Deep Learning: Predicting Environments From Short-Time Observations of Postural Balance.
Deep Learning: Predicting Environments From Short-Time Observations of Postural Balance.
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DOI：
10.1109/tbme.2022.3170850
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发表时间：
2022-11
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这项研究引入了一种深度学习方法，以准确预测可能导致姿势稳定性下降的具有挑战性的机械环境。利用双轴机器人平台来模拟各种环境并收集窄站姿和宽站姿期间的压力中心数据。开发了一种卷积神经网络（CNN）来预测给定分段时间序列平衡数据的环境条件。不同的窗口大小进行了检查，以调查其可靠的预测的最小长度。此外，还将所提出的CNN的有效性与传统的机器学习模型进行了比较。然后评估其对低采样数据或更自然的立场数据的适用性。CNN在2.5秒长度的姿势摇摆数据下，整体预测准确率达到94.5%以上，这是传统机器学习无法实现的（ps < 0.05）。将数据长度增加到2.5秒以上，CNN的准确性略有提高，但训练时间大幅增加（增加60%）。重要的是，平均归一化混淆矩阵的结果表明，CNN更能够区分中等环境条件。深度学习也可以在预测环境方面产生相当的性能，即使采样数据少得多或站立姿势改变。CNN消除了特征准备的负担，并在处理短长度数据时准确预测环境。这也表明了潜在的真实的生活中的应用。这项研究有助于可穿戴设备和人类互动机器人（例如，外骨骼和假体）通过预测环境背景和防止潜在的福尔斯。
This study introduces a deep learning approach to accurately predict challenging mechanical environments that possibly cause decreasing postural stability. Dual-axis robotic platforms were utilized to simulate various environments and collect center-of-pressure data during narrow and wide stance. A convolutional neural network (CNN) was developed to predict environmental conditions given segmented time-series balance data. Different window sizes were examined to investigate its minimal length for reliable prediction. Effectiveness of the presented CNN was additionally compared with that of conventional machine learning models. Its applicability with low sampled data or more natural stance data was then evaluated. The CNN achieved above 94.5% in the overall prediction accuracy even with 2.5-second length postural sway data, which cannot be achieved by traditional machine learning (ps < 0.05). Increasing data length beyond 2.5 seconds slightly improved the accuracy of CNN but substantially increased training time (60% longer). Importantly, results from averaged normalized confusion matrices revealed that CNN is much more capable of differentiating the mid-level environmental condition. Deep learning could also produce comparable performance in predicting environments even with much lower sampled data or with standing posture changed. CNN removed the burden of feature preparation and accurately predicted environments when dealing with short-length data. It also indicated potentials to real life applications. This study contributes to the advancement of wearable devices and human interactive robots (e.g., exoskeletons and prostheses) by predicting environmental contexts and preventing potential falls.