Dataset Information

Data on toothbrushing study comparing infrared-based motion tracking versus video observation.

ABSTRACT: Investigations of toothbrushing habits are an important vector to understand their influence on brushing effectiveness. User compliance in toothbrushing is known to deviate from professional recommendations in brushing time, evenness across all areas of the dentition, and brushing force [1,2]. Despite the recent development of tools designed to guide users to optimised brushing habits [3,4], research on habit evaluation and tracking is limited and typically relies on labour-intensive video observation (VO) [5]. Here we present raw data on toothbrush position as determined by an automated motion tracking (MT) capability and by human VO and provide a technical description of the MT capability. The MT system described in this article was developed in collaboration with Soft2Tec GmbH (Rüsselsheim, Germany) as a potential substitute for the VO tool. The MT system consists of a monocular vision module and a target module with active infrared LED trackers. The MT system determined the position and orientation of a toothbrush relative to the jaw while subjects brushed under realistic conditions. For VO, a trained assessor coded video recording data from toothbrushing sessions. The data presented here describes a clinical study (103 subjects; 46 completed two sessions, 57 completed one session, altogether 149 events) comparing toothbrushing behaviour recorded with the MT system and with VO simultaneously. The raw data was deposited in Mendeley Data, under data identification number doi:10.17632/4f384xrbhm.1 [https://data.mendeley.com/datasets/4f384xrbhm/1].

SUBMITTER: Wolf M

PROVIDER: S-EPMC7316997 | biostudies-literature | 2020 Aug

REPOSITORIES: biostudies-literature

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Data on toothbrushing study comparing infrared-based motion tracking versus video observation.

Wolf Michael M Klein Patrick P Engelmohr Reiner R Erb Jasmin J Gübler René R

Data in brief 20200616

Investigations of toothbrushing habits are an important vector to understand their influence on brushing effectiveness. User compliance in toothbrushing is known to deviate from professional recommendations in brushing time, evenness across all areas of the dentition, and brushing force [1,2]. Despite the recent development of tools designed to guide users to optimised brushing habits [3,4], research on habit evaluation and tracking is limited and typically relies on labour-intensive video obser ...[more]

PMID: 32613045

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Project description:By modifying and calibrating an active vertex model to experiments, we have simulated numerically a confluent cellular monolayer spreading on an empty space and the collision of two monolayers of different cells in an antagonistic migration assay. Cells are subject to inertial forces and to active forces that try to align their velocities with those of neighboring ones. In agreement with experiments in the literature, the spreading test exhibits formation of fingers in the moving interfaces, there appear swirls in the velocity field, and the polar order parameter and the correlation and swirl lengths increase with time. Numerical simulations show that cells inside the tissue have smaller area than those at the interface, which has been observed in recent experiments. In the antagonistic migration assay, a population of fluidlike Ras cells invades a population of wild type solidlike cells having shape parameters above and below the geometric critical value, respectively. Cell mixing or segregation depends on the junction tensions between different cells. We reproduce the experimentally observed antagonistic migration assays by assuming that a fraction of cells favor mixing, the others segregation, and that these cells are randomly distributed in space. To characterize and compare the structure of interfaces between cell types or of interfaces of spreading cellular monolayers in an automatic manner, we apply topological data analysis to experimental data and to results of our numerical simulations. We use time series of data generated by numerical simulations to automatically group, track and classify the advancing interfaces of cellular aggregates by means of bottleneck or Wasserstein distances of persistent homologies. These techniques of topological data analysis are scalable and could be used in studies involving large amounts of data. Besides applications to wound healing and metastatic cancer, these studies are relevant for tissue engineering, biological effects of materials, tissue and organ regeneration.

Project description:Introduction: Video-based clinical rating plays an important role in assessing dystonia and monitoring the effect of treatment in dyskinetic cerebral palsy (CP). However, evaluation by clinicians is time-consuming, and the quality of rating is dependent on experience. The aim of the current study is to provide a proof-of-concept for a machine learning approach to automatically assess scoring of dystonia using 2D stick figures extracted from videos. Model performance was compared to human performance. Methods: A total of 187 video sequences of 34 individuals with dyskinetic CP (8-23 years, all non-ambulatory) were filmed at rest during lying and supported sitting. Videos were scored by three raters according to the Dyskinesia Impairment Scale (DIS) for arm and leg dystonia (normalized scores ranging from 0-1). Coordinates in pixels of the left and right wrist, elbow, shoulder, hip, knee and ankle were extracted using DeepLabCut, an open source toolbox that builds on a pose estimation algorithm. Within a subset, tracking accuracy was assessed for a pretrained human model and for models trained with an increasing number of manually labeled frames. The mean absolute error (MAE) between DeepLabCut's prediction of the position of body points and manual labels was calculated. Subsequently, movement and position features were calculated from extracted body point coordinates. These features were fed into a Random Forest Regressor to train a model to predict the clinical scores. The model performance trained with data from one rater evaluated by MAEs (model-rater) was compared to inter-rater accuracy. Results: A tracking accuracy of 4.5 pixels (approximately 1.5 cm) could be achieved by adding 15-20 manually labeled frames per video. The MAEs for the trained models ranged from 0.21 ± 0.15 for arm dystonia to 0.14 ± 0.10 for leg dystonia (normalized DIS scores). The inter-rater MAEs were 0.21 ± 0.22 and 0.16 ± 0.20, respectively. Conclusion: This proof-of-concept study shows the potential of using stick figures extracted from common videos in a machine learning approach to automatically assess dystonia. Sufficient tracking accuracy can be reached by manually adding labels within 15-20 frames per video. With a relatively small data set, it is possible to train a model that can automatically assess dystonia with a performance comparable to human scoring.

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Data on toothbrushing study comparing infrared-based motion tracking versus video observation.

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Data on toothbrushing study comparing infrared-based motion tracking versus video observation.

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