Human capital reporting as a source of reliable information for marketing communication process in a socially responsible company

This research addresses the technical challenges of gathering, processing, and validating human capital metrics within socially responsible organizations using decentralized Edge AI and embedded sensor networks. To ensure the reliability of information utilized in corporate marketing and communication processes, a robust hardware-software co-design framework is proposed. The system utilizes low-power edge computing nodes equipped with environmental and physiological sensors to monitor workplace safety, ergonomic compliance, and cognitive load indicators in real-time. By processing sensor data locally at the edge, the architecture preserves employee privacy while generating verifiable, high-integrity aggregate metrics. A lightweight cryptographic verification protocol is integrated into the embedded firmware to prevent data tampering, thereby establishing a secure chain

of custody from the physical workspace to the corporate reporting database. The performance of the proposed Edge AI framework is evaluated in terms of latency, classification accuracy of human activity recognition models, and energy efficiency of the embedded nodes. This methodology provides a reliable, automated, and ethically aligned data-gathering paradigm that supports transparent human capital reporting, bridging the gap between physical operational metrics and high-level corporate social responsibility communication.

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