A fuzzy-logic approach for longitudinal assessment of patients’ psychophysiological state: an application to upper-limb orthopedic robot-aided rehabilitation

Abstract

Abstract Understanding the psychophysiological state during robot-aided rehabilitation is crucial for assessing the patient experience during treatments. This paper introduces a psychophysiological estimation approach using a Fuzzy Logic inference model to assess patients’ perception of robots during upper-limb robot-aided rehabilitation sessions. The patients were asked to perform nine cycles of 3D point-to-point trajectories toward different targets at varying heights with the assistance of an anthropomorphic robotic arm (i.e. KUKA LWR 4+). Physiological parameters, including galvanic skin response, heart rate, and respiration rate, were monitored across ten out of forty daily sessions. This data enabled the construction of an inference model to estimate patients’ perception states. Results expressed in terms of correlation coefficients between the patient state and the increasing number of sessions. Correlation coefficients showed statistically significant strong associations: a state of heightened engagement (formerly described as “Excited”) had $$\rho = -0.73$$ ρ = - 0.73 (p-value=0.01), and a more calm and resting state (namely “Relaxed” state) had $$\rho = 0.70$$ ρ = 0.70 (p-value=0.02) with the number of sessions completed. All patients had positive interaction with the robot, initially expressing curiosity and interest that gradually shifted to a more “Relaxed” state over time.

Keywords

• Psychophysiological estimation
• Robot-aided rehabilitation
• Physiological monitoring
• Fuzzy logic