The future of Consumer Electronics (CEs) is moving rapidly towards Unmanned Aerial Systems (UAS), wearables, and Explainable AI (XAI). UAS are facilitating near real-time aerial monitoring of the environment, wearables permit continuous monitoring of physiological and biometric data, and XAI is the next step toward transparency in systems through XAI informed decision-making that users of Artificial Intelligence (AI)can trust and understand. In this paper, we propose a new multi-modal architecture that integrates UAS, wearable devices, and XAI to generate an intelligent and adaptive CE ecosystem. The architecture proposed uses a sequential data gathering process involving UAS and wearables, and the multi-modal data are fused and modeled using machine learning techniques. Transparency and user accountability can be established through the use of XAI systems like Hapley Additive exPlanations (SHAP) and Local Interpretable Model-agnostic Explanations (LIME) to provide clear and actionable explanation of AI-driven outputs. Our results indicate accuracy of 92% with an explanation fidelity of 95%, a significant improvement over conventional technology. In addition, the proposed architecture will have tremendous potential for disruption in the healthcare, fitness, and smart home spaces, as personalization and ethical use of data are paramount. The novel contributions of this work in uniquely bringing together aerial monitoring, physiological monitoring, and AI, furthers toward the goals of building trustworthiness in CEs and user-centered intelligent systems.

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