arXiv:2506.22726v4 Announce Type: replace-cross Abstract: Deep learning for human sensing on edge systems presents significant potential for smart applications. However, its training and development are hindered by the limited availability of sensor data and resource constraints of edge systems. While transferring pre-trained models to different sensing applications is promising, existing methods often require extensive sensor data and computational resources, resulting in high costs and limited transferability. In this paper, we propose XTransfer, a first-of-its-kind method enabling modality-
The proliferation of IoT devices and the increasing demand for real-time, low-latency AI inference at the edge drives the need for efficient model deployment strategies.
This development allows for more pervasive and specialized AI applications on resource-constrained devices, reducing reliance on centralized cloud processing and enhancing data privacy.
The ability to quickly adapt pre-trained AI models to new sensing modalities with minimal data and computational resources significantly lowers the barrier to entry for edge AI development.
- · Edge AI hardware manufacturers
- · Smart device developers
- · Human-computer interaction researchers
- · IoT service providers
- · Traditional cloud-centric AI service models
- · Companies reliant on large-scale data collection for model training
- · Developers stuck with resource-intensive AI models
Widespread adoption of specialized AI models on everyday devices for human sensing tasks.
Increased innovation in health monitoring, smart environments, and personalized assistance without requiring constant data uploads.
Ethical and privacy concerns around pervasive human sensing becoming more pressing, driving new regulatory frameworks.
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Read at arXiv cs.LG