一个嵌入式系统用于收集和实时分类触摸数据集的嵌入式系统
Olcay Kursun1, Ahmad Patooghy1
1Department of Computer Science, University of Central Arkansas, AR, USA, 72035.
概括
这项研究介绍了一种有效的嵌入式系统,用于实时触觉感知,使机器人和假肢能够准确地分类材料属性. 这种新的方法减少了计算负载,通过更简单的分类器确保了高精度.
科学领域:
- 机器人和人机交互的人机交互
- 嵌入式系统工程 嵌入式系统工程
- 感官神经科学是一种神经科学.
背景情况:
- 实时触觉感知对于机器人,假肢和增强现实中的巧妙操纵至关重要.
- 现有的嵌入式系统在有效地从触觉传感器数据中提取和分类材料特性方面面临着挑战.
- 心理物理维度的广泛范围需要优化信号处理,以嵌入触觉感知.
研究的目的:
- 开发和验证用于实时振动刺激和信号记录的嵌入式系统.
- 实现一个高效的,没有内存的信号特征提取方法,用于实时处理.
- 使用一组传感器在嵌入式系统上实现高精度触觉纹理分类.
主要方法:
- 开发两个嵌入式系统:一个用于振动刺激,一个用于信号记录和分类.
- 离线数据质量验证使用福里埃变换来进行特征提取和机器学习分类器 (SVM,神经网络).
- 实时数据处理的无内存特征提取方法的实施.
主要成果:
- 拟议的无内存特征提取显著降低了计算复杂性.
- 即使使用像随机森林这样的不那么复杂的分类器,也保持了高分类准确度.
- 使用一组传感器证明了低成本,高精度,实时触觉纹理分类的可行性.
结论:
- 开发的嵌入式系统可实现高效的实时触觉感知,用于材料属性分析.
- 没有内存的特征提取方法在嵌入式应用程序中具有计算效率和有效性.
- 这种方法有助于创建具有增强感官反的先进机器人和假肢系统.
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