Haptic rehabilitation exercises performance evaluation using automated inference systems

Haptics and virtual environments offer the opportunity to improve the traditional methods of stroke rehabilitation. Traditionally, a therapist has to subjectively evaluate the patient's performance. This paper aims to introduce an automated inference system that utilises haptic data to quantise the patient's performance. Two systems were implemented: a Fuzzy Inference System (FIS) and an Adaptive Neuro-Fuzzy Inference System (ANFIS). The two systems were validated with sample input/output datasets. Testing with real subjects' data has led to the conclusion that the CyberForce system is incapable of providing normative data for evaluating the patient performance due to calibration and consistency issues.