Wearable High‐Density MXene‐Bioelectronics for Neuromuscular Diagnostics, Rehabilitation, and Assistive Technologies
Wearable High‐Density MXene‐Bioelectronics for Neuromuscular Diagnostics, Rehabilitation, and Assistive Technologies
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DOI：
10.1002/smtd.202201318
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发表时间：
2022-12
期刊：
影响因子：
12.4
通讯作者：
Raghav Garg;Nicolette Driscoll;S. Shankar;T. Hullfish;E. Anselmino;F. Iberite;Spencer Averbeck;Manini Rana;S. Micera;J. Baxter;Flavia Vitale
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作者：
Raghav Garg;Nicolette Driscoll;S. Shankar;T. Hullfish;E. Anselmino;F. Iberite;Spencer Averbeck;Manini Rana;S. Micera;J. Baxter;Flavia Vitale
高密度表面肌电图（HDsEMG）可实现无创肌肉监测和疾病诊断。当前 HDsEMG 技术的临床转化受到成本、可扩展性有限、可用性低和空间覆盖范围最小的阻碍。在此，本研究提出、验证并展示了通过安全且可扩展的 Ti3C2TX 液相处理制造的干式可穿戴 MXene HDsEMG 阵列 (MXtrodes) 的广泛临床适用性。该制造方案允许轻松定制阵列几何形状以匹配受试者的解剖结构，而无凝胶和最少的皮肤准备增强了可用性和舒适性。 MXtrode 阵列的低阻抗和高电导率允许在真实的临床场景中以比最先进的无线肌电图传感器更高的质量和空间分辨率检测大肌肉群的活动。为了证明 MXtrodes 在神经肌肉诊断和康复方面的临床适用性，显示了在各种任务（从受控收缩到行走）期间整个小腿肌肉群的同步 HDsEMG 和生物力学测绘。最后，展示了通过 MXtrodes 获取的 HDsEMG 与机器学习管道的集成以及对人类步态阶段的准确预测。结果强调了基于 MXene 的可穿戴生物电子学在研究神经肌肉功能和疾病以及精准康复方面的优势和可转化性。
High‐density surface electromyography (HDsEMG) allows noninvasive muscle monitoring and disease diagnosis. Clinical translation of current HDsEMG technologies is hampered by cost, limited scalability, low usability, and minimal spatial coverage. Here, this study presents, validates, and demonstrates the broad clinical applicability of dry wearable MXene HDsEMG arrays (MXtrodes) fabricated from safe and scalable liquid‐phase processing of Ti3C2Tx. The fabrication scheme allows easy customization of array geometry to match subject anatomy, while the gel‐free and minimal skin preparation enhance usability and comfort. The low impedance and high conductivity of the MXtrode arrays allow detection of the activity of large muscle groups at higher quality and spatial resolution than state‐of‐the‐art wireless electromyography sensors, and in realistic clinical scenarios. To demonstrate the clinical applicability of MXtrodes in the context of neuromuscular diagnostics and rehabilitation, simultaneous HDsEMG and biomechanical mapping of muscle groups across the whole calf during various tasks, ranging from controlled contractions to walking is shown. Finally, the integration of HDsEMG acquired with MXtrodes with a machine learning pipeline and the accurate prediction of the phases of human gait are shown. The results underscore the advantages and translatability of MXene‐based wearable bioelectronics for studying neuromuscular function and disease, as well as for precision rehabilitation.