Department of Biomedical Engineering, Faculty of Engineering, Lund University, 223 63 Lund, Sweden.
Department of Hand Surgery, Institute of Clinical Sciences, Sahlgrenska Academy, Sahlgrenska University Hospital, University of Gothenburg, 402 33 Gothenburg, Sweden.
Sensors (Basel). 2022 Jul 5;22(13):5054. doi: 10.3390/s22135054.
Although seemingly effortless, the control of the human hand is backed by an elaborate neuro-muscular mechanism. The end result is typically a smooth action with the precise positioning of the joints of the hand and an exerted force that can be modulated to enable precise interaction with the surroundings. Unfortunately, even the most sophisticated technology cannot replace such a comprehensive role but can offer only basic hand functionalities. This issue arises from the drawbacks of the prosthetic hand control strategies that commonly rely on surface EMG signals that contain a high level of noise, thus limiting accurate and robust multi-joint movement estimation. The use of intramuscular EMG results in higher quality signals which, in turn, lead to an improvement in prosthetic control performance. Here, we present the evaluation of fourteen common/well-known algorithms (mean absolute value, variance, slope sign change, zero crossing, Willison amplitude, waveform length, signal envelope, total signal energy, Teager energy in the time domain, Teager energy in the frequency domain, modified Teager energy, mean of signal frequencies, median of signal frequencies, and firing rate) for the direct and proportional control of a prosthetic hand. The method involves the estimation of the forces generated in the hand by using different algorithms applied to iEMG signals from our recently published database, and comparing them to the measured forces (ground truth). The results presented in this paper are intended to be used as a baseline performance metric for more advanced algorithms that will be made and tested using the same database.
虽然看似毫不费力，但人类手部的控制是由精细的神经肌肉机制支持的。最终的结果通常是手部关节的精确定位和施加的力的平稳动作，这些力可以进行调节，以实现与周围环境的精确交互。不幸的是，即使是最先进的技术也无法替代这种全面的作用，而只能提供基本的手部功能。这个问题源于假肢手控制策略的缺点，这些策略通常依赖于表面肌电图信号，这些信号包含高水平的噪声，从而限制了精确和稳健的多关节运动估计。使用肌内肌电图会产生更高质量的信号，从而提高假肢控制性能。在这里，我们评估了 14 种常见/知名算法（绝对值、方差、斜率符号变化、过零、Willison 幅度、波形长度、信号包络、总信号能量、时域中的 Teager 能量、频域中的 Teager 能量、改进的 Teager 能量、信号频率的平均值、信号频率的中位数和发射率）用于假肢手的直接和比例控制。该方法涉及使用不同算法对手部产生的力进行估计，这些算法应用于我们最近发布的数据库中的 iEMG 信号，并将其与测量的力（地面真实）进行比较。本文中呈现的结果旨在用作更先进的算法的基线性能指标，这些算法将使用相同的数据库进行制作和测试。
