Skip to content
No description, website, or topics provided.
Branch: master
Clone or download
Fetching latest commit…
Cannot retrieve the latest commit at this time.
Permalink
Type Name Latest commit message Commit time
Failed to load latest commit information.
README.md

README.md

Analysis of Medical and Sports Sensor Data Using Deep Learning - AMSS

The development of intelligent medical and sports data analysis systems has experienced a significant boost in recent years thanks to the emergence of a machine learning paradigm known as deep learning (DL). DL algorithms have enabled the development of highly accurate systems (with performance comparable to that of human experts, in some cases) and have become a standard choice for analyzing medical and sport data, especially images and videos. Dozens of commercial applications using deep learning to analyze, classify, segment and measure data from different modalities of sensors are currently available. Deep learning methods applied on medical and sports data are contributing to understand the evolution of chronic diseases, predicting the risk of developing those diseases, and understanding the performance of athletes and their risk of overuse injuries. Researchers in industry, hospitals, sports institutes and academia have published hundreds of scientific contributions in this area during the last year alone.

The presented workshop is meant as a forum for the discussion of the impact of deep learning on medical and sport sensor data analysis and a focused venue for sharing novel scientific contributions in the area of deep learning.

Topics of interest include (but are not limited to):

· Novel approaches for medical and sport sensor data classification, event detection, segmentation, and abnormality detection using DL;

· DL for injury analysis;

· DL for image medical data analysis;

· Content-Based Sensor Data Retrieval (CSDR) using DL;

· Medical and Sport Sensor data understanding using DL;

· Medical and Sport Sensor data visualisation;

· Sensor data generation and preprocessing methods using unsupervised DL like GANs, autoencoders, etc.;

· Multimodal analysis and fusion using DL;

· Applications of DL in different fields and disciplines.

· Combination of Sports and health data.

· Human behavior modelling using DL;

Authors are invited to submit their original contributions before the deadline following the conference submission guidelines. Each contribution must be prepared following the ACM two-column format, and should not exceed the length of 6 (six) Letter-sized pages. For detailed instructions, please visit the conference homepage. (URL to be added)

Paper submission guidelines Please follow the description at the conference web page. (URL to be added)

Important dates: Tba.

Chairs:

Michael Riegler, SimulaMet & Krisitiania, Norway [michael@simula.no]

Pål Halvorsen, SimulaMet, Norway [paalh@simula.no]

Steven Hicks, SimulaMet, Norway | [steven@simula.no]

Enrique Garcia Ceja, SINTEF, Norway [enrique.garcia-ceja@sintef.no]

Tor-Morten Grønli, Kristiania [Tor-Morten.Gronli@kristiania.no]

You can’t perform that action at this time.