Variable task difficulty with augmented sensory feedback for motor training in virtual reality

Introduction This study examined how motor task difficulty and augmented sensory feedback (ASF) modality influence task performance, physiological responses, and perceptual experience during virtual reality (VR)-based upper-limb motor training. Building on related brace- and VR-based ASF platforms previously examined in neurotypical and spinal cord injury cohorts, the long-term goal is to support neurorehabilitation-oriented adaptive VR training; however, the present study used neurotypical participants as an initial validation step. Methods Fourteen right-handed neurotypical participants used a supportive upper-limb brace and surface electromyography (EMG) for myoelectric control of a virtual robotic arm in a two-dimensional tracking task. A 2 × 2 within-subject design crossed ASF modality (visual feedback versus visual + haptic feedback) with task difficulty (straight-line versus sinusoidal trajectory). Outcomes included normalized path length, task completion time, movement jerk, alpha- and beta-band electroencephalogram (EEG) power over motor cortex, heart rate, EMG amplitude, EMG-displacement-based virtual work, and perceptual ratings of agency, motivation, and utility. Short-term pre-to-post percentage changes were computed for each measure. Results VHF straight-line training produced the largest reductions in normalized path length (-4.69%) and completion time (-6.12%), whereas VHF sinusoid training produced the largest reductions in movement jerk (-5.67%) and virtual work (-13.33%). ASF modality affected path length, completion time, jerk, EMG amplitude, and virtual work, whereas task difficulty modulated beta-band EEG power and the physiological index. Changes in path length were positively associated with changes in completion time and EMG amplitude. Discussion These findings suggest that ASF modality and task difficulty can differentially shape short-term behavioral, physiological, and perceptual responses in healthy participants. The exploratory distribution of participant-specific best-case conditions motivates future studies using independent trial subsets to evaluate whether behavioral and physiological measures can support individualized VR training.