DIY AI Smart Glass: Design, Development, and Evaluation of an Indigenous Multimodal Wearable Assistant

The rapid advancement of multimodal artificial intelligence has accelerated the development of wearable smart assistants capable of integrating vision, speech, and contextual reasoning. However, commercially available AI smart glasses remain expensive, proprietary, and difficult to customize, limiting their accessibility for education, research, and assistive technology development, particularly in resource-constrained environments. This study presents DIY AI Smart Glass, an indigenous low-cost multimodal wearable assistant designed to provide an open-source alternative for real-time human–computer interaction. The primary aim of this research is to develop and evaluate an affordable wearable AI platform capable of understanding spoken language, interpreting visual information, and generating contextual voice responses through an integrated multimodal framework. The proposed system employs an ESP32-S3 AI camera module equipped with an OV3660 camera, PDM microphone, and inbuilt speaker, while computationally intensive multimodal inference is performed on a host computer using a Python-based architecture integrated with a cloud-based large multimodal language model. The methodology incorporates wireless acquisition of audio and visual data, wake-word activation, speech understanding, scene interpretation, contextual reasoning, and real-time speech synthesis, enabling natural hands-free interaction through a single wearable device. Unlike conventional wearable vision systems that primarily perform object detection, the proposed framework supports both visual scene understanding and general conversational assistance within a unified architecture. Experimental validation was conducted across representative visual and non-visual interaction scenarios to evaluate functionality, response generation, and user experience. The developed prototype demonstrates reliable multimodal interaction while maintaining a significantly lower hardware cost than commercial smart glasses, thereby providing an accessible platform for assistive technologies, educational robotics, wearable artificial intelligence research, and rapid prototyping of next-generation human-centred intelligent systems.