A hybrid Body-Machine Interface integrating signals from muscles and motions
A hybrid Body-Machine Interface integrating signals from muscles and motions
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DOI：
10.1088/1741-2552/ab9b6c
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发表时间：
2020-08-01
影响因子：
4
通讯作者：
Casadio, Maura
中科院分区：
文献类型：
--
作者：
Rizzoglio, Fabio;Pierella, Camilla;Casadio, Maura
目的：身体-机器接口（BoMIs）建立了一种操作各种设备的方法，允许其用户通过利用脊髓损伤或中风后仍然可用的肌肉和运动的冗余来扩展其运动能力的极限。在此，我们考虑了两种类型的信号的集成，即来自惯性测量单元（伊穆斯）的运动信号和用肌电图（EMG）记录的肌肉活动，这两种信号都有助于BoMI. Approach的操作。因此，我们使用基于非线性回归的方法从EMG信号预测IMU，之后将预测的和实际的IMU信号组合成混合控制信号。这种方法的目标是为用户提供在运动和EMG控制之间无缝切换的可能性，使用BoMI作为促进选定肌肉参与的工具。我们测试了三种控制模式，EMG，IMU和混合，在一个队列的15名未受损的参与者的接口。参与者通过引导计算机光标在一组targets.Main结果上练习达到运动，我们发现，所提出的混合控制导致与基于IMU的控制相当的性能，并显着优于仅EMG控制。结果还表明，混合光标控制主要受EMG信号的影响。Significance.我们得出结论，将EMG与IMU信号相结合可能是一种有效的方法，可以针对肌肉激活，同时克服仅EMG控制的局限性。
Objective.Body-Machine Interfaces (BoMIs) establish a way to operate a variety of devices, allowing their users to extend the limits of their motor abilities by exploiting the redundancy of muscles and motions that remain available after spinal cord injury or stroke. Here, we considered the integration of two types of signals, motion signals derived from inertial measurement units (IMUs) and muscle activities recorded with electromyography (EMG), both contributing to the operation of the BoMI.Approach.A direct combination of IMU and EMG signals might result in inefficient control due to the differences in their nature. Accordingly, we used a nonlinear-regression-based approach to predict IMU from EMG signals, after which the predicted and actual IMU signals were combined into a hybrid control signal. The goal of this approach was to provide users with the possibility to switch seamlessly between movement and EMG control, using the BoMI as a tool for promoting the engagement of selected muscles. We tested the interface in three control modalities, EMG-only, IMU-only and hybrid, in a cohort of 15 unimpaired participants. Participants practiced reaching movements by guiding a computer cursor over a set of targets.Main results.We found that the proposed hybrid control led to comparable performance to IMU-based control and significantly outperformed the EMG-only control. Results also indicated that hybrid cursor control was predominantly influenced by EMG signals.Significance.We concluded that combining EMG with IMU signals could be an efficient way to target muscle activations while overcoming the limitations of an EMG-only control.