动力数据增强使用时空双分辨器生成对抗网络来预测婴儿爬行的关节角度
Qiliang Xiong1,2, Bo Liu1, Yating Dong1,2
1Department of Biomedical Engineering, Nanchang Hangkong University, Jiangxi 330063, China.
Journal of biomechanical engineering
|November 15, 2025
概括
一个新的时空双分辨器生成对抗网络 (SDGAN) 创建合成婴儿爬行数据. 这种方法显著提高了关节角度预测准确度,用于康复中的外骨控制.
科学领域:
- 机器人技术 机器人技术 机器人技术
- 生物医学工程 生物医学工程
- 数据科学数据科学数据科学
背景情况:
- 产生准确的婴儿爬行运动对于开发外骨控制策略至关重要.
- 由于收集成本和隐私问题,婴儿爬行数据有限,这阻碍了模型的性能.
研究的目的:
- 引入一个新的时空双分辨器生成对抗网络 (SDGAN) 来生成合成婴儿运动数据.
- 评估SDGAN与基准模型的有效性,并评估其对联合角度预测准确性的影响.
主要方法:
- SDGAN生成器使用双重区分器学习空间联合关系和时间动态.
- 将SDGAN与TimeGAN,DAT-GAN和MTS-GAN进行比较,使用詹森-香农分歧.
- 评估了合成与真实数据混合比对基于LSTM的联合角度预测准确度的影响.
主要成果:
- SDGAN实现了最低的平均Jensen-Shannon差异 (0.027),表现优于基准模型.
- 将SDGAN数据以1:1的比例与真实数据相结合,产生了最好的关节角度预测 (MAE为1.36度),改善了40.4%.
- 大多数关节的分布相似度和预测准确度的改善在统计学上是显著的.
结论:
- 可持续发展目标有效地产生现实的婴儿爬行动力学数据,解决数据稀缺问题.
- SDGAN生成的数据显著提高了用于儿科动力学建模的关节角度预测准确度.
- 这种方法对改进数据增强和恢复应用有前途.
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