Next Article in Journal
Deep Learning for Detecting Building Defects Using Convolutional Neural Networks
Previous Article in Journal
HoloLens-Based AR System with a Robust Point Set Registration Algorithm
Previous Article in Special Issue
Rethinking Family-Centred Design Approach Towards Creating Digital Products and Services
Open AccessArticle

Time-Frequency Characteristics of In-Home Radio Channels Influenced by Activities of the Home Occupant

1
Faculty of Engineering and Science, University of Agder, P.O. Box 509, 4898 Grimstad, Norway
2
SuperRadio, Toftes Gate 2, 0556 Oslo, Norway
*
Author to whom correspondence should be addressed.
Sensors 2019, 19(16), 3557; https://doi.org/10.3390/s19163557
Received: 22 June 2019 / Revised: 31 July 2019 / Accepted: 10 August 2019 / Published: 15 August 2019
(This article belongs to the Special Issue Sensing Technologies for Ambient Assisted Living and Smart Homes)
  |  
PDF [1312 KB, uploaded 15 August 2019]
  |  

Abstract

While aging is a serious global concern, in-home healthcare monitoring solutions are limited to context-aware systems and wearable sensors, which may easily be forgotten or ignored for privacy and comfort reasons. An emerging non-wearable fall detection approach is based on processing radio waves reflected off the body, who has no active interaction with the system. This paper reports on an indoor radio channel measurement campaign at 5.9 GHz, which has been conducted to study the impact of fall incidents and some daily life activities on the temporal and spectral properties of the indoor channel under both line-of-sight (LOS) and obstructed-LOS (OLOS) propagation conditions. The time-frequency characteristic of the channel has been thoroughly investigated by spectrogram analysis. Studying the instantaneous Doppler characteristics shows that the Doppler spread ignores small variations of the channel (especially under OLOS conditions), but highlights coarse ones caused by falls. The channel properties studied in this paper can be considered to be new useful metrics for the design of reliable fall detection algorithms. We share all measured data files with the community through Code Ocean. The data can be used for validating a new class of channel models aiming at the design of smart activity recognition systems via a software-based approach. View Full-Text
Keywords: non-stationary radio channels; spectrogram analysis; Doppler power spectral density; device-free fall detection; ambient-assisted living non-stationary radio channels; spectrogram analysis; Doppler power spectral density; device-free fall detection; 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

Borhani, A.; Pätzold, M.; Yang, K. Time-Frequency Characteristics of In-Home Radio Channels Influenced by Activities of the Home Occupant. Sensors 2019, 19, 3557.

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