Towards Equitable Robotic Furnishing Agents for Aging-in-Place: ADL-Grounded Design Exploration

In aging-in-place contexts, small difficulties in Activities of Daily Living (ADL) can accumulate, affecting well-being through fatigue, anxiety, reduced autonomy, and safety risks. This position paper argues that robotics for older adult wellbeing must move beyond "convenience features" and centre equity, justice, and responsibility. We conducted ADL-grounded semi-structured interviews with four adults in their 70s-80s, identifying recurrent challenges (finding/ organising items, taking medication, and transporting objects) and deriving requirements to reduce compounded cognitive-physical burden. Based on these insights, we propose an in-home robotic furnishing-agent concept leveraging computer vision and generative AI and LLMs for natural-language interaction, context-aware reminders, safe actuation, and user-centred transparency. We then report video-stimulated follow-up interviews with the same participants, highlighting preferences for confirmation before actuation, predictability, adjustable speed/autonomy, and multimodal feedback, as well as equity-related concerns. We conclude with open questions on evaluating and deploying equitable robotic wellbeing systems in real homes.

Prediction of Football Player Value using Bayesian Ensemble Approach

The transfer fees of sports players have become astronomical. This is because bringing players of great future value to the club is essential for their survival. We present a case study on the key factors affecting the world's top soccer players' transfer fees based on the FIFA data analysis. To predict each player's market value, we propose an improved LightGBM model by optimizing its hyperparameter using a Tree-structured Parzen Estimator (TPE) algorithm. We identify prominent features by the SHapley Additive exPlanations (SHAP) algorithm. The proposed method has been compared against the baseline regression models (e.g., linear regression, lasso, elastic net, kernel ridge regression) and gradient boosting model without hyperparameter optimization. The optimized LightGBM model showed an excellent accuracy of approximately 3.8, 1.4, and 1.8 times on average compared to the regression baseline models, GBDT, and LightGBM model in terms of RMSE. Our model offers interpretability in deciding what attributes football clubs should consider in recruiting players in the future.