HemiBot: design and technical validation of an adaptive Robotic coaching system for Unilateral Spatial Neglect rehabilitation
- 1Bioengineering Laboratory, Department of Electrical, Computer and Biomedical Engineering, University of Pavia, Pavia, Italy
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.
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BibTeX
@article{soldi2026hemibot,
title = {{HemiBot: design and technical validation of an adaptive Robotic coaching system for Unilateral Spatial Neglect rehabilitation}},
author = {Roberto Soldi and Bruna Maria Vittoria Guerra and Stefania Sozzi and Francesco Lunghi and Leo Russo and Micaela Schmid and Stefano Ramat},
journal = {Frontiers in Robotics and AI},
year = {2026},
month = oct,
doi = {10.3389/frobt.2026.1950821},
url = {https://doi.org/10.3389/frobt.2026.1950821},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - HemiBot: design and technical validation of an adaptive Robotic coaching system for Unilateral Spatial Neglect rehabilitation
AU - Roberto Soldi
AU - Bruna Maria Vittoria Guerra
AU - Stefania Sozzi
AU - Francesco Lunghi
AU - Leo Russo
AU - Micaela Schmid
AU - Stefano Ramat
PY - 2026
DA - 2026/10/21
JO - Frontiers in Robotics and AI
DO - 10.3389/frobt.2026.1950821
UR - https://doi.org/10.3389/frobt.2026.1950821
AB - 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.
SN - 2296-9144
ER -