# A Survey on Video Big Data Analytics: Architecture, Technologies, and Open Research Challenges
## 4 ViMindXAI: A Scalable and Cognitive AI Platform for VBDA
### Real-World Application Scenarios of ViMindXAI
#### 4.3.2 Case Study 2: Healthcare Monitoring and Patient Safety  
In healthcare environments such as hospitals and long-term care facilities, continuous and intelligent video monitoring is essential for ensuring patient safety. Critical incidents, including falls, prolonged inactivity, and unsupervised movement, require immediate detection and response. At the same time, analytics systems must uphold strict privacy standards and operate efficiently in resource-constrained settings. ViMindXAI addresses these requirements by delivering real-time, federated, and privacy-aware video analytics tailored specifically for clinical deployments.  
At the DAL, video streams from patient rooms and corridors are processed on edge devices such as Jetson Xavier and Coral TPU. Lightweight activity recognition models, including LSTM and 3D CNNs, are deployed via ONNX for hardware-agnostic inference, enabling the detection of behavioral indicators such as falls and wandering. In parallel, audio streams are preprocessed using Whisper to identify signs of vocal distress. Data ingestion is handled through Apache Kafka and Apache NiFi, ensuring consistent, low-latency, and fault-tolerant delivery from multiple endpoints.  
The DIL establishes a secure and federated data backbone based on Delta Lake and MinIO. This hybrid architecture supports both local buffering and long-term cloud-integrated indexing, enabling scalable and privacy-conscious video storage. Semantic indexing leverages multimodal embeddings generated by CLIP and Whisper, capturing both visual and auditory context. These embeddings are indexed using FAISS to facilitate rapid, approximate similarity-based retrieval. Privacy-aware synchronization protocols govern data access and movement in alignment with HIPAA and GDPR.  
Within the DaAL, ViMindXAI executes real-time anomaly detection and temporal pattern modeling to identify clinically significant risks, including prolonged inactivity, irregular movement, or repeated expressions of distress. These models are continuously refined using FL strategies such as FedAvg, allowing for localized model updates across hospital zones without exposing raw video data. XAI modules enhance interpretability by visualizing the spatial and temporal features that contribute to each decision, supporting transparent clinical validation and oversight.  
The UEL provides HIPAA-compliant dashboards powered by Grafana and integrated with ViMindXAI's semantic search engine. Clinicians can receive real-time alerts, issue natural-language video queries such as \"falls inward A after midnight,\" and visualize mobility trends through interactive dashboards. Annotated video summaries and XAI overlays further support clinical review and facilitate timely, evidence-based decision-making.  
ViMindXAI delivers a robust, semantically enriched, and privacy-conscious video analytics platform for smart healthcare monitoring. Its modular and AI-driven architecture supports real-time event detection, regulatory compliance, and transparent system behavior across all layers, making it a comprehensive solution for next-generation clinical safety systems.