Adaptive Recalibration of a Prosthetic Leg Neural Control System

描述(申请人提供)：据估计，2005年美国有623,000人接受大腿截肢；由于人口老龄化和血管疾病发病率的增加，这一数字将继续增长。新兴的机械腿假体领域为改善这些人的功能结果提供了令人兴奋的可能性；然而，必须提高对这些设备的控制能力。模式识别算法可以用来解码假肢上机械传感器的数据以预测步行模式，我们的初步数据表明，通过解码肌电(EMG)信号中的模式来结合神经控制信息可以提高准确性。然而，肌电信号随电极位置、皮肤/电极阻抗或肌肉疲劳而变化，目前尚不清楚如何将这些信号整合到临床上可行的长期使用的强大控制系统中。我们的长期目标是创建强大、直观和 用于下肢假肢的通用型控制系统。在这项拟议的研究中，我们的目标是设计和测试一种自适应框架--可以补偿残存肢体肌电信号的变化--以控制动力膝关节和踝关节假体。我们的中心假设是，基于初步数据，可以使用机械传感器数据和先验步态轮廓信息来监控神经控制系统的适应，以更准确地预测步行模式。其基本原理是，肌电信号为控制系统提供重要的神经信息，并且考虑到肌电信号随时间的非平稳行为将提高系统的鲁棒性。我们将通过以下三个具体目标来验证我们的假设：(1)开发步态模式估计器，以在控制系统预测正确或错误后稳健地标记先前的行走模式；(2)寻找一种有效的方法来更新模式识别控制系统，以预测行走模式；以及(3)评估12名经股截肢患者的实时自适应神经控制系统。在目标1下，我们将开发一个系统，该系统可以准确地估计用户在前一步中的操作模式(例如，行走、爬楼梯)。这些数据将被用作专家，为在线自适应控制系统提供监督的标签。在目标2下，监督适应提供的控制精度改进将与无监督适应提供的改进进行比较。在目标3下，自适应系统将被转换为实时嵌入式系统，并由12名经股截肢患者进行测试。这一建议提供了一种结合神经控制信息的创新方法，并消除了使用肌电信号来改善对下肢假体控制的关键障碍。这项拟议的研究意义重大，因为它将产生一个健壮的控制系统，允许对电动假肢进行更直观的控制。 这反过来将促进这些设备的使用，并改善数万人的移动性。这项技术还可能被转化为改善对动力外骨骼的控制--另一个重要的新兴研究领域。

DESCRIPTION (provided by applicant): An estimated 623,000 people were living with major lower limb amputation in the United States in 2005; this number will continue to grow due to population aging and increasing incidence of dysvascular disease. The emerging field of robotic leg prostheses provides exciting possibilities to enhance functional outcomes for these individuals; however, the ability to control of these devices must be improved. Pattern recognition algorithms may be used to decode data from mechanical sensors on the prosthesis to predict ambulation mode, and our preliminary data shows that incorporating neural control information by decoding patterns in electromyographic (EMG) signals improves accuracy. However, EMG signals vary with electrode position, skin/electrode impedance, or muscle fatigue, and it remains unclear how to incorporate these signals within a robust control system that is clinically viable for long-term use. Our long-term goal is to create robust, intuitive, and generalizable control systems for lower-limb prostheses. Our objective in the proposed research is to design and test an adaptive framework-that can compensate for changes in residual limb EMG signals-to control a powered knee and ankle prosthesis. Our central hypothesis, based on preliminary data, is that adaptation of a neural control system may be supervised using mechanical sensor data and a priori gait profile information to more accurately predict ambulation mode. The rationale is that EMG signals provide important neural information to the control system, and that accounting for non-stationary behavior of EMG signals over time will improve system robustness. We will test our hypothesis through the following three specific aims: (1) Develop a gait-pattern estimator to robustly label prior ambulation modes following correct or incorrect control system predictions; (2) Identify an effective method to update the pattern recognition control system for prediction of ambulation modes; and (3) Evaluate a real-time adaptive neural control system in 12 transfemoral amputees. Under Aim 1, we will develop a system that accurately estimates what mode (e.g., walking, stair climbing) the user was operating within during the previous stride. This data will be used as an 'expert' to provide a label to supervise an online adaptive control system. Under Aim 2, the improvement in control accuracy provided by supervised adaptation will be compared to that provided by unsupervised adaptation. Under Aim 3, the adaptive system will be translated to a real-time embedded system and tested by 12 transfemoral amputees. This proposal provides an innovative approach to incorporating neural control information and removes a critical barrier to using EMG signals to improve control of lower limb prostheses. The proposed research is significant because it will result in a robust control system that will allow more intuitive control of powered leg prostheses. This will in turn facilitate use of these devices and improve mobility for tens of thousands of people. This technology may also be translated to improve control of powered exoskeletons-another important emerging field of research.