重建手势,利用从舞蹈动作中提取的协同效应
Parthan Olikkal1, Chris Dollo1, Akshara Ajendla1
1Vinjamuri Lab, Department of Computer Science and Electrical Engineering, University of Maryland Baltimore County, Baltimore, 21250, USA.
Scientific reports
|November 25, 2025
概括
印度古典舞蹈手势 (mudras) 提供了一种改进机器人手掌控的新方法. 这种以舞蹈为灵感的方法提高了手势识别准确度和精度,用于机器人和康复领域的应用.
科学领域:
- 机器人和人机交互的人机交互
- 生物力学和运动控制
- 文化遗产和表演艺术
背景情况:
- 人类手势识别对于机器人,手语和人机交互至关重要.
- 现有的研究主要集中在手势识别上,往往忽视了舞蹈运动的治疗潜力.
- 印度古典舞蹈,特别是Bharatanatyam,利用复杂的手势称为mudras与结构化的运动模式.
研究的目的:
- 引入一种用于理解和重建人类手势的新方法,使用来自Bharatanatyam mudras的运动原体.
- 为了比较mudra衍生的运动协同效应与自然手握所产生的效果.
- 为了展示重建的手势在控制人形机器人的应用.
主要方法:
- 使用高斯模拟的联合角速度提取了手势协同作用,将它们表示为基本运动音节.
- 利用这些音节重建了75种不同的手势,包括美国手语 (ASL) 姿势,自然抓取和传统的mudras.
- 用连续联合映射方法将重建的手势映射到人形机器人 (Mitra) 上.
主要成果:
- 穆德拉衍生的协同效应实现了更高的重建精度:95.78%的自然抓取和92.99%的穆德拉,优于自然抓取衍生的协同效应 (88.92%和82.51%,分别).
- 巴拉塔纳蒂亚姆木德拉的结构性质导致了更强的运动音节,增强了概括性和精确性.
- 成功地将重建的手势映射到五度自由度的机器人手上.
结论:
- 巴拉塔纳蒂亚姆木德拉提供了一个强大的,结构化的学习框架,用于手势的表现和重建.
- 舞蹈启发的结构化学习对提高灵敏度,康复和运动控制有很大的潜力.
- 这种方法为机器人,假肢和康复领域更有效的基于手势的交互模型铺平了道路.
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