Training
Underwater sampling of heart rate for sports training has growing attention recently because of the availability of new sensors able to gather data while the user is swimming. Namely, optical sensor for the wrist and strap sensor for the chest. Underwater data transmission is not an option, forcing the analysis to be done off-line. Thus, movement and distance from heart could infuence the gap between data from sensors.
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This dataset is used in conjunction with the manuscript submission entitled: “Training with a Visual-Haptic Simulator for Trocar Insertion,” which is submitted for consideration as a short paper in Transactions on Haptics for presentation with the 2024 Haptics Symposium Conference. Trocar insertions have the greatest risk of injuring a patient during laparoscopic surgery, with most of the injuries being attributed to surgeon error. However, surgeon error can be reduced through proper training.
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The dataset and source code used in paper "Pick the Better and Leave the Rest: Leveraging Multiple Retrieved Results to Guide Response Generation".
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To treat visual attention defficiency and train pulmonary function simulataneously, we developed new Virtual reality(VR) system. Proposed VR system is consisted of three VR training games(Rocket game, Candle game and Food game).
We conducted user study on 24 ADHD children and collected Pulmonary function test(PFT), Advanced test of attention(ATA).
In this file, we coded data into the form that can be used SPSS statstic tool.
We wanted to compare date in two ways, pre-post comparision and between-subjects comparision.
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This dataset contains the images used in the paper "Fine-tuning a pre-trained Convolutional Neural Network Model to translate American Sign Language in Real-time".
M. E. Morocho Cayamcela and W. Lim, "Fine-tuning a pre-trained Convolutional Neural Network Model to translate American Sign Language in Real-time," 2019 International Conference on Computing, Networking and Communications (ICNC), Honolulu, HI, USA, 2019, pp. 100-104.
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