Development of a Wearable Sensor System and AI-Driven Analysis for Objective Bruxism Assessment

Project details

Problem: Bruxism, the involuntary grinding and clenching of teeth, is a prevalent condition affecting millions worldwide. Current diagnostic methods rely heavily on subjective patient reports and dental assessments, often failing to detect the condition until significant damage has occurred. This leads to delayed interventions, undertreatment, and a substantial burden on individuals and healthcare systems.

Innovation: Wearable sensor technologies hold the potential to revolutionise bruxism diagnosis and management. By unobtrusively collecting real-time data on a range of physiological cues, these devices could provide objective, quantifiable evidence of bruxism events, informing early diagnosis and personalised treatment strategies.

Project Aims: This PhD project aims to develop and validate a novel wearable sensor system for comprehensive bruxism assessment. Key objectives include:

• Multimodal Sensor Exploration and Selection: Research, identify, and integrate a suite of multimodal sensors capable of detecting the subtle physiological cues associated with bruxism events. This will involve considering factors such as sensitivity, wearability, and signal quality.
• Wearable System Design: Design a comfortable, secure, and user-friendly wearable device, such as a headband or custom mouthguard-integrated form factor. Signal processing and data transmission methods (wired or wireless) will be optimised for robust data capture in natural sleep environments.
• AI-Driven Analysis: Develop advanced AI algorithms to analyse the collected sensor data, aiming to:
• Distinguish bruxism episodes from normal jaw movements.
• Classify bruxism events based on intensity, duration, and associated patterns.
• Potentially identify correlations between bruxism patterns and other biometric data.
• Initial Validation: Conduct studies with healthy volunteers in controlled settings to collect a dataset for AI training and initial system validation.

Outcomes: This project has the potential to yield a breakthrough in bruxism diagnostics, leading to earlier detection, individualised treatment plans, and improved outcomes for patients. The developed wearable system and AI algorithms could also form the basis for future research in related fields, such as sleep disorders and temporomandibular joint (TMJ) dysfunction.

Entry Requirements:

Candidates must have a first or upper second class honours degree or significant research experience.

How to apply:

Please complete a University Postgraduate Research Application form available here: www.shef.ac.uk/postgraduate/research/apply

Please clearly state the prospective main supervisor in the respective box and select (School of Clinical Dentistry) as the department.