Characterizing Healthy & Post-Stroke Neuromotor Behavior During 6D Upper-Limb Isometric Gaming: Implications for Design of End-Effector Rehabilitation Robot Interfaces

TL;DR

An assessment of how subtle aspects of interface design impact user behavior is presented; an analysis of how pathological neuromotor behaviors are reflected in end-effector force dynamics; and a novel hidden Markov model (HMM)-based neuromotor behavior classification method based on surface electromyography signals during cyclic motions are presented.

Abstract

Successful robot-mediated rehabilitation requires designing games and robot interventions that promote healthy motor practice. However, the interplay between a given user's neuromotor behavior, the gaming interface, and the physical robot makes designing system elements -- and even characterizing what behaviors are "healthy" or pathological -- challenging. We leverage our OpenRobotRehab 1.0 open access data set to assess the characteristics of 13 healthy and 2 post-stroke users' force output, muscle activations, and game performance while executing isometric trajectory tracking tasks using an end-effector rehabilitation robot. We present an assessment of how subtle aspects of interface design impact user behavior; an analysis of how pathological neuromotor behaviors are reflected in end-effector force dynamics; and a novel hidden Markov model (HMM)-based neuromotor behavior classification method based on surface electromyography (sEMG) signals during cyclic motions. We demonstrate that task specification (including which axes are constrained and how users interpret tracking instructions) shapes user behavior; that pathology-related features are detectable in 6D end-effector force data during isometric task execution (with significant differences between healthy and post-stroke profiles in force error and average force production at $p=0.05$); and that healthy neuromotor strategies are heterogeneous and inherently difficult to characterize. We also show that our HMM-based models discriminate healthy and post-stroke neuromotor dynamics where synergy-based decompositions reflect no such differentiation. Lastly, we discuss these results' implications for the design of adaptive end-effector rehabilitation robots capable of promoting healthier movement strategies across diverse user populations.

Figures (11)

• Figure 1: End-effector robot--mediated rehabilitation --- in which users are instructed to perform varied trajectory tracking tasks while assisted (or resisted) by a robot --- consists of complex feedback loops (blue) between task specification and display, human neuromotor behavior, and robot intervention. In this work, we leverage multimodal biosensor data, collected from 13 healthy and 2 post-stroke participants during completion of 8 isometric gaming tasks under 2 pose conditions anandextensible2025, to address three questions key to effective therapy design (gold), toward development of rehabilitation robots that can induce targeted, healthy neuromotor practice.
• Figure 2: Motor rehabilitation platform enabling measurement of muscle engagement and end effector forces during trajectory tracking tasks. Users exert forces and torques on a SensONE 6-axis load cell (a) (Bota Systems AG, Zürich, Switzerland) through the attached handle, which are then mapped to $x$--$y$ coordinates of on-screen avatar (b) to allow users to follow red target ball (c) through different trajectories within a custom gamified rehabilitation environment developed in Unity (Unity Software Inc., San Francisco, CA, USA). During each trajectory tracking task, surface electromyography (sEMG) electrodes (d) --- two Trigno Quattro 4-channel sensor motes, controlled through a Trigno Base Station (Delsys, Inc., Natick, MA, USA) --- placed on key muscles of the arm (e) record muscle activations. The system currently supports isometric rehabilitation tasks at arbitrary poses --- the LBR iiwa 14 R820 7-degree-of-freedom cobot (f) (KUKA AG, Augsburg, Germany) remains static --- but will be expanded in the future to support a variety of robot controllers. Surface EMG electrode placements: anterior deltoid (AD), middle deltoid (MD), posterior deltoid (PD), and biceps brachii (BB), grounded at the acromion (G1); triceps brachii (long head, TR), brachioradialis (BR), wrist flexors (FL), and wrist extensors (EX), grounded at the olecranon (G2). Reprinted from anandextensible2025.
• Figure 3: Experimental flow during collection of pilot data set. Participants were consented and surveyed, then completed trajectory tracking tasks at two ADL-inspired poses --- Conditions A (shoulder adducted, elbow flexed $\sim 90\degree$, forearm horizontal) and B (shoulder slightly flexed above horizontal with near full elbow extension) --- in randomized order before providing final survey feedback. Survey data are not examined in this work. Reprinted from anandextensible2025.
• Figure 4: Illustration of three distinct observed target tracking behaviors, depicted during the spline 2 trajectory tracking task. Participants were instructed to follow target ball $t$ with avatar ball $a$ as it traversed the specified trajectory. Observed strategies included (1) pursuit of the target along the trajectory, (2) shortest-distance pursuit to $t$'s current position, and (3) movement toward an anticipated future position of $t$.
• Figure 5: Exemplar time series force profiles of two healthy participants (02, top and 08, middle) and one post-stroke participant (21, bottom) performing the $x$-axis trajectory tracking task in pose Condition A. All participants exhibit not only "productive" force exertions ($x$, red) contributing to task completion, but substantial "non-productive" output in orthogonal dimensions ($y$, green and $z$, blue). The magnitude and temporal characteristics of these non-productive exertions vary within both healthy and post-stroke populations, illustrating the importance of careful specification when prescribing movement tasks. The post-stroke participant also exhibits markedly altered force production, with sustained off-axis force generation and reduced efficiency in producing task-relevant forces, reflecting underlying pathology.