# Controlling Robot-Assisted Locomotion with Extended Kalman Filter Estimates of Phase and Activity

> **NIH NIH R01** · UNIVERSITY OF MICHIGAN AT ANN ARBOR · 2021 · $86,861

## Abstract

PROJECT ABSTRACT
This diversity supplement to the parent R01 project will address our overall goal—to model and control human
locomotion over continuously varying tasks—by indirectly estimating a gait-state comprising (1) the phase
variable, (2) the phase rate, and (3) a selection of task variables such as the step length and ramp inclination.
The phase variable is a signal that monotonically increases during the natural progression each human stride,
and the phase rate is the rate of change of the phase variable. Task variables represent the physical
characteristics of a locomotion task, such as the ground’s inclination or the speed of the human. Grouped
together in the gait-state, these variables parametrize human leg kinematics and kinetics through a gait-
mapping, which must be learned from gait data. The central hypothesis of this project is that continuously
varying activities can be tracked in real-time by using the theory of state estimation to estimate this gait-state
vector indirectly through a comparison between measured human kinematics and the kinematic predictions of
the gait-mapping; this estimate can then be used to either imitate (in the prosthetic case) or augment (in the
orthotic case) the human’s behavior. In this research, PhD candidate Roberto Manuel Leonardo Medrano
III, BS (Leo) will be advised by PI Robert Gregg, PhD and Co-I Elliott Rouse, PhD, who will help him develop
as a leader/researcher throughout the course of this project at the University of Michigan (U-M).

## Key facts

- **NIH application ID:** 10328286
- **Project number:** 3R01HD094772-04S1
- **Recipient organization:** UNIVERSITY OF MICHIGAN AT ANN ARBOR
- **Principal Investigator:** Robert D Gregg
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $86,861
- **Award type:** 3
- **Project period:** 2018-09-01 → 2023-11-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10328286

## Citation

> US National Institutes of Health, RePORTER application 10328286, Controlling Robot-Assisted Locomotion with Extended Kalman Filter Estimates of Phase and Activity (3R01HD094772-04S1). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/10328286. Licensed CC0.

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