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Sponsored Design Multi-Agent AI Systems Using MCP and A2A: Engineer your own Pyt Design Multi-Agent AI Systems Using MCP and A2A: Engineer your own Pyt

Author

Sinan Chen

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Open access Jul 2026

Event-Driven Multimodal Sensing and Computing for Context-Aware Home Monitoring Using Stereo Vision and Dietary Event Anchoring

Real-world home monitoring requires sensing systems that can capture daily behaviour without continuous raw-video retention or excessive user burden. However, domestic environments present irregular activity timing, fragmented human presence, asynchronous multimodal events, and privacy-sensitive data management. This study proposes an event-driven multimodal sensing and computing framework for context-aware home monitoring using stereo vision and dietary event anchoring. The framework integrates stereo RGB-based three-dimensional human motion sensing, dining-zone-triggered meal image acquisition, runtime event orchestration, timestamp-based cross-modal synchronization, privacy-aware local storage, and large-language-model-assisted dietary context interpretation. Instead of continuously recording all sensor streams, the system activates and organizes sensing through human presence detection, debounce logic, cooldown-based session control, and dining-zone occupancy events. Meal-related events are used as contextual anchors to associate motion sessions and dietary observations into synchronized behavioural episodes. The prototype was deployed for 11 consecutive days in a real kitchen–dining environment, with the stabilized real-time monitoring phase evaluated from 11 to 14 February 2026. During this phase, the system generated 26 event-driven motion sessions and 51,165 captured pose frames, of which 25,925 were valid. Sustained active sessions accounted for 30.8% of all sessions but contributed 81.5% of captured pose frames, indicating that event-driven orchestration concentrated motion data within behaviourally meaningful activity windows. Eight meal-related records were obtained, seven of which overlapped with motion sessions, resulting in 87.5% meal-event overlap coverage. Structured pose outputs required approximately 550 kB/min, corresponding to about 33 MB/h of recorded pose data. LLM-assisted meal-image interpretation achieved a mean absolute percentage error of 25.44%, supporting its use for coarse dietary-context description rather than precise nutritional quantification. However, this result is interpreted only as evidence for coarse dietary-context description and not as validation of a precise nutritional or clinical dietary assessment method. These results demonstrate the system-level feasibility of transforming irregular domestic observations into structured, temporally indexed, and privacy-aware multimodal behavioural records for future home monitoring applications.

Zhaozhen Tong, K. Ono, Masahide Nakamura et al. · 0 citations
Open access Jul 2026

Privacy-Preserving Peer-to-Peer Cross-Domain Collaborative Filtering via Intent-Adaptive Graph Reconstruction

Cross-domain collaborative filtering effectively alleviates the data sparsity issue but raises serious privacy concerns. Federated learning has been integrated into cross-domain collaborative filtering to reduce these risks by securely exchanging embeddings or model parameters. However, the current federated paradigm often relies on the simple alignment of coarse-grained representations, while propagating information on a rigid local graph. Without fine-grained preference modeling, models easily suffer from representation collapse. The inherent sparsity of local graphs further limits their robustness. To address these issues, we propose P2P-IAGR, a privacy-preserving peer-to-peer cross-domain collaborative filtering framework based on intent-adaptive graph reconstruction. Specifically, our framework first disentangles user and item representations into fine-grained latent intent prototypes. We perturb these prototypes using Local Differential Privacy (LDP) and securely exchange them across domains. A contrastive learning strategy is then used for cross-domain alignment. Next, guided by the combined cross-domain intent prior, P2P-IAGR differentiably reconstructs an augmented graph view. We apply a dual-view structural contrastive learning objective to dynamically inject external collaborative signals into the sparse local topology. Extensive experiments on real-world datasets show that P2P-IAGR significantly outperforms state-of-the-art methods, achieving an average improvement of 5.85% in NDCG and 6.89% in HR.

Munan Li, Hao Zhang, Jialong Li et al. · 0 citations

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