A 3D printed electronic wearable device to generate vertical, horizontal and phono-articulatory jaw movement parameters: A concept implementation

Abstract

Purpose The current research aimed to develop a concept open-source 3D printable, electronic wearable head gear to record jaw movement parameters. Materials & methods A 3D printed wearable device was designed and manufactured then fitted with open-source sensors to record vertical, horizontal and phono-articulatory jaw motions. Mean deviation and relative error were measured invitro. The device was implemented on two volunteers for the parameters of maximum anterior protrusion (MAP), maximum lateral excursion (MLE), normal (NMO), and maximum (MMO) mouth opening and fricative phono-articulation. Raw data was normalized using z-score and root mean squared error (RMSE) values were used to evaluate relative differences in readings across the two participants. Results RMSE differences across the left and right piezoresistive sensors demonstrated near similar bilateral movements during normal (0.12) and maximal mouth (0.09) opening for participant 1, while varying greatly for participant 2 (0.25 and 0.14, respectively). There were larger differences in RMSE during accelerometric motion in different axes for MAP, MLE and Fricatives. Conclusion The current implementation demonstrated that a 3D printed electronic wearable device with open-source sensor technology can record horizontal, vertical, and phono-articulatory maxillomandibular movements in two participants. However, future efforts must be made to overcome the limitations documented within the current experiment.

Deep learning and predictive modelling for generating normalised muscle function parameters from signal images of mandibular electromyography

“…In the current research landscape, several gaps have been identified: Despite the widespread application of data normalisation and synthetic signal processing in various medical signal-based datasets, the normalisation of EMG signals from masticatory muscles for broad applicability and correlation with similar image datasets from different facilities, devices and varying timeframes remains unexplored [ 22 ]. The development of a image-to-signal conversion system is necessary for mandibular EMG, but it must ensure that the converted signal features are classifiable through clustering.…”

“…Despite the widespread application of data normalisation and synthetic signal processing in various medical signal-based datasets, the normalisation of EMG signals from masticatory muscles for broad applicability and correlation with similar image datasets from different facilities, devices and varying timeframes remains unexplored [ 22 ].…”

Variables Associated with Jaw Clicking in a South Australian Population: A Cross-Sectional Study

“…Surface electromyography (BioEMG III, BioResearch Associates Inc., Brown Deer, WI, USA) to record masticatory muscle activity during mouth opening and closing. • Joint vibration analysis (JVA; BioResearch Associates Inc., Brown Deer, WI, USA) to evaluate TMJ function, producing mean vibration integrals and amplitudes above 300 Hz to assess joint complex quality during opening [16,21].…”