Deep learning for activity recognition
published: Nov. 17, 2017, recorded: September 2017, views: 1399
Report a problem or upload filesIf you have found a problem with this lecture or would like to send us extra material, articles, exercises, etc., please use our ticket system to describe your request and upload the data.
Enter your e-mail into the 'Cc' field, and we will keep you updated with your request's status.
Human activity recognition (HAR) plays an important role in people’s daily life by learning and identifying high-level knowledge about human activity from raw sensor inputs. Conventional pattern recognition approaches have made tremendous progress on HAR tasks by adopting machine learning algorithms such as decision tree, random forest or support vector machine, but the fast development and advancement of deep learning have overpass the accuracy of traditional machine learning results. This seminar is focused on Deep learning applied to HAR using wearable sensors. Current architectures used and how to implement them for achieving good results will be explained. Limitations and new challenges will be also discussed.
Link this pageWould you like to put a link to this lecture on your homepage?
Go ahead! Copy the HTML snippet !