Vaskov Alex, Wallace Dylan, Desai Karan, Laidlaw Ann, Kung Theodore, Gates Deanna, Kemp Stephen, Chestek Cynthia, Cederna Paul
University of Michigan-Ann Arbor.
University of Michigan.
Res Sq. 2025 May 14:rs.3.rs-5989030. doi: 10.21203/rs.3.rs-5989030/v1.
Upper limb loss can negatively impact an individual's ability to perform daily tasks as well as mental health and well-being. Currently available prosthetic control interfaces provide limited prosthetic finger dexterity compared to the complex functions that multi-articulating robotic hands are capable of actuating. A significant barrier is the ability to reliably sense efferent motor action potentials from peripheral nerves when a patient's muscles are lost or damaged due to amputation and injury. In an early-feasibility clinical trial, we implanted four patients with intramuscular electrodes in Regenerative Peripheral Nerve Interfaces (RPNIs). In all patients, the electrodes recorded large-amplitude and stable control signals from RPNIs with a median Signal-to-Noise Ratio (SNR) of 40.6 throughout their study participation. No serious adverse events occurred related to the electrode implantation or the devices themselves. Furthermore, implanting RPNIs provided valuable information to create an algorithm to predict movements previously mediated by lost muscles. These results indicate RPNI-electrode implantation is a repeatable and viable technique to record nerve signals for prosthetic control.
上肢缺失会对个人完成日常任务的能力以及心理健康和幸福感产生负面影响。与多关节机器人手能够实现的复杂功能相比,目前可用的假肢控制接口提供的假肢手指灵活性有限。一个重大障碍是,当患者的肌肉因截肢和受伤而丧失或受损时,能否可靠地感知来自周围神经的传出运动动作电位。在一项早期可行性临床试验中,我们为四名患者在再生周围神经接口(RPNIs)中植入了肌内电极。在所有患者中,电极在整个研究参与期间都记录到了来自RPNIs的大幅度且稳定的控制信号,中位信噪比(SNR)为40.6。未发生与电极植入或设备本身相关的严重不良事件。此外,植入RPNIs为创建一种算法提供了有价值的信息,该算法可预测先前由失去的肌肉介导的运动。这些结果表明,RPNI电极植入是一种可重复且可行的技术,用于记录神经信号以进行假肢控制。