After neurological injury, individuals often undergo physical therapy to regain motor function, which can be supplemented with use of virtual reality (VR). Rehabilitation commonly employs methods that encourage movement variability to promote functional gains, such as perturbations. Rehabilitation also commonly integrates additional sensory modalities for guidance and cognitive engagement to the protocol. In this exploratory, proof-of-concept study, neurotypical participants were trained on a custom tracing task with targeted dynamic shifting to induce movement variability, under both expected (proactive) and unexpected (reactive) conditions, with and without added auditory feedback. Participants significantly (p < 0.05) improved performance (tracing accuracy) after training with audio feedback. Participants trained without audio feedback showed decreased electrodermal activity (EDA), a measure of physiological engagement. Audio feedback during reach training with complex objectives (e.g., dynamic shifting) can promote performance improvements and cognitive engagement.

Neurological injuries and disorders affecting hand motor control can severely impair the ability to perform activities of daily living and substantially reduce quality of life. Technologies such as virtual reality (VR) are increasingly used to address fundamental challenges in therapy, including motivation and engagement; further, programmable features of digital interfaces offer additional opportunities to personalize and optimize motor training. In this proof-of-concept study, we developed and evaluated a novel VR-based training framework to support improved dexterity and hand function using physiological (sensory-driven) and cognitive (memory) cues designed to promote greater task-relevant neural engagement. The proposed approach leverages the integration of augmented sensory feedback (ASF) with memory-anchored cues for motor learning of target hand gestures. Using a within-subjects design, thirteen neurotypical adults completed four training conditions: (1) control (baseline gesture-matching in VR), (2) visual ASF (enhanced visualization and feedback of gesture accuracy), (3) memory-anchored cues (associating gestures with semantically meaningful entities, loosely analogous to American Sign Language), and (4) hybrid multimodal (visual ASF + memory-anchored cues). Training with the hybrid condition produced the fastest skill acquisition (9.3 trials to reach an 80% accuracy threshold) and the steepest initial learning slope (1.86 ± 0.12%/trial), with all conditions differing significantly in initial slope (all p < 0.002). Post-training assessment showed that the hybrid condition achieved the highest gesture accuracy (95.2%), greatest normalized post-training accuracy gain (14.3% above baseline), fastest execution time to target gesture (1.14 s), and lowest variability in gestural kinematics (SD = 3.9%). Both ASF and memory-anchored cue conditions each also independently outperformed the control condition on gesture accuracy (both p ≤ 0.002), with omnibus ANOVAs indicating significant condition effects across metrics. Together, these findings suggest that pairing ASF cues with memory-based cognitive scaffolding can yield additive benefits for motor skill acquisition and stability. Pending validation in clinical populations, such approaches may inform the design of VR-based motor training frameworks for rehabilitation.

In this pilot evaluation of a virtual reality platform for upper-extremity motor rehabilitation, 9 neurotypical participants performed a force-modulated tracking task across 4 trajectory difficulty levels with and without augmented visual feedback; tracking accuracy improved in all conditions, with the descriptive visual-feedback advantage more pronounced at higher difficulty.