Division of Translational Neuroscience, Department of Clinical Neurosciences, Hotchkiss Brain Institute, Alberta Children's Hospital Research Institute, Cumming School of Medicine, University of Calgary, 3330 Hospital Drive NW, Calgary, AB, T2N 4N1, Canada.
Department of Neurology, First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China.
J Neural Transm (Vienna). 2019 Aug;126(8):1029-1036. doi: 10.1007/s00702-019-02020-0. Epub 2019 Jun 1.
Objective measurement of walking speed and gait deficits are an important clinical tool in chronic illness management. We previously reported in Parkinson's disease that different types of gait tests can now be implemented and administered in the clinic or at home using Ambulosono smartphone-sensor technology, whereby movement sensing protocols can be standardized under voice instruction. However, a common challenge that remains for such wearable sensor systems is how meaningful data can be extracted from seemingly "noisy" raw sensor data, and do so with a high level of accuracy and efficiency. Here, we describe a novel pattern recognition algorithm for the automated detection of gait-cycle breakdown and freezing episodes. Ambulosono-gait-cycle-breakdown-and-freezing-detection (Free-D) integrates a nonlinear m-dimensional phase-space data extraction method with machine learning and Monte Carlo analysis for model building and pattern generalization. We first trained Free-D using a small number of data samples obtained from thirty participants during freezing of gait tests. We then tested the accuracy of Free-D via Monte Carlo cross-validation. We found Free-D to be remarkably effective at detecting gait-cycle breakdown, with mode error rates of 0% and mean error rates < 5%. We also demonstrate the utility of Free-D by applying it to continuous holdout traces not used for either training or testing, and found it was able to identify gait-cycle breakdown and freezing events of varying duration. These results suggest that advanced artificial intelligence and automation tools can be developed to enhance the quality, efficiency, and the expansion of wearable sensor data processing capabilities to meet market and industry demand.
客观的行走速度和步态缺陷测量是慢性病管理的重要临床工具。我们之前在帕金森病中报告说，现在可以使用 Ambulosono 智能手机传感器技术在诊所或家中实施和管理不同类型的步态测试，通过语音指令可以标准化运动感应协议。然而，对于这种可穿戴传感器系统来说，仍然存在一个共同的挑战，即如何从看似“嘈杂”的原始传感器数据中提取有意义的数据，并具有高精度和高效率。在这里，我们描述了一种用于自动检测步态周期中断和冻结事件的新型模式识别算法。Ambulosono-步态周期中断和冻结检测（Free-D）将非线性 m 维相空间数据提取方法与机器学习和蒙特卡罗分析集成在一起，用于模型构建和模式概括。我们首先使用从三十名参与者在步态冻结测试期间获得的少量数据样本对 Free-D 进行训练。然后，我们通过蒙特卡罗交叉验证测试 Free-D 的准确性。我们发现 Free-D 在检测步态周期中断方面非常有效，模式错误率为 0%，平均错误率<5%。我们还通过将 Free-D 应用于未用于训练或测试的连续保留迹线来证明其效用，并发现它能够识别不同持续时间的步态周期中断和冻结事件。这些结果表明，可以开发先进的人工智能和自动化工具来增强可穿戴传感器数据处理能力的质量、效率和扩展，以满足市场和行业的需求。
