A Deep Learning based Wearable Healthcare IoT Device for AI-enabled Hearing Assistance Automation

TL;DR

This paper introduces a deep learning-enabled wearable IoT device that assists hearing-impaired individuals by converting speech to text and detecting emergency sounds with high accuracy, enhancing communication and safety.

Contribution

It presents a novel AI-enabled IoT device integrating speech recognition and emergency sound detection using deep learning on a microcontroller platform.

Findings

Achieved 92% accuracy in sound recognition and classification

Demonstrated real-time performance of the prototype system

Enabled effective communication assistance for hearing-impaired users

Abstract

With the recent booming of artificial intelligence (AI), particularly deep learning techniques, digital healthcare is one of the prevalent areas that could gain benefits from AI-enabled functionality. This research presents a novel AI-enabled Internet of Things (IoT) device operating from the ESP-8266 platform capable of assisting those who suffer from impairment of hearing or deafness to communicate with others in conversations. In the proposed solution, a server application is created that leverages Google's online speech recognition service to convert the received conversations into texts, then deployed to a micro-display attached to the glasses to display the conversation contents to deaf people, to enable and assist conversation as normal with the general population. Furthermore, in order to raise alert of traffic or dangerous scenarios, an 'urban-emergency' classifier is developed using a deep learning model, Inception-v4, with transfer learning to detect/recognize alerting/alarming sounds, such as a horn sound or a fire alarm, with texts generated to alert the prospective user. The training of Inception-v4 was carried out on a consumer desktop PC and then implemented into the AI based IoT application. The empirical results indicate that the developed prototype system achieves an accuracy rate of 92% for sound recognition and classification with real-time performance.