2026-10-23T16:01:54Zhttps://gumushane-rdlopen.rawdatalibrary.net/oai/request

oai:gumushane-rdlopen.rawdatalibrary.net:gumushane/123072026-10-22T11:35:25Zopenairedrivercom_gumushane_10000col_gumushane_10001com_gumushane_10007col_gumushane_10171

A Personalized Target Placement Optimization Framework for VR-Based Upper Extremity RehabilitationTüre, HayatiKalfa, ErenAslan, Muhammed EminIşık, Buket ÖzdemirTopçu, OsmanÖzdemir, ErhanSarıhan, Köksalant colony optimizationhybrid optimizationmotor learningparticle swarm optimizationpersonalized therapystroke rehabilitationswarm intelligencetarget placementupper extremity rehabilitationvirtual realityFeatured Application: The proposed framework can be directly integrated into existing VR rehabilitation platforms to provide real-time personalized target placement for post-stroke upper extremity therapy, requiring no additional hardware beyond a standard CPU. Virtual reality (VR)-based rehabilitation is an established modality for upper extremity motor recovery; however, existing systems frequently rely on fixed, random, or therapist-tuned target placement that disregards patient-specific motor capacity and population-level priors. This study proposes a cross-patient collaborative swarm intelligence framework that derives zone-based patient profiles from real VR trajectories and augments them with a similarity-weighted cohort prior distilled from clinically similar patients’ successful trajectory clouds and zone-transition graphs. A hybrid Ant Colony Optimization (ACO)–Particle Swarm Optimization (PSO) algorithm optimizes 12 targets per session across a 27-zone ( (Formula presented.) ) workspace using a five-component fitness function encompassing reachability, zone balance, movement efficiency, heatmap-guided challenge coverage, and swarm-flow consistency. The framework was evaluated retrospectively on a single-center cohort of 36 post-stroke patients and 6373 sessions under a leakage-safe simulation protocol with 70/30 chronological splits; outcomes are model-based proxy success rates derived from each patient’s profile rather than directly observed task success. The hybrid strategy achieved a mean simulated success rate of 85.5% ± 5.5%, a 36.4% relative improvement over random placement (Wilcoxon (Formula presented.), Cohen’s (Formula presented.) ); the leakage-safe split yielded 80.1% on the held-out segment versus 61.1% for random, with no statistically significant train–test gap ( (Formula presented.) ). Ablation confirmed both PSO and ACO are individually necessary ( (Formula presented.) pp, (Formula presented.) ). Total session-start computation is 78 ms on standard CPU hardware. These findings constitute a proof-of-concept that collaborative personalized swarm optimization can substantially outperform heuristic target placement under in silico evaluation; clinical efficacy in terms of standardized motor outcome measures remains to be established in a prospective randomized controlled trial, and the findings should be replicated across centers, task modes, and a larger cohort before generalization.Multidisciplinary Digital Publishing Institute (MDPI)2026Articlehttps://rdlopen.rawdatalibrary.net/r/gumushane/12307https://doi.org/10.3390/app161258062076-3417enApplied Sciences (Switzerland)info:eu-repo/semantics/openAccessinfo:eu-repo/semantics/articlehttps://gumushane-rdlopen.rawdatalibrary.net/tr/items/gumushane/12307