Work

HemiBot: design and technical validation of an adaptive Robotic coaching system for Unilateral Spatial Neglect rehabilitation

Year: 2026

Type: article

Abstract: Introduction: Robot-mediated neuro-motor rehabilitation offers intensive, repeatable, and objectively monitored therapy, yet existing systems rarely integrate automated session management, real-time per-repetition scoring, and data-driven adaptive difficulty progression within a single non-contact architecture. This work presents HemiBot (Hemineglect roBot), a ROS 2 software stack implementing a fully automated coaching pipeline for semicircular arm-pointing exercises on the TIAGo mobile manipulator (PAL Robotics), targeting post-stroke Unilateral Spatial Neglect (USN), in which patients fail to orient toward or interact with stimuli in the contralesional hemispace. Methods: The robot's end effector moves into the neglected hemispace as an exogenous attentional cue while the participant actively reaches toward it with the affected arm. The system extends a skeletal tracking framework with a Behavior Tree orchestrating exercise execution and per-repetition metric computation, a Coach node performing session-level adaptive difficulty progression, Italian empathetic text-to-speech feedback, and a scoreboard for therapist supervision. The scoring metric combines arm-alignment angle and hand-end-effector distance in a logical AND gate, yielding a normalized 0-100 score partitioned into three performance bands (Fail <25, Acceptable 25-75, Great ≥75). A participant is promoted when the mean session score exceeds 75 and demoted when the fail ratio reaches 0.50. Sixteen healthy participants (mean age 54.5 ± 6.8 years) were divided into two groups of eight: a high-performance group performing all repetitions correctly, and a low-performance group following a controlled error pattern designed to trigger demotion. Kinematic time series from the robot's onboard RGB-D camera were compared against a stereophotogrammetric gold standard (BTS SMART-DX EVO). Results: Scoring concordance between TIAGo- and mocap-derived classifications reached 100% (Cohen's κ = 1.000) across all 120 repetitions, with no cross-band errors between Fail and Great. The executed arc trajectory showed near-perfect agreement with the commanded geometry (Pearson r > 0.99). Adaptive behavior was correct for all 16 participants. Discussion: These findings verify that the onboard measurement chain reproduces gold-standard band assignments under the tested conditions and that the session-level decision pipeline operates end-to-end as specified. Validation of the scoring thresholds under naturally occurring, near-boundary performance remains necessary before clinical use.

Source: Frontiers in Robotics and AI

DOI: 10.3389/frobt.2026.1950821