| Sumario: | Purpose: The objective of this study is to develop and assess a wearable assistive device, enabled by the Internet of Things (IoT), that improves obstacle perception, mobility, and interaction with the environment for those with visual impairments. The technology amalgamates real-time object detection, facial recognition, and obstacle sensing into a unified framework to facilitate autonomous and secure navigation. Materials and Methods: An experimental design-and-build methodology was employed to create the prototype utilising an ESP32-CAM module, Arduino microcontroller, ultrasonic sensor, and text-to-speech feedback interface. Deep learning and machine learning algorithms—Support Vector Machine (SVM) for facial detection, ResNet-50 Convolutional Neural Network (CNN) for facial identification, and YOLOv4 for object detection—were employed to facilitate precise, low-latency performance. An evaluation of the system was performed under various environmental conditions utilising conventional criteria such as accuracy, precision, recall, and F1-score. Results: The implemented system attained a recognition accuracy surpassing 90% with negligible computational latency, making it appropriate for real-time applications. The hybrid SVM–YOLOv4 model exhibited a harmonious mix of precision and efficiency, facilitating dependable multi-object recognition under various environmental and lighting situations. The quantitative results confirm the strength and flexibility of the proposed wearable device for supportive purposes. Conclusions: The IoT-enabled wearable assistive technology demonstrates considerable promise to enhance mobility, environmental awareness, and independence in those with visual impairments. The device combines hardware sensors with advanced computer vision models, linking theoretical innovation to practical rehabilitative applications. Future endeavours will encompass extensive user validation to improve usability, accessibility, and user comfort. Consequences for rehabilitation: Wearable devices driven by IoT can enhance mobility and situational awareness for those with visual impairments. The incorporation of machine learning-derived feedback facilitates safer, autonomous navigation in fluctuating situations. The modular system architecture facilitates adaption for a wider range of assistive and rehabilitation applications. The research enhances the design of cost-effective, intelligent assistive technologies that align with rehabilitative objectives and the creation of inclusive technology. IMPLICATIONS FOR REHABILITATION: Wearable assistive technology powered by the Internet of Things has significant ramifications for rehabilitation, mainly because it gives visually impaired people more mobility and independence through voice feedback, real-time object and face recognition, and enhanced spatial awareness through an ultrasonic sensor and buzzer system. In addition to helping with personal navigation, this multifaceted assistive support—which combines computer vision, machine learning, and real-time feedback—has the potential for wider applications in fields like security and surveillance. Ultimately, by tackling the major obstacles that visually impaired people encounter when navigating and engaging with their surroundings, this technology helps to create a more thorough approach to rehabilitation.
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