User-driven Retrospectively Supervised Classification Updating (RESCU) system for robust upper limb prosthesis control

用户驱动的回顾性监督分类更新 (RESCU) 系统，用于稳健的上肢假肢控制

基本信息

批准号：
10078697
负责人：
Rahul Reddy Kaliki
金额：
$ 6.41万
依托单位：
INFINITE BIOMEDICAL TECHNOLOGIES, LLC
依托单位国家：
美国
项目类别：
财政年份：
2020
资助国家：
美国
起止时间：
2020-05-01 至 2021-04-30
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10078697
关键词：
Activities of Daily Living Adoption Algorithms Amputees Area Artificial Intelligence Award Calibration Certification Classification Clinical Clinical Research Clinical assessments Communication Consumption Data Development Devices Electromyography Evaluation Focus Groups Freedom Goals Hand Individual Intuition Joints Label Limb Prosthesis Machine Learning Measures Methods Outcome Ownership Parents Patient Self-Report Pattern Recognition Performance Phase Prosthesis Research Signal Transduction Small Business Innovation Research Grant Supervision Surface Surveys System Systems Integration Techniques Technology Testing Time Training Universities Update Upper Extremity Validation Work adaptive learning base clinical translation empowerment functional improvement improved innovation intelligent algorithm learning algorithm myoelectric control novel operation programs prospective prosthesis control satisfaction signal processing smart watch verification and validation

项目摘要

ABSTRACT Approximately 41,000 individuals live with upper-limb loss (loss of at least one hand) in the US. Fortunately, prosthetic devices have advanced considerably in the past decades with the development of dexterous, anthropomorphic hands. However, potentially the most promising used control strategy, myoelectric control, lacks a correspondingly high-level of performance and hence the use of dexterous hands remains highly limited. The need for a complete overhaul in upper limb prosthesis control is well highlighted by the abandonment rates of myoelectric devices, which can reach up to 40% in the case of trans-humeral amputees. The area of research that has received the most focus over the past decade has been “pattern recognition,” which is a signal processing based control method that uses multi-channel surface electromyography as the control input. While pattern recognition provides intuitive operation of multiple prosthetic degrees of freedom, it lacks robustness and requires frequent, often daily calibration. Thus, it has not yet achieved the desired clinical acceptance. Our team proposes clinical translation of a novel highly adaptive upper limb prosthesis control system that incorporates two major advances: 1) machine learning (robust classification by implementing a non-boundary based algorithm), and 2) training by retrospectively incorporating user data from activities of daily living (ADL). The proposed system will enable machine intelligence with user input for prosthesis control. Our work is organized as follows: Phase I: (a) First, we will implement a fundamentally new machine intelligence technique, Extreme Learning Machine with Adaptive Sparse Representation Classification (EASRC), that is more resilient to untrained noisy conditions that users may encounter in the real-world and requires less data than traditional myoelectric signal processing. (b) In parallel, we will implement an adaptive learning algorithm, Nessa, which allows users to relabel misclassified data recorded during use and then update the EASRC classifier to adapt to any major extrinsic or intrinsic changes in the signals. Taken together, EASRC and Nessa comprise the Retrospectively Supervised Classification Updating (RESCU) system. Once, the RESCU implementation is complete, we will optimize the system through a joint effort with Johns Hopkins University, and complete an iterative benchtop RESCU evaluation with a focus group of 3 amputee subjects and their prosthetists. Phase II: Verification and validation of RESCU will be completed, culminating in third-party validation testing and certification. Finally, we will complete a clinical assessment including self-reporting subjective measures, and real-world usage metrics in a long-term clinical study.

抽象的在美国，大约有41,000名患有上限损失（至少一只手损失）的人。幸运的是，在过去的几十年中，随着灵巧，拟人手。但是，可能是最有前途的控制策略，肌电控制，缺乏相应高级的性能，因此灵巧的手仍然很高有限的。需要在上肢假体控制中进行全面大修的需求很好地强调了肌电设备的遗弃率，在跨性别的情况下最多可达到40％截肢者。在过去十年中，人们最集中于研究的领域是“模式识别，“这是一种基于信号处理的控制方法，使用多通道表面肌电图作为对照输入。虽然模式识别提供了多个的直观操作假肢的自由度，它缺乏稳健性，并且经常需要每天的校准。那就是尚未实现所需的临床接受。我们的团队提议对新型高度适应性上肢假体控制系统进行临床翻译结合了两个主要进步：1）机器学习（通过实施非边界的鲁棒分类基于算法）和2）回顾性培训，从日常生活活动（ADL）中纳入用户数据。提出的系统将通过用户输入来实现机器智能以进行假体控制。我们的工作是组织如下：第一阶段：（a）首先，我们将实施一种从根本上实施新的机器智能技术，极限学习具有自适应稀疏表示分类（EASRC）的机器，对未经训练的机器更具弹性用户可能在现实世界中遇到的噪音条件，并且比传统肌电所需的数据更少信号处理。（b）并行，我们将实施一种自适应学习算法Nessa，该算法允许用户在使用过程中记录的Relabel错误分类数据，然后更新EASRC分类器以适应任何信号的主要外部或内在变化。总之，EASRC和Nessa建立了追溯性监督分类更新（RESCU）系统。一旦完成了RESCU的实施，我们将通过与Johns的共同努力来优化系统霍普金斯大学，并与3个截肢者组成的焦点小组完成迭代台式救援评估主题及其假肢。第二阶段：将完成RESCU的验证和验证，最终在第三方验证测试中和认证。最后，我们将完成包括自我报告的主观措施，包括的临床评估，以及一项长期临床研究中的现实使用指标。