Utilizing IoT Data Streams for Continuous Monitoring and Machine Learning Analysis of Stuttering Patterns

Abstract

Stuttering, a pervasive speech disorder, presents significant challenges in real-time monitoring and personalized intervention. This paper presents a pioneering approach that harnesses Internet of Things (IoT) data streams and advanced machine learning techniques for continuous stuttering pattern analysis, aiming to overcome existing limitations in diagnosis and treatment. Traditional assessment methods for stuttering often lack real-time capabilities and rely on subjective evaluations, impeding timely intervention strategies. Our study focuses on developing a comprehensive framework for continuous stuttering pattern monitoring, leveraging IoT devices such as wearable sensors and smartphones to capture speech data and associated physiological signals in real-time. Machine learning algorithms, including deep learning models, are employed to analyze these data streams, revealing distinctive patterns indicative of speech disfluency. Our findings demonstrate the feasibility and effectiveness of utilizing IoT data streams for continuous stuttering monitoring, enabling accurate detection and classification of speech disfluencies in real-time. This research underscores the potential of IoT-enabled continuous monitoring coupled with machine learning analysis in revolutionizing stuttering assessment and management, offering a promising avenue for early detection and personalized intervention strategies tailored to individuals with stuttering.