Rizzoglio Fabio, Pierella Camilla, De Santis Dalia, Mussa-Ivaldi Ferdinando, Casadio Maura
Department of Informatics, Bioengineering, Robotics and Systems Engineering, University of Genoa, 16145, Genoa, Italy. Department of Physiology, Feinberg School of Medicine, Northwestern University, Chicago, IL 60611, United States of America. Shirley Ryan Ability Lab, Chicago, IL 60611, United States of America. Author to whom any correspondence should be addressed.
J Neural Eng. 2020 Jul 13;17(4):046004. doi: 10.1088/1741-2552/ab9b6c.
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.
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.
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.
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.
人体-机器接口（BoMIs）建立了一种操作各种设备的方法，使使用者能够通过利用脊髓损伤或中风后仍可用的肌肉和运动冗余来扩展其运动能力极限。在此，我们考虑整合两种信号，即来自惯性测量单元（IMUs）的运动信号和通过肌电图（EMG）记录的肌肉活动，这两种信号都有助于BoMI的操作。
由于IMU和EMG信号性质不同，直接组合它们可能会导致控制效率低下。因此，我们采用基于非线性回归的方法从EMG信号预测IMU信号，然后将预测的和实际的IMU信号组合成一个混合控制信号。这种方法的目标是为用户提供在运动控制和EMG控制之间无缝切换的可能性，将BoMI作为促进选定肌肉参与的工具。我们在15名未受损参与者的队列中测试了该接口的三种控制模式，即仅EMG模式、仅IMU模式和混合模式。参与者通过在一组目标上引导计算机光标来练习伸手动作。
我们发现，所提出的混合控制产生的性能与基于IMU的控制相当，并且明显优于仅EMG控制。结果还表明，混合光标控制主要受EMG信号影响。
我们得出结论，将EMG与IMU信号相结合可能是一种有效靶向肌肉激活的方法，同时克服了仅EMG控制的局限性。
