Alazrai Rami, Khalifeh Ala, Alnuman Nasim, Alabed Deena, Mowafi Yaser
Annu Int Conf IEEE Eng Med Biol Soc. 2016 Aug;2016:319-322. doi: 10.1109/EMBC.2016.7590704.
Late development and evolution of high degree-of-freedom (DOF) robotic hands have seen great technological strides to enhance the quality of life for amputated people. A robust hand kinematic estimation mechanisms have shown promising results to control robotic hands that can mimic the human hand functions and perform daily life hand dexterous tasks. In this paper, we propose an ensemble-based regression approach for continuous estimation of wrist and fingers movements from surface Electromyography (sEMG) signals. The proposed approach extracts time-domain features from the sEMG signals, and uses Gradient Boosted Regression Tree (GBRT) ensembles to estimate the kinematics of the wrist and fingers. Furthermore, we propose two different performance evaluation procedures to demonstrate the efficacy of the approach in providing a feasible approach towards accurately estimating hand kinematics.
高度自由度（DOF）机器人手的后期发展与演进在提升截肢者生活质量方面取得了巨大的技术进步。强大的手部运动学估计机制已展现出令人期待的成果，能够控制可模仿人类手部功能并执行日常生活中手部灵巧任务的机器人手。在本文中，我们提出一种基于集成的回归方法，用于从表面肌电图（sEMG）信号中连续估计手腕和手指的运动。所提出的方法从sEMG信号中提取时域特征，并使用梯度提升回归树（GBRT）集成来估计手腕和手指的运动学。此外，我们提出两种不同的性能评估程序，以证明该方法在提供一种准确估计手部运动学的可行方法方面的有效性。
