Bardizbanian Berj, Zhu Ziling, Li Jianan, Huang Xinming, Dai Chenyun, Martinez-Luna Carlos, McDonald Benjamin E, Farrell Todd R, Clancy Edward A
Annu Int Conf IEEE Eng Med Biol Soc. 2020 Jul;2020:369-373. doi: 10.1109/EMBC44109.2020.9175675.
Single-use EMG-force models (i.e., a new model is trained each time the electrodes are donned) are used in various areas, including ergonomics assessment, clinical biomechanics, and motor control research. For one degree of freedom (1-DoF) tasks, input-output (black box) models are common. Recently, black box models have expanded to 2-DoF tasks. To facilitate efficient training, we examined parameters of black box model training methods in 2-DoF force-varying, constant-posture tasks consisting of hand open-close combined with one wrist DoF. We found that approximately 40-60 s of training data is best, with progressively higher EMG-force errors occurring for progressively shorter training durations. Surprisingly, 2-DoF models in which the dynamics were universal across all subjects (only channel gain was trained to each subject) generally performed 15-21% better than models in which the complete dynamics were trained to each subject. In summary, lower error EMG-force models can be formed through diligent attention to optimization of these factors.
一次性肌电图-力模型（即每次佩戴电极时都训练一个新模型）被应用于多个领域，包括人体工程学评估、临床生物力学和运动控制研究。对于单自由度（1-DoF）任务，输入-输出（黑箱）模型很常见。最近，黑箱模型已扩展到双自由度任务。为了促进高效训练，我们在由手的开合与一个手腕自由度相结合的双自由度力变、姿势恒定任务中，研究了黑箱模型训练方法的参数。我们发现，大约40-60秒的训练数据是最佳的，训练持续时间越短，肌电图-力误差就会逐渐越高。令人惊讶的是，动力学在所有受试者中通用（仅针对每个受试者训练通道增益）的双自由度模型通常比针对每个受试者训练完整动力学的模型表现好15-21%。总之，通过认真关注这些因素的优化，可以形成误差更低的肌电图-力模型。
