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

ACCESS DATA

Publications

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

Similar Datasets

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:End-effector-based robotic systems provide easy-to-set-up motion support in rehabilitation of stroke and spinal-cord-injured patients. However, measurement information is obtained only about the motion of the limb segments to which the systems are attached and not about the adjacent limb segments. We demonstrate in one particular experimental setup that this limitation can be overcome by augmenting an end-effector-based robot with a wearable inertial sensor. Most existing inertial motion tracking approaches rely on a homogeneous magnetic field and thus fail in indoor environments and near ferromagnetic materials and electronic devices. In contrast, we propose a magnetometer-free sensor fusion method. It uses a quaternion-based algorithm to track the heading of a limb segment in real time by combining the gyroscope and accelerometer readings with position measurements of one point along that segment. We apply this method to an upper-limb rehabilitation robotics use case in which the orientation and position of the forearm and elbow are known, and the orientation and position of the upper arm and shoulder are estimated by the proposed method using an inertial sensor worn on the upper arm. Experimental data from five healthy subjects who performed 282 proper executions of a typical rehabilitation motion and 163 executions with compensation motion are evaluated. Using a camera-based system as a ground truth, we demonstrate that the shoulder position and the elbow angle are tracked with median errors around 4 cm and 4°, respectively; and that undesirable compensatory shoulder movements, which were defined as shoulder displacements greater ±10 cm for more than 20% of a motion cycle, are detected and classified 100% correctly across all 445 performed motions. The results indicate that wearable inertial sensors and end-effector-based robots can be combined to provide means for effective rehabilitation therapy with likewise detailed and accurate motion tracking for performance assessment, real-time biofeedback and feedback control of robotic and neuroprosthetic motion support.

Project description:BACKGROUND:Sleep apnea is a respiratory disorder characterized by an intermittent reduction (hypopnea) or cessation (apnea) of breathing during sleep. Depending on the presence of a breathing effort, sleep apnea is divided into obstructive sleep apnea (OSA) and central sleep apnea (CSA) based on the different pathologies involved. If the majority of apneas in a person are obstructive, they will be diagnosed as OSA or otherwise as CSA. In addition, as it is challenging and highly controversial to divide hypopneas into central or obstructive, the decision about sleep apnea type (OSA vs CSA) is made based on apneas only. Choosing the appropriate treatment relies on distinguishing between obstructive apnea (OA) and central apnea (CA). OBJECTIVE:The objective of this study was to develop a noncontact method to distinguish between OAs and CAs. METHODS:Five different computer vision-based algorithms were used to process infrared (IR) video data to track and analyze body movements to differentiate different types of apnea (OA vs CA). In the first two methods, supervised classifiers were trained to process optical flow information. In the remaining three methods, a convolutional neural network (CNN) was designed to extract distinctive features from optical flow and to distinguish OA from CA. RESULTS:Overnight sleeping data of 42 participants (mean age 53, SD 15 years; mean BMI 30, SD 7 kg/m2; 27 men and 15 women; mean number of OA 16, SD 30; mean number of CA 3, SD 7; mean apnea-hypopnea index 27, SD 31 events/hour; mean sleep duration 5 hours, SD 1 hour) were collected for this study. The test and train data were recorded in two separate laboratory rooms. The best-performing model (3D-CNN) obtained 95% accuracy and an F1 score of 89% in differentiating OA vs CA. CONCLUSIONS:In this study, the first vision-based method was developed that differentiates apnea types (OA vs CA). The developed algorithm tracks and analyses chest and abdominal movements captured via an IR video camera. Unlike previously developed approaches, this method does not require any attachment to a user that could potentially alter the sleeping condition.

Dataset Information

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

Publications

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

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure