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

Information

  • Research Project
  • 10078697
  • ApplicationId
    10078697
  • Core Project Number
    U44NS108894
  • Full Project Number
    3U44NS108894-02S1
  • Serial Number
    108894
  • FOA Number
    PA-18-837
  • Sub Project Id
  • Project Start Date
    5/1/2020 - 4 years ago
  • Project End Date
    4/30/2021 - 3 years ago
  • Program Officer Name
    HUDAK, ERIC MICHAEL
  • Budget Start Date
    5/1/2020 - 4 years ago
  • Budget End Date
    4/30/2021 - 3 years ago
  • Fiscal Year
    2020
  • Support Year
    02
  • Suffix
    S1
  • Award Notice Date
    4/30/2020 - 4 years ago

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

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.

IC Name
NATIONAL INSTITUTE OF NEUROLOGICAL DISORDERS AND STROKE
  • Activity
    U44
  • Administering IC
    NS
  • Application Type
    3
  • Direct Cost Amount
  • Indirect Cost Amount
  • Total Cost
    64079
  • Sub Project Total Cost
  • ARRA Funded
    False
  • CFDA Code
    853
  • Ed Inst. Type
  • Funding ICs
    NINDS:64079\
  • Funding Mechanism
    SBIR-STTR RPGs
  • Study Section
  • Study Section Name
  • Organization Name
    INFINITE BIOMEDICAL TECHNOLOGIES, LLC
  • Organization Department
  • Organization DUNS
    037376022
  • Organization City
    BALTIMORE
  • Organization State
    MD
  • Organization Country
    UNITED STATES
  • Organization Zip Code
    212024264
  • Organization District
    UNITED STATES