Krasoulis Agamemnon, Kyranou Iris, Erden Mustapha Suphi, Nazarpour Kianoush, Vijayakumar Sethu
Institute of Perception, Action and Behaviour, School of Informatics, University of Edinburgh, Edinburgh, UK.
Institute for Adaptive and Neural Computation, School of Informatics, University of Edinburgh, Edinburgh, UK.
J Neuroeng Rehabil. 2017 Jul 11;14(1):71. doi: 10.1186/s12984-017-0284-4.
Myoelectric pattern recognition systems can decode movement intention to drive upper-limb prostheses. Despite recent advances in academic research, the commercial adoption of such systems remains low. This limitation is mainly due to the lack of classification robustness and a simultaneous requirement for a large number of electromyogram (EMG) electrodes. We propose to address these two issues by using a multi-modal approach which combines surface electromyography (sEMG) with inertial measurements (IMs) and an appropriate training data collection paradigm. We demonstrate that this can significantly improve classification performance as compared to conventional techniques exclusively based on sEMG signals.
We collected and analyzed a large dataset comprising recordings with 20 able-bodied and two amputee participants executing 40 movements. Additionally, we conducted a novel real-time prosthetic hand control experiment with 11 able-bodied subjects and an amputee by using a state-of-the-art commercial prosthetic hand. A systematic performance comparison was carried out to investigate the potential benefit of incorporating IMs in prosthetic hand control.
The inclusion of IM data improved performance significantly, by increasing classification accuracy (CA) in the offline analysis and improving completion rates (CRs) in the real-time experiment. Our findings were consistent across able-bodied and amputee subjects. Integrating the sEMG electrodes and IM sensors within a single sensor package enabled us to achieve high-level performance by using on average 4-6 sensors.
The results from our experiments suggest that IMs can form an excellent complimentary source signal for upper-limb myoelectric prostheses. We trust that multi-modal control solutions have the potential of improving the usability of upper-extremity prostheses in real-life applications.
肌电模式识别系统能够解码运动意图以驱动上肢假肢。尽管学术研究最近取得了进展，但此类系统的商业应用率仍然很低。这一限制主要是由于缺乏分类稳健性以及同时需要大量肌电图（EMG）电极。我们建议通过使用一种多模态方法来解决这两个问题，该方法将表面肌电图（sEMG）与惯性测量（IM）以及适当的训练数据收集范式相结合。我们证明，与仅基于sEMG信号的传统技术相比，这可以显著提高分类性能。
我们收集并分析了一个大型数据集，该数据集包含20名健全人和2名截肢参与者执行40种动作的记录。此外，我们使用最先进的商业假肢手，对11名健全人和1名截肢者进行了一项新颖的实时假肢手控制实验。进行了系统的性能比较，以研究在假肢手控制中纳入IM的潜在益处。
纳入IM数据显著提高了性能，在离线分析中提高了分类准确率（CA），在实时实验中提高了完成率（CR）。我们的研究结果在健全人和截肢者受试者中都是一致的。将sEMG电极和IM传感器集成在一个单一的传感器包中，使我们能够通过平均使用4 - 6个传感器实现高水平的性能。
我们实验的结果表明，IM可以成为上肢肌电假肢的优秀补充源信号。我们相信多模态控制解决方案有潜力在实际应用中提高上肢假肢的可用性。
