RehabGen: Generative Expert-in-the-Loop Vision-Language Model for Everyday Contextualized Rehabilitation Exercises

Self-directed rehabilitation in everyday environments remains difficult due to limited feedback and the absence of contextually grounded guidance. In response, we present RehabGen, a generative vision-language model that generates personalized exercise plans contextualized to a patient's egocentric image and available everyday objects. We followed an expert-in-the-loop (EITL) framework: gathering domain requirements through expert interviews and focus group interviews ( N =8), constructing a 1.66k multimodal preference dataset, and aligning the model using supervised fine-tuning (SFT) and Direct Preference Optimization (DPO). In Expert Validation ( N =23), DPO achieved a 57.6% item-level win rate over the Baseline, with higher scores in Personalized Recommendation (85.7%), Identify Purpose (63.6%), and Recommendation Satisfaction (66.7%). In Patient Experience Evaluation ( N =8), both models outperformed the Baseline. This work contributes an EITL framework and open-source models available at http://rehabgen.hcitech.org/ for clinically grounded, context-aware rehabilitation planning.