Michael C. Rosenberg

Loss of hand control expressiveness revealed by task- and individual-specificity in spatiotemporal finger coordination

The paper analyzes spatiotemporal finger coordination in able-bodied and post-stroke hands, finding reduced complexity and expressiveness in paretic fingers.

Gait signature changes with walking speed are similar among able-bodied young adults despite persistent individual-specific differences

The paper shows gait signatures remain individual-specific across walking speeds, with 3D kinematic data achieving high accuracy in classifying individuals.

Associations between music and dance relationships, rhythmic proficiency, and spatiotemporal movement modulation ability in adults with and without mild cognitive impairment

Quantifying changes in individual-specific template-based representations of center-of-mass dynamics during walking with ankle exoskeletons using Hybrid-SINDy

This study uses Hybrid-SINDy to analyze changes in center-of-mass dynamics during walking with ankle exoskeletons in unimpaired adults and a post-stroke individual, finding no changes in unimpaired dynamics but increased rotary stiffness in the paretic leg with exoskeletons.

Discovering individual-specific gait signatures from data-driven models of neuromechanical dynamics

This paper introduces a data-driven model to discover individual-specific gait signatures by analyzing neuromechanical dynamics across different gait speeds and conditions.

Motor and cognitive deficits limit the ability to flexibly modulate spatiotemporal gait features in older adults with mild cognitive impairment

This study evaluates how age and mild cognitive impairment affect the ability to modulate gait features during dance-based therapy.

A machine learning approach to quantify individual gait responses to ankle exoskeletons

This study uses neural networks to quantify individual gait responses to ankle exoskeletons by modeling discrepancies between nominal and exoskeleton gait data.

Predicting walking response to ankle exoskeletons using data-driven models

This study evaluates three data-driven phase-varying models to predict kinematic and myoelectric responses to ankle exoskeletons during walking, finding LPV models provide better accuracy than PV models for kinematic predictions.