Activity Recognition Evaluation Via Machine Learning
Date
Authors
Rameka, ANA
Connor, AM
Kruse, J
Supervisor
Item type
Journal Article
Degree name
Journal Title
Journal ISSN
Volume Title
Publisher
European Alliance for Innovation (EAI)
Abstract
With the proliferation of relatively cheap Internet of Things (IoT) devices, smart environments have been highlighted as an example of how the IoT can make our lives easier. Each of these ‘things’ produces data which can work in unison to react to its users. Machine learning makes use of this data to make inferences about our habits and activities, such as our buying preferences or likely commute destinations. However, this level of human inclusion within the IoT relies on indirect inferences from the usage of these devices or services. Activity recognition is already a widely researched area and could provide a more direct way of including humans within this system. This research explores the feasibility of using a cost effective, unobtrusive, single modality ground-based sensor matrix to track subtle pressure changes to predict user activity, in an effort to assess its ability to act as an intermediary interface between humans and digital systems such as the IoT.Description
Keywords
Activity Recognition; Machine Learning; Internet of Things; Smart Floors; Smart Chairs; Smart Environments
Source
AMSYS 18: e3; doi: 10.4108/eai.23-3-2018.161436
Publisher's version
Rights statement
Copyright © 2019 A.N.A. Rameka et al., licensed to EAI. This is an open access article distributed under the terms of the Creative Commons Attribution licence (http://creativecommons.org/licenses/by/3.0/), which permits unlimited use, distribution and reproduction in any medium so long as the original work is properly cited.