Research on Video Fall Detection System Based on Multimodal Large Language Models

This article explores how to use Multimodal Large Language Models (MLLMs) to implement video fall detection, evaluating the models' performance in human activity recognition and fall state detection through experimental paradigms such as zero-shot, few-shot, and chain-of-thought. This research aims to address issues like inconvenience of wearing, high false alarm rates, and insufficient privacy protection in traditional fall detection solutions, providing a new direction for intelligent monitoring in the medical and health field.

With the intensification of global population aging, falls among the elderly have become a serious public health issue (one of the main causes of injury and death for people over 65 years old). Traditional fall detection relies on wearable devices or dedicated sensors, which have problems such as inconvenience to wear, high false alarm rates, and insufficient privacy protection. Computer vision technology provides a non-invasive and flexible deployment approach for fall detection, but traditional deep learning models require a large amount of labeled data and have limited generalization ability. Multimodal Large Language Models (MLLMs) have strong visual understanding and language reasoning capabilities, bringing new possibilities to solve the above problems. This study explores their application in video fall detection and human activity recognition tasks.

1. Zero-shot learning: No examples, only task instructions, to test the model's general visual understanding ability. Enabled via experiment=zeroshot. Advantages: No training data required, low deployment cost; but precise task description is needed.
2. Few-shot learning: Provide labeled examples to guide the model. Two strategies: random selection (experiment=fewshot), similarity retrieval (based on precomputed embedding vectors, experiment=fewshot_similarity). Core advantage: Quickly adapt to tasks through in-context learning without fine-tuning, suitable for data-scarce scenarios.
3. Chain-of-thought reasoning: Zero-shot chain-of-thought requires the model to generate reasoning processes, improving interpretability and accuracy in complex scenarios. Enabled via experiment=zeroshot_cot.

• Inference Engine: Uses the vLLM framework (high throughput, memory efficiency optimization, PagedAttention technology to improve GPU memory utilization). Configuration uses the Hydra framework (configurations for vLLM, sampling, model, prompts, etc.).
• Data Processing and Caching: PyAV library preprocesses videos, supports disk caching (.pt files stored in outputs/tensor_cache) and memory caching (lazy loading of few-shot examples). The combination of two-level caching improves efficiency.
• Embedding Calculation: Uses the Qwen3-VL-Embedding model to extract video features, stored in outputs/embeddings/. Run via experiment=embed, which is a prerequisite step for similarity-based few-shot experiments.

• Zero-shot experiment (InternVL3.5-8B): python scripts/vllm_inference.py experiment=zeroshot model=internvl model.params=8B
• Random few-shot (QwenVL-4B): python scripts/vllm_inference.py experiment=fewshot model=qwenvl model.params=4B
• Similarity-based few-shot (QwenVL-8B): python scripts/vllm_inference.py experiment=fewshot_similarity model=qwenvl model.params=8B
• Chain-of-thought reasoning: python scripts/vllm_inference.py experiment=zeroshot_cot
• Pre-build disk cache: python scripts/build_tensor_cache.py experiment=zeroshot data.cache_dir=outputs/tensor_cache

Prediction results and evaluation metrics are stored in output_dir/predictions/<wandb-project>/ and output_dir/evaluation_results/<wandb-project>/. Weights & Biases (wandb) is integrated to track experiments (supports online, offline, and disabled modes). Evaluation metrics include precision, recall, F1 score for fall detection, and classification accuracy for human activity recognition. By comparing results from different paradigms, we analyze the impact of in-context learning and explicit reasoning on performance.

• Application Prospects: Suitable for scenarios such as nursing homes, hospitals, and households of elderly people living alone. Advantages include strong generalization ability, flexible deployment (models with different parameters), interpretability (chain-of-thought), and fast adaptation (few-shot).
• Technical Challenges: High computational resource requirements (GPU needed), privacy protection (video data is sensitive), real-time requirements (low latency), and false alarm control (distinguishing similar fall-like actions).

This project demonstrates the potential of MLLMs in video fall detection. Through systematic experiments to evaluate the effects of different paradigms, it provides references for applications in the medical and health field. Future directions: Explore efficient model architectures to reduce computational overhead, multi-camera fusion to improve robustness, adaptive learning mechanisms for continuous improvement, and integration with other sensors to build multimodal fusion systems. With the advancement of MLLM technology, intelligent health monitoring systems will play an important role in an aging society.