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

> **NIH NIH U44** · INFINITE BIOMEDICAL TECHNOLOGIES, LLC · 2020 · $64,079

## Abstract

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.

## Key facts

- **NIH application ID:** 10078697
- **Project number:** 3U44NS108894-02S1
- **Recipient organization:** INFINITE BIOMEDICAL TECHNOLOGIES, LLC
- **Principal Investigator:** Rahul Reddy Kaliki
- **Activity code:** U44 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $64,079
- **Award type:** 3
- **Project period:** 2020-05-01 → 2021-04-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10078697

## Citation

> US National Institutes of Health, RePORTER application 10078697, User-driven Retrospectively Supervised Classification Updating (RESCU) system for robust upper limb prosthesis control (3U44NS108894-02S1). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10078697. Licensed CC0.

---

*[NIH grants dataset](/datasets/nih-grants) · CC0 1.0*