Assistive Technology Laboratory, Faculty of Electrical Engineering, Federal University of Uberlandia, Uberlandia 38400-902, Brazil.
Department of Applied Physical Therapy, Federal University of Triangulo Mineiro, Uberaba 38065-430, Brazil.
Sensors (Basel). 2024 Sep 30;24(19):6362. doi: 10.3390/s24196362.
Muscle tone is defined as the resistance to passive stretch, but this definition is often criticized for its ambiguity since some suggest it is related to a state of preparation for movement. Muscle tone is primarily regulated by the central nervous system, and individuals with neurological disorders may lose the ability to control normal tone and can exhibit abnormalities. Currently, these abnormalities are mostly evaluated using subjective scales, highlighting a lack of objective assessment methods in the literature. This study aimed to use surface electromyography (sEMG) and machine learning (ML) for the objective classification and characterization of the full spectrum of muscle tone in the upper limb. Data were collected from thirty-nine individuals, including spastic, healthy, hypotonic and rigid subjects. All of the classifiers applied achieved high accuracy, with the best reaching 96.12%, in differentiating muscle tone. These results underscore the potential of the proposed methodology as a more reliable and quantitative method for evaluating muscle tone abnormalities, aiming to address the limitations of traditional subjective assessments. Additionally, the main features impacting the classifiers' performance were identified, which can be utilized in future research and in the development of devices that can be used in clinical practice.
肌肉张力定义为对被动拉伸的抵抗力，但这个定义常常因其模糊性而受到批评，因为一些人认为它与运动准备状态有关。肌肉张力主要由中枢神经系统调节，神经功能障碍患者可能失去控制正常张力的能力，并表现出异常。目前，这些异常主要使用主观量表进行评估，这表明文献中缺乏客观评估方法。本研究旨在使用表面肌电图（sEMG）和机器学习（ML）对上肢肌肉张力的全谱进行客观分类和特征描述。数据来自 39 名个体，包括痉挛、健康、张力减退和僵硬的受试者。应用的所有分类器都实现了很高的准确性，最好的达到了 96.12%，能够区分肌肉张力。这些结果强调了所提出的方法作为一种更可靠和定量的评估肌肉张力异常的方法的潜力，旨在解决传统主观评估的局限性。此外，还确定了影响分类器性能的主要特征，这些特征可用于未来的研究和开发可用于临床实践的设备。
