Next Article in Journal
Collaborative Resource Management for Negotiable Multi-Operator Small Cell Networks
Previous Article in Journal
putEMG—A Surface Electromyography Hand Gesture Recognition Dataset
Previous Article in Special Issue
Adaptive Motion Artifact Reduction Based on Empirical Wavelet Transform and Wavelet Thresholding for the Non-Contact ECG Monitoring Systems
Open AccessArticle

AI-Based Early Change Detection in Smart Living Environments

CNR—National Research Council of Italy, IMM—Institute for Microelectronics and Microsystems, 73100 Lecce, Italy
*
Author to whom correspondence should be addressed.
Sensors 2019, 19(16), 3549; https://doi.org/10.3390/s19163549
Received: 27 June 2019 / Revised: 29 July 2019 / Accepted: 9 August 2019 / Published: 14 August 2019
(This article belongs to the Special Issue Signal Processing for Intelligent Sensor Systems)
  |  
PDF [8740 KB, uploaded 14 August 2019]
  |  

Abstract

In the smart environments we live today, a great variety of heterogeneous sensors are being increasingly deployed with the aim of providing more and more value-added services. This huge availability of sensor data, together with emerging Artificial Intelligence (AI) methods for Big Data analytics, can yield a wide array of actionable insights to help older adults continue to live independently with minimal support of caregivers. In this regard, there is a growing demand for technological solutions able to monitor human activities and vital signs in order to early detect abnormal conditions, avoiding the caregivers’ daily check of the care recipient. The aim of this study is to compare state-of-the-art machine and deep learning techniques suitable for detecting early changes in human behavior. At this purpose, specific synthetic data are generated, including activities of daily living, home locations in which such activities take place, and vital signs. The achieved results demonstrate the superiority of unsupervised deep-learning techniques over traditional supervised/semi-supervised ones in terms of detection accuracy and lead-time of prediction. View Full-Text
Keywords: artificial intelligence; machine learning; deep learning; smart living; multi-sensor system; big data analytics; change detection; human behavior; activity of daily living; ambient assisted living artificial intelligence; machine learning; deep learning; smart living; multi-sensor system; big data analytics; change detection; human behavior; activity of daily living; ambient assisted living
Figures

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
SciFeed

Share & Cite This Article

MDPI and ACS Style

Diraco, G.; Leone, A.; Siciliano, P. AI-Based Early Change Detection in Smart Living Environments. Sensors 2019, 19, 3549.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Sensors EISSN 1424-8220 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top