Karrenbach Maxim, Preechayasomboon Pornthep, Sauer Peter, Boe David, Rombokas Eric
Department of Electrical and Computer Engineering, University of Washington, Seattle, WA, United States.
Department of Mechanical Engineering, University of Washington, Seattle, WA, United States.
Front Bioeng Biotechnol. 2022 Dec 15;10:1034672. doi: 10.3389/fbioe.2022.1034672. eCollection 2022.
We anticipate wide adoption of wrist and forearm electomyographic (EMG) interface devices worn daily by the same user. This presents unique challenges that are not yet well addressed in the EMG literature, such as adapting for session-specific differences while learning a longer-term model of the specific user. In this manuscript we present two contributions toward this goal. First, we present the MiSDIREKt (Multi-Session Dynamic Interaction Recordings of EMG and Kinematics) dataset acquired using a novel hardware design. A single participant performed four kinds of hand interaction tasks in virtual reality for 43 distinct sessions over 12 days, totaling 814 min. Second, we analyze this data using a non-linear encoder-decoder for dimensionality reduction in gesture classification. We find that an architecture which recalibrates with a small amount of single session data performs at an accuracy of 79.5% on that session, as opposed to architectures which learn solely from the single session (49.6%) or learn only from the training data (55.2%).
我们预计同一用户每天佩戴的手腕和前臂肌电图（EMG）接口设备将得到广泛应用。这带来了肌电图文献中尚未得到充分解决的独特挑战，例如在学习特定用户的长期模型时适应特定会话的差异。在本手稿中，我们针对这一目标提出了两项贡献。首先，我们展示了使用新型硬件设计获取的MiSDIREKt（肌电图和运动学的多会话动态交互记录）数据集。一名参与者在虚拟现实中进行了四种手部交互任务，在12天内共进行了43个不同的会话，总计814分钟。其次，我们使用非线性编码器 - 解码器对这些数据进行分析，以降低手势分类的维度。我们发现，一种用少量单会话数据重新校准的架构在该会话上的准确率为79.5%，而仅从单会话学习的架构（49.6%）或仅从训练数据学习的架构（55.2%）则不然。
