Robot-Induced Bias in Movement Smoothness Metrics for Rehabilitation: Quantification and Correction in Simulation
David Regev, Alex Scherbakov, Eran Beeri Bamani
原文摘要
Robot mechanics can bias kinematic measures of recovery and limit comparisons across rehabilitation devices. We developed a closed-loop human-robot simulation calibrated to published healthy and stroke group kinematics. Paired runs held motor-control parameters and movement plans fixed to quantify bias in seven metrics relative to a low-impedance reference, normalized using external repeatability benchmarks. Within a subset informed by published device specifications, median log dimensionless jerk (LDLJ) bias was 0.93 and 1.79 benchmark units for stroke and healthy profiles, respectively. Sobol analysis identified Coulomb friction and apparent mass as leading contributors; LDLJ bias persisted across 11 robustness variants. LDLJ bias increased as simulated movements became less fragmented. If recovery similarly reduces fragmentation, this effect could partly mask improvement in measured smoothness. Correction combining identified robot dynamics and observed metrics with gradient-boosted trees reduced median normalized absolute error from 0.77 to 0.10 for LDLJ and from 0.24 to 0.08 for spectral arc length (SPARC) in the device-informed subset of a random 20% hold-out. Median smoothness residuals remained at or below 0.15 benchmark units across four separately held-out parameter regions. Under an alternative controller, LDLJ error decreased from 1.60 to 0.17 without retraining, although transfer varied across metrics. These simulation results support mechanical characterization and movement-dependent correction toward more comparable robotic assessments, motivating validation in patients.
cs.RO · q-bio.QM