手套网络:通过多感应数据和深度学习方法增强掌握分类
Subhash Pratap1,2, Jyotindra Narayan3,4, Yoshiyuki Hatta2
1Department of Mechanical Engineering, Indian Institute of Technology Guwahati, Guwahati 781039, India.
Sensors (Basel, Switzerland)
|July 13, 2024
概括
本研究介绍了 Glove-Net,这是一种混合深度学习模型,用于使用多传感器数据手套进行掌握分类. 该模型有效地结合了手指姿势和力量数据,在人机交互中实现了优越的掌握识别.
科学领域:
- 机器人和人机交互的人机交互
- 机器学习和模式识别.
- 生物力学和人类运动分析
背景情况:
- 掌握分类对于人机交互,机器人,假肢和康复至关重要.
- 现有的方法往往依赖于单一的模式,限制了把握动态捕获.
- 需要先进的模型,整合多感官数据进行全面的掌握分析.
研究的目的:
- 引入一种使用多感应数据手套进行掌握分类的新方法.
- 提出和评估Glove-Net,一种混合CNN-BiLSTM架构用于掌握模式识别.
- 评估多式模式掌握分类与单式模式方法相比的性能.
主要方法:
- 采集了10名参与者的掌握数据,使用多感应数据手套捕捉YCB对象集的手指曲角度和手指尖力.
- 开发并训练了一种混合卷积神经网络 (CNN) 和双向长短记忆 (BiLSTM) 网络,称为Glove-Net.
- 使用掌握姿势数据,掌握力数据和综合多式联络数据评估分类性能.
主要成果:
- 混合CNN-BiLSTM手套网实现了最高的测试准确率:90.83% (姿势),73.12% (力) 和98.75% (组合).
- 单模CNN实现了88.09% (姿势) 和69.38% (力),而LSTM实现了86.02% (姿势) 和70.52% (力).
- 在所有评估的模型中,组合的多式联运数据显著优于单式联运.
结论:
- 与单模方法相比,多模态掌握分类显著提高了识别准确性.
- 拟议的Glove-Net架构有效地利用来自多传感器数据的时空特征,以实现精确的掌握识别.
- 这项研究推进了人机交互能力,在先进的机器人和辅助技术中具有潜在的应用.
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