Intuitive neuromyoelectric control of a dexterous bionic arm using a modified Kalman filter
Intuitive neuromyoelectric control of a dexterous bionic arm using a modified Kalman filter
复制标题
DOI：
10.1016/j.jneumeth.2019.108462
复制
发表时间：
2020-01-15
影响因子：
3
通讯作者：
Clark, Gregory A.
中科院分区：
文献类型：
--
作者：
George, Jacob A.;Davis, Tyler S.;Clark, Gregory A.
背景：多关节假肢能够实现灵巧的手部运动。然而，临床上可用的控制策略无法为用户提供直观的、独立的、实时的多自由度（dof）控制。新方法：我们详细介绍了一种改进的卡尔曼滤波器（MKF）的使用，为六自由度假肢（如DEKA“LUKE”手臂）提供直观、独立和比例控制。输入特征包括犹他斜电极阵列记录的神经放电率和肌内肌电图（EMG）记录的平均绝对值。特别的修改包括阈值和卡尔曼滤波器输出的非单位增益。结果：神经数据和肌电数据可以有效地结合起来。我们还强调，相对于未修改的卡尔曼滤波器，修改可以优化以显着提高性能。阈值显著减少了意外移动，促进了不同自由度的更独立控制。增益明显大于1，有助于缓解运动启动。最优修改可以在离线时快速确定，并在线上转化为功能改进。使用便携式带回家的系统，参与者进行各种日常生活活动。与现有方法的比较：与模式识别相比，MKF允许用户连续调节其力输出，这对精细灵巧性至关重要。MKF的计算效率也很高，可以在不到五分钟的时间内完成训练。结论：MKF可用于探索长期在家控制灵巧假手的功能和心理益处。
Background: Multi-articulate prostheses are capable of performing dexterous hand movements. However, clinically available control strategies fail to provide users with intuitive, independent and proportional control over multiple degrees of freedom (DOFs) in real-time.New Method: We detail the use of a modified Kalman filter (MKF) to provide intuitive, independent and proportional control over six-DOF prostheses such as the DEKA "LUKE" arm. Input features include neural firing rates recorded from Utah Slanted Electrode Arrays and mean absolute value of intramuscular electromyographic (EMG) recordings. Ad-hoc modifications include thresholds and non-unity gains on the output of a Kalman filter.Results: We demonstrate that both neural and EMG data can be combined effectively. We also highlight that modifications can be optimized to significantly improve performance relative to an unmodified Kalman filter. Thresholds significantly reduced unintended movement and promoted more independent control of the different DOFs. Gains were significantly greater than one and served to ease movement initiation. Optimal modifications can be determined quickly offline and translate to functional improvements online. Using a portable take-home system, participants performed various activities of daily living.Comparison with Existing Methods: In contrast to pattern recognition, the MKF allows users to continuously modulate their force output, which is critical for fine dexterity. The MKF is also computationally efficient and can be trained in less than five minutes.Conclusions: The MKF can be used to explore the functional and psychological benefits associated with long-term, at-home control of dexterous prosthetic hands.