Pilacinski Artur, Christ Lukas, Boshoff Marius, Iossifidis Ioannis, Adler Patrick, Miro Michael, Kuhlenkötter Bernd, Klaes Christian
Chair of Neurotechnology, Medical Faculty, Ruhr University Bochum, Bochum, Germany.
Institute Product and Service Engineering, Ruhr University Bochum, Bochum, Germany.
Front Neurorobot. 2024 Sep 24;18:1383089. doi: 10.3389/fnbot.2024.1383089. eCollection 2024.
Human activity recognition (HAR) and brain-machine interface (BMI) are two emerging technologies that can enhance human-robot collaboration (HRC) in domains such as industry or healthcare. HAR uses sensors or cameras to capture and analyze the movements and actions of humans, while BMI uses human brain signals to decode action intentions. Both technologies face challenges impacting accuracy, reliability, and usability. In this article, we review the state-of-the-art techniques and methods for HAR and BMI and highlight their strengths and limitations. We then propose a hybrid framework that fuses HAR and BMI data, which can integrate the complementary information from the brain and body motion signals and improve the performance of human state decoding. We also discuss our hybrid method's potential benefits and implications for HRC.
人类活动识别（HAR）和脑机接口（BMI）是两项新兴技术，它们可以在工业或医疗保健等领域增强人机协作（HRC）。HAR使用传感器或摄像头来捕捉和分析人类的动作和行为，而BMI则使用人脑信号来解码动作意图。这两项技术都面临着影响准确性、可靠性和可用性的挑战。在本文中，我们回顾了HAR和BMI的最新技术和方法，并突出了它们的优势和局限性。然后，我们提出了一个融合HAR和BMI数据的混合框架，该框架可以整合来自大脑和身体运动信号的互补信息，并提高人类状态解码的性能。我们还讨论了我们的混合方法对人机协作的潜在好处和影响。
