INFINITE BIOMEDICAL TECHNOLOGIES, LLC — Department of Health and Human Services SBIR Phase I: NINDS

Description

ABSTRACT Approximately 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 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 machine learning robust classification by implementing a non boundary based algorithm and 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 amputee subjects and their prosthetists Our milestones for Phase I are as follows Milestone Extreme Learning Machine with Adaptively Sparse Representation EASRC algorithm successfully implemented and verified Milestone Implementation of Nessa adaptive learning algorithm and smartwatch interface Milestone User Needs and Design Inputs locked as a result of Focus Group testing Milestone Hold a pre submission meeting with FDA for feedback on device classification and planned product performance testing Milestone Hold a Scientific Steering Group SSG meeting Milestone Convene a Study Monitoring Committee SMC and hold an initial meeting to review clinical plans Milestone Develop Clinical Study Protocol Milestone Register the study on www clinicaltrials gov PROJECT NARRATIVE In this project we aim to empower the user by bringing them into the control loop of their prosthesis and improve the stability of their control strategy over time Specifically we implement to a robust classifier an adaptive learning algorithm and a smartwatch interface which allows the user to teach their device when it misunderstands the commands that the user is sending to control the prosthesis This will result in improved control without cumbersome or time consuming effort on the part of the user and more importantly we hope that it will give the user a greater sense of empowerment and ownership over their prosthesis