Low-Cost Automatic Ambient Assisted Living System

The recent increase in ageing population in countries around the world has brought a lot of attention toward research and development of ambient assisted living (AAL) systems. These systems should be inexpensive to be installed in elderly homes, protecting their privacy and more importantly being no...

Full description

Saved in:

Bibliographic Details
Published in:	2018 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops) pp. 693 - 697
Main Authors:	Malekmohamadi, Hossein, Moemeni, Armaghan, Orun, Ahmet, Purohit, Jayendra Kumar
Format:	Conference Proceeding
Language:	English
Published:	IEEE 01-03-2018
Subjects:	Ambient assisted living Assisted Living Forestry Hidden Markov models Human activity recognition kinect multi Layer Perceptron Naïve Bayes Radio frequency Random Forest Skeleton Three-dimensional displays Training
Online Access:	Get full text
Tags:	Add Tag No Tags, Be the first to tag this record!

Description
Summary:	The recent increase in ageing population in countries around the world has brought a lot of attention toward research and development of ambient assisted living (AAL) systems. These systems should be inexpensive to be installed in elderly homes, protecting their privacy and more importantly being non-invasive and smart. In this paper, we introduce an inexpensive system that utilises off-the-shelf sensor to grab RGB-D data. This data is then fed into different learning algorithms for classification different activity types. We achieve a very good success rate (99.9%) for human activity recognition (HAR) with the help of light-weighted and fast random forests (RF).
DOI:	10.1109/PERCOMW.2018.8480390