IMU （AUROC = 0.80）。在最佳技术集和最小IMU集的冻结时间百分比（ICC = 0.93, 0.89）和冻结事件数（ICC = 0.95, 0.86）上，这些模型与人类评分者之间的相关性分别为良好到优异。几个由三个或更少的IMU组成的IMU组在技术性能和耐磨性方面都得到了很高的评价，而更多的IMU不一定在FOG检测中表现更好。我们公开分享我们的数据和软件，以进一步开发和采用通用的开源模型，该模型使用原始信号和标准传感器集，用于在家监测步态冻结。在线版本包含补充材料，可在10.1186/s12984-022-00992-x获得。
Freezing of gait, a common symptom of Parkinson’s disease, presents as sporadic episodes in which an individual’s feet suddenly feel stuck to the ground. Inertial measurement units (IMUs) promise to enable at-home monitoring and personalization of therapy, but there is a lack of consensus on the number and location of IMUs for detecting freezing of gait. The purpose of this study was to assess IMU sets in the context of both freezing of gait detection performance and patient preference. Sixteen people with Parkinson’s disease were surveyed about sensor preferences. Raw IMU data from seven people with Parkinson’s disease, wearing up to eleven sensors, were used to train convolutional neural networks to detect freezing of gait. Models trained with data from different sensor sets were assessed for technical performance; a best technical set and minimal IMU set were identified. Clinical utility was assessed by comparing model- and human-rater-determined percent time freezing and number of freezing events. The best technical set consisted of three IMUs (lumbar and both ankles, AUROC = 0.83), all of which were rated highly wearable. The minimal IMU set consisted of a single ankle IMU (AUROC = 0.80). Correlations between these models and human raters were good to excellent for percent time freezing (ICC = 0.93, 0.89) and number of freezing events (ICC = 0.95, 0.86) for the best technical set and minimal IMU set, respectively. Several IMU sets consisting of three IMUs or fewer were highly rated for both technical performance and wearability, and more IMUs did not necessarily perform better in FOG detection. We openly share our data and software to further the development and adoption of a general, open-source model that uses raw signals and a standard sensor set for at-home monitoring of freezing of gait. The online version contains supplementary material available at 10.1186/s12984-022-00992-x.
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影响因子：
3.4
作者：
Bikias T;Iakovakis D;Hadjidimitriou S;Charisis V;Hadjileontiadis LJ
通讯作者：
Hadjileontiadis LJ
影响因子：
5.1
作者：
Mancini M;Shah VV;Stuart S;Curtze C;Horak FB;Safarpour D;Nutt JG
通讯作者：
Nutt JG
影响因子：
4.1
作者：
Morris, Tiffany R.;Cho, Catherine;Moore, Steven T.
通讯作者：
Moore, Steven T.
影响因子：
2
作者：
Mancini, Martina;El-Gohary, Mahmoud;Horak, Fay B.
通讯作者：
Horak, Fay B.
影响因子：
3.7
作者：
O'Day, Johanna;Syrkin-Nikolau, Judy;Bronte-Stewart, Helen
通讯作者：
Bronte-Stewart, Helen