在指尖上进行极子平衡:以模型为动机的机器学习预测跌倒的情况
Minakshi Debnath1, Joshua Chang2,3, Keshav Bhandari1
1Department of Computer Science, Texas State University, San Marcos, TX, United States.
Frontiers in physiology
|April 19, 2024
概括
预测像跌倒这样的不良生理事件可以使用深度学习来改进. 转移学习,结合合成和有限的人类数据,显著提高了对人类棍棒跌倒的预测准确度.
科学领域:
- 生理学建模 生理学建模
- 计算神经科学是一种计算神经科学.
- 机器学习是机器学习.
背景情况:
- 生理系统表现出复杂的,潜在的混乱动态,使不良事件的准确预测变得复杂.
- 开发诸如落或心律失常等事件的预测模型具有挑战性,尤其是在有限的训练数据的情况下.
- 棒平衡为研究不良事件预测提供了一个可操作的实验模型.
研究的目的:
- 调查长期短期记忆 (LTSM) 深度学习网络对预测棒跌落的有效性.
- 评估不同的培训策略:合成数据,人体数据和转移学习.
- 为了确定转移学习是否可以提高人类平衡任务中跌落预测的准确性.
主要方法:
- 使用长期短期记忆 (LTSM) 深度学习网络.
- 在合成数据上训练了LTSM模型,来自棒平衡的数学模型.
- 通过运动分析捕获的人类棒平衡数据训练了LTSM模型.
- 通过对合成数据进行预训练和对人类数据进行微调来实现应用转移学习.
主要成果:
- 仅在合成数据上训练的LTSM模型在降落预测方面表现优于在有限的人类数据上训练的模型.
- 转移学习显著提高了LTSM模型预测人类数据下降的能力.
- 转移学习方法在预测真人棒摔倒时达到60%-70%的准确性,提前2.35秒.
结论:
- 模型生成的 (合成) 数据可以有效地与转移学习一起利用,以改善不良生理事件的计算预测.
- 转移学习提供了一个有前途的策略,以提高深度学习模型的预测能力,当真实世界数据稀缺时.
- 这种方法有可能改善不同生理系统中各种不良事件的预测.
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