Adaptive Recalibration of a Prosthetic Leg Neural Control System

描述（由申请人提供）：2005年，美国估计有623,000人患有严重下肢截肢；由于人口老龄化和血管性疾病发病率的增加，这一数字将继续增长。机器人假肢的新兴领域提供了令人兴奋的可能性，以提高这些个体的功能结果；然而，控制这些设备的能力必须得到改进。模式识别算法可用于解码假肢上机械传感器的数据以预测行走模式，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，肌电图信号随电极位置、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纳入临床可行的长期使用的鲁棒控制系统中。我们的长期目标是创建健壮的，直观的，和

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.