Department of Mechanical Engineering and Construction, Universitat Jaume I, E12071 Castellón, Spain.
Sensors (Basel). 2021 Apr 26;21(9):3035. doi: 10.3390/s21093035.
The role of the hand is crucial for the performance of activities of daily living, thereby ensuring a full and autonomous life. Its motion is controlled by a complex musculoskeletal system of approximately 38 muscles. Therefore, measuring and interpreting the muscle activation signals that drive hand motion is of great importance in many scientific domains, such as neuroscience, rehabilitation, physiotherapy, robotics, prosthetics, and biomechanics. Electromyography (EMG) can be used to carry out the neuromuscular characterization, but it is cumbersome because of the complexity of the musculoskeletal system of the forearm and hand. This paper reviews the main studies in which EMG has been applied to characterize the muscle activity of the forearm and hand during activities of daily living, with special attention to muscle synergies, which are thought to be used by the nervous system to simplify the control of the numerous muscles by actuating them in task-relevant subgroups. The state of the art of the current results are presented, which may help to guide and foster progress in many scientific domains. Furthermore, the most important challenges and open issues are identified in order to achieve a better understanding of human hand behavior, improve rehabilitation protocols, more intuitive control of prostheses, and more realistic biomechanical models.
手的作用对于日常生活活动的执行至关重要，从而确保了全面和自主的生活。它的运动由大约 38 块肌肉组成的复杂肌肉骨骼系统控制。因此，测量和解释驱动手部运动的肌肉激活信号在许多科学领域都非常重要，如神经科学、康复、物理治疗、机器人技术、假肢和生物力学。肌电图（EMG）可用于进行神经肌肉特征描述，但由于前臂和手部肌肉骨骼系统的复杂性，使用起来很麻烦。本文回顾了主要的研究，这些研究将肌电图应用于日常生活活动中对手部和前臂肌肉活动的特征描述，特别关注肌肉协同作用，人们认为神经系统通过在与任务相关的子组中激活它们来简化对众多肌肉的控制，从而使用肌肉协同作用。介绍了当前研究结果的最新进展，这可能有助于指导和促进许多科学领域的进展。此外，还确定了最重要的挑战和未解决的问题，以更好地理解人手行为，改进康复方案，实现更直观的假肢控制，以及建立更逼真的生物力学模型。
