Adaptive Recalibration of a Prosthetic Leg Neural Control System

假肢神经控制系统的自适应重新校准

• 批准号: 8921846
• 负责人: Levi John Hargrove
• 金额: $ 66.56万
• 依托单位: REHABILITATION INSTITUTE OF CHICAGO D/B/A SHIRLEY RYAN ABILITYLAB
• 依托单位国家: 美国
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-09-06 至 2018-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8921846
• 关键词: Accounting; Address; Algorithms; American; Amputees; Ankle; Artificial Leg; Behavior; Classification; Data; Data Collection; Development; Devices; Disabled Persons; Disease; Electrodes; Electromyography; Entropy; Fostering; Gait; Goals; Health; Human; Incidence; Individual; Knee; Knowledge; Label; Life; Limb Prosthesis; Lower Extremity; Measures; Mechanics; Methods; Microprocessor; Mission; Motor; Muscle Fatigue; Outcome; Patients; Pattern; Pattern Recognition; Persons; Positioning Attribute; Productivity; Prosthesis; Public Health; Quality of life; Rehabilitation Research; Rehabilitation therapy; Research; Robotics; Signal Transduction; Skin; System; Technology; Testing; Time; Training; Translating; Uncertainty; United States; Update; Upper Extremity; Variant; Walking; aging population; arm movement; base; clinical application; design; electric impedance; exoskeleton; functional outcomes; improved; improved mobility; innovation; limb amputation; neuroregulation; powered prosthesis; prosthesis control; public health relevance; relating to nervous system; research study; residual limb; sensor

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.

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障碍疾病发病率的增加，这一数字将继续增长。新兴的机器人腿假肢领域提供了令人兴奋的可能性，以提高这些人的功能结果，但是，必须提高这些设备的控制能力。模式识别算法可用于解码来自假体上的机械传感器的数据以预测Ambassador模式，并且我们的初步数据表明，通过解码肌电图（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，EMG信号随着电极位置、皮肤/电极阻抗或肌肉疲劳而变化，并且仍然不清楚如何将这些信号结合在临床上长期使用可行的鲁棒控制系统内。我们的长期目标是创建一个强大的，直观的， 用于下肢假肢的通用控制系统。我们的目标是在拟议的研究是设计和测试一个自适应框架，可以补偿残肢肌电信号的变化，以控制动力膝关节和踝关节假体。我们的中心假设，初步数据的基础上，是神经控制系统的适应可以监督使用机械传感器数据和先验步态轮廓信息，以更准确地预测amphibia模式。基本原理是，肌电信号提供了重要的神经信息的控制系统，并考虑到非平稳行为的肌电信号随时间的推移将提高系统的鲁棒性。我们将通过以下三个具体目标来测试我们的假设：（1）开发一种步态模式估计器，以根据正确或不正确的控制系统预测来鲁棒地标记先前的截肢模式;（2）确定一种有效的方法来更新模式识别控制系统，以预测截肢模式;（3）在12名经股截肢者中评估实时自适应神经控制系统。根据目标1，我们将开发一个系统，准确地估计什么模式（例如，步行、爬楼梯），用户在先前的步幅期间在其中操作。该数据将被用作“专家”，以提供标签来监督在线自适应控制系统。在目标2下，将监督自适应提供的控制精度的改进与无监督自适应提供的控制精度的改进进行比较。根据目标3，自适应系统将被转换为实时嵌入式系统，并由12名经股截肢者进行测试。这一建议提供了一种创新的方法，将神经控制信息，并消除了一个关键的障碍，使用肌电信号，以改善下肢假肢的控制。所提出的研究是重要的，因为它将导致一个强大的控制系统，将允许更直观的控制动力腿假肢。 这反过来将促进这些设备的使用，并提高成千上万人的流动性。这项技术也可以用来改善动力外太空飞行器的控制，这是另一个重要的新兴研究领域。