Dataset Information

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.

SUBMITTER: Farook TH

PROVIDER: S-EPMC10499219 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature

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Publications

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

Farook Taseef Hasan TH Ahmed Saif S Talukder Md Shoriful Islam MSI Dudley James J

PloS one 20230913 9

<h4>Purpose</h4>The current research aimed to develop a concept open-source 3D printable, electronic wearable head gear to record jaw movement parameters.<h4>Materials & methods</h4>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 (MA ...[more]

PMID: 37703272

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Project description:IntroductionThis study examined the potential of a device agnostic approach for predicting physical activity from consumer wearable accelerometry compared with a research-grade accelerometry.MethodsSeventy-five 5- to 12-year-olds (58% male, 63% White) participated in a 60-min protocol. Children wore wrist-placed consumer wearables (Apple Watch Series 7 and Garmin Vivoactive 4) and a research-grade device (ActiGraph GT9X) concurrently with an indirect calorimeter (COSMED K5). Activity intensities (i.e., inactive, light, moderate-to-vigorous physical activity) were estimated via indirect calorimetry (criterion), and the Hildebrand thresholds were applied to the raw accelerometer data from the consumer wearables and research-grade device. Epoch-by-epoch (e.g., weighted sensitivity, specificity) and discrepancy (e.g., mean bias, absolute error) analyses evaluated agreement between accelerometry-derived and criterion estimates. Equivalence testing evaluated the equivalence of estimates produced by the consumer wearables and ActiGraph.ResultsEstimates produced by the raw accelerometry data from ActiGraph, Apple, and Garmin produced similar criterion agreement with weighted sensitivity = 68.2% (95% confidence interval (CI), 67.1%-69.3%), 73.0% (95% CI, 71.8%-74.3%), and 66.6% (95% CI, 65.7%-67.5%), respectively, and weighted specificity = 84.4% (95% CI, 83.6%-85.2%), 82.0% (95% CI, 80.6%-83.4%), and 75.3% (95% CI, 74.7%-75.9%), respectively. Apple Watch produced the lowest mean bias (inactive, -4.0 ± 4.5; light activity, 2.1 ± 4.0) and absolute error (inactive, 4.9 ± 3.4; light activity, 3.6 ± 2.7) for inactive and light physical activity minutes. For moderate-to-vigorous physical activity, ActiGraph produced the lowest mean bias (1.0 ± 2.9) and absolute error (2.8 ± 2.4). No ActiGraph and consumer wearable device estimates were statistically significantly equivalent.ConclusionsRaw accelerometry estimated inactive and light activity from wrist-placed consumer wearables performed similarly to, if not better than, a research-grade device, when compared with indirect calorimetry. This proof-of-concept study highlights the potential of device-agnostic methods for quantifying physical activity intensity via consumer wearables.

Dataset Information

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

Purpose

Materials & methods

Results

Conclusion

Publications

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

Similar Datasets

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