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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：
10013405
负责人：
Rahul Reddy Kaliki
金额：
$ 73.56万
依托单位：
INFINITE BIOMEDICAL TECHNOLOGIES, LLC
依托单位国家：
美国
项目类别：
财政年份：
2018
资助国家：
美国
起止时间：
2018-09-15 至 2021-08-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10013405
关键词：
Activities of Daily Living Adoption Algorithms Amputees Area Artificial Intelligence 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 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 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.

摘要