PDF(Pubmed)
Abstract：
High-density electromyography (HD-EMG) can provide a natural interface to enhance human-computer interaction (HCI). This study aims to demonstrate the capability of a novel HD-EMG forearm sleeve equipped with up to 150 electrodes to capture high-resolution muscle activity, decode complex hand gestures, and estimate continuous hand position via joint angle predictions. Ten able-bodied participants performed 37 hand movements and grasps while EMG was recorded using the HD-EMG sleeve. Simultaneously, an 18-sensor motion capture glove calculated 23 joint angles from the hand and fingers across all movements for training regression models. For classifying across the 37 gestures, our decoding algorithm was able to differentiate between sequential movements with 97.3 ± 0.3 % accuracy calculated on a 100 ms bin-by-bin basis. In a separate mixed dataset consisting of 19 movements randomly interspersed, decoding performance achieved an average bin-wise accuracy of 92.8 ± 0.8 % . When evaluating decoders for use in real-time scenarios, we found that decoders can reliably decode both movements and movement transitions, achieving an average accuracy of 93.3 ± 0.9 % on the sequential set and 88.5 ± 0.9 % on the mixed set. Furthermore, we estimated continuous joint angles from the EMG sleeve data, achieving a R 2 of 0.884 ± 0.003 in the sequential set and 0.750 ± 0.008 in the mixed set. Median absolute error (MAE) was kept below 10° across all joints, with a grand average MAE of 1.8 ± 0 . 04 ∘ and 3.4 ± 0 . 07 ∘ for the sequential and mixed datasets, respectively. We also assessed two algorithm modifications to address specific challenges for EMG-driven HCI applications. To minimize decoder latency, we used a method that accounts for reaction time by dynamically shifting cue labels in time. To reduce training requirements, we show that pretraining models with historical data provided an increase in decoding performance compared with models that were not pretrained when reducing the in-session training data to only one attempt of each movement. The HD-EMG sleeve, combined with sophisticated machine learning algorithms, can be a powerful tool for hand gesture recognition and joint angle estimation. This technology holds significant promise for applications in HCI, such as prosthetics, assistive technology, rehabilitation, and human-robot collaboration.
摘要：
高密度肌电图（HD-EMG）可以提供自然的界面来增强人机交互（HCI）。这项研究旨在证明配备多达150个电极的新型HD-EMG前臂套筒捕获高分辨率肌肉活动的能力。解码复杂的手势，并通过关节角度预测来估计连续的手部位置。十名健全的参与者进行了37次手部动作和抓握，同时使用HD-EMG套筒记录了EMG。同时,18个传感器的动作捕捉手套在所有动作中计算了手和手指的23个关节角度，用于训练回归模型。为了对37个手势进行分类，我们的解码算法能够区分连续运动，以100ms逐bin计算得出的准确率为97.3±0.3％。在一个由随机散布的19个动作组成的单独混合数据集中，解码性能实现了92.8±0.8%的平均逐窗口精度。当评估解码器在实时场景中使用时，我们发现解码器可以可靠地解码运动和运动过渡，在顺序集上达到93.3±0.9％的平均精度，在混合集上达到88.5±0.9％的平均精度。此外，我们从肌电图套筒数据估计了连续的关节角度，在顺序集中实现0.884±0.003的R2，在混合集中实现0.750±0.008。所有关节的中间绝对误差(MAE)保持在10°以下,平均MAE为1.8±0。04○和3.4±0。对于顺序数据集和混合数据集，分别。我们还评估了两种算法修改，以解决EMG驱动的HCI应用的具体挑战。为了最小化解码器延迟，我们使用了一种方法，通过在时间上动态移动提示标签来解释反应时间。为了减少培训需求，我们表明，当将会话训练数据减少到每次运动的一次尝试时，与没有预训练的模型相比，具有历史数据的预训练模型提供了解码性能的提高。HD-EMG套筒,结合复杂的机器学习算法，可以是手势识别和关节角度估计的有力工具。这项技术在HCI中的应用具有重要的前景，例如假肢，辅助技术,康复,以及人机协作。
