Abnormal bite force is an important risk factor for oral and maxillofacial disorders, which is a critical dilemma that dentists face every day without effective solutions. Therefore, it is of great clinical significance to develop a wireless bite force measurement device and explore quantitative measurement methods to help find effective strategies for improving occlusal diseases. This study designed the open window carrier of a bite force detection device through 3D printing technology, and then the stress sensors were integrated and embedded into a hollow structure. The sensor system mainly consisted of a pressure signal acquisition module, a main control module, and a server terminal. A machine learning algorithm will be leveraged for bite force data processing and parameter configuration in the future. This study implemented a sensor prototype system from scratch to fully evaluate each component of the intelligent device. The experimental results showed reasonable parameter metrics for the device carrier and demonstrated the feasibility of the proposed scheme for bite force measurement. An intelligent and wireless bite force device with a stress sensor system is a promising approach to occlusal disease diagnosis and treatment.
Keywords: engineering; machine learning; occlusal splints; osteoarthritis; printing; stress.