Next Article in Journal
Reduced-Drift Virtual Gyro from an Array of Low-Cost Gyros
Next Article in Special Issue
An Improved Approach for RSSI-Based only Calibration-Free Real-Time Indoor Localization on IEEE 802.11 and 802.15.4 Wireless Networks
Previous Article in Journal
A Passive Learning Sensor Architecture for Multimodal Image Labeling: An Application for Social Robots
Previous Article in Special Issue
Reliable Identification of Vehicle-Boarding Actions Based on Fuzzy Inference Syste
Article Menu
Issue 2 (February) cover image

Export Article

Open AccessArticle
Sensors 2017, 17(2), 351; doi:10.3390/s17020351

Assessing Human Activity in Elderly People Using Non-Intrusive Load Monitoring

Electronics Department, University of Alcalá, Escuela Politécnica, Ctra. Madrid-Barcelona, Km. 33,600, 28871 Alcalá de Henares, Spain
*
Author to whom correspondence should be addressed.
Academic Editor: Leonhard M. Reindl
Received: 30 November 2016 / Revised: 21 January 2017 / Accepted: 25 January 2017 / Published: 11 February 2017
View Full-Text   |   Download PDF [4757 KB, uploaded 11 February 2017]   |  

Abstract

The ageing of the population, and their increasing wish of living independently, are motivating the development of welfare and healthcare models. Existing approaches based on the direct heath-monitoring using body sensor networks (BSN) are precise and accurate. Nonetheless, their intrusiveness causes non-acceptance. New approaches seek the indirect monitoring through monitoring activities of daily living (ADLs), which proves to be a suitable solution. ADL monitoring systems use many heterogeneous sensors, are less intrusive, and are less expensive than BSN, however, the deployment and maintenance of wireless sensor networks (WSN) prevent them from a widespread acceptance. In this work, a novel technique to monitor the human activity, based on non-intrusive load monitoring (NILM), is presented. The proposal uses only smart meter data, which leads to minimum intrusiveness and a potential massive deployment at minimal cost. This could be the key to develop sustainable healthcare models for smart homes, capable of complying with the elderly people’ demands. This study also uses the Dempster-Shafer theory to provide a daily score of normality with regard to the regular behavior. This approach has been evaluated using real datasets and, additionally, a benchmarking against a Gaussian mixture model approach is presented. View Full-Text
Keywords: non-intrusive load monitoring; activity monitoring; ambient intelligence (AmI); activity recognition (AR); ambient assisted living (AAL) non-intrusive load monitoring; activity monitoring; ambient intelligence (AmI); activity recognition (AR); ambient assisted living (AAL)
Figures

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 alert for new publications

Never miss any articles matching your research from any publisher
  • Get alerts for new papers matching your research
  • Find out the new papers from selected authors
  • Updated daily for 49'000+ journals and 6000+ publishers
  • Define your Scifeed now

SciFeed Share & Cite This Article

MDPI and ACS Style

Alcalá, J.M.; Ureña, J.; Hernández, Á.; Gualda, D. Assessing Human Activity in Elderly People Using Non-Intrusive Load Monitoring. Sensors 2017, 17, 351.

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