Research map / Federated learning and privacy / Privacy attacks and secure learning

AI-Driven Personalized Fall Prevention for Older Adults

AAAI 2025 · Federated learning and privacy › Privacy attacks and secure learning · approach: Visual privacy and face de-identification · 1 citations (OpenAlex)

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A research proposal outlining a five-component AI system for personalized fall prevention in older adults, combining pose estimation, multimodal risk assessment, RL-based interventions, explainable NLP interaction, and privacy techniques, with no experiments reported.

Problem

Falls among older adults cause reduced quality of life, higher healthcare costs, and increased mortality, and current prevention is not personalized or scalable.

Approach

Proposes integrating HRNet-based pose estimation with attention, LSTM-GCN activity recognition, federated multimodal risk assessment, DQN with GAN-simulated environments for intervention planning, SHAP explanations with a fine-tuned GPT-3 interface, and differential privacy plus homomorphic encryption into one system. It is a plan only; no system is built or evaluated.

Key results

• No experimental results are reported; the paper is a research proposal with planned evaluations.
• Cites a meta-analysis (Hopewell et al.) reporting that effective fall prevention strategies can reduce fall rates by up to 23%, as motivation only.

Limitations

• No implementation or experimental results; the system is entirely proposed.
• Citations appear mismatched in the text (e.g., GPT-3 attributed to Dwork et al., DQN to [9], GAN approach attributed to Lundberg et al.), weakening sourcing.
• Clinical trials and five-year timeline are planned but not designed in detail.

Experimental setup

Datasets: MPII COCO

Benchmarks: MPII COCO

Metrics: MPJPE precision recall AUROC

Builds on: HRNet Deep Q-Network SHAP GPT-3 federated learning

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