Accurate Fall Detection in a Top View Privacy Preserving Configuration
Fall detection is one of the most investigated themes in the research on assistive solutions for aged people. In particular, a false-alarm-free discrimination between falls and non-falls is indispensable, especially to assist elderly people living alone. Current technological solutions designed to m...
Main Authors: | , , |
---|---|
Format: | Article |
Language: | English |
Published: |
MDPI AG
2018-05-01
|
Series: | Sensors |
Subjects: | |
Online Access: | http://www.mdpi.com/1424-8220/18/6/1754 |
id |
doaj-59230cc6bf4b4027907d9ed6a3473d50 |
---|---|
record_format |
Article |
spelling |
doaj-59230cc6bf4b4027907d9ed6a3473d502020-11-25T01:05:58ZengMDPI AGSensors1424-82202018-05-01186175410.3390/s18061754s18061754Accurate Fall Detection in a Top View Privacy Preserving ConfigurationManola Ricciuti0Susanna Spinsante1Ennio Gambi2Dipartimento di Ingegneria dell’Informazione, Università Politecnica delle Marche via Brecce Bianche 12, 60131 Ancona, ItalyDipartimento di Ingegneria dell’Informazione, Università Politecnica delle Marche via Brecce Bianche 12, 60131 Ancona, ItalyDipartimento di Ingegneria dell’Informazione, Università Politecnica delle Marche via Brecce Bianche 12, 60131 Ancona, ItalyFall detection is one of the most investigated themes in the research on assistive solutions for aged people. In particular, a false-alarm-free discrimination between falls and non-falls is indispensable, especially to assist elderly people living alone. Current technological solutions designed to monitor several types of activities in indoor environments can guarantee absolute privacy to the people that decide to rely on them. Devices integrating RGB and depth cameras, such as the Microsoft Kinect, can ensure privacy and anonymity, since the depth information is considered to extract only meaningful information from video streams. In this paper, we propose an accurate fall detection method investigating the depth frames of the human body using a single device in a top-view configuration, with the subjects located under the device inside a room. Features extracted from depth frames train a classifier based on a binary support vector machine learning algorithm. The dataset includes 32 falls and 8 activities considered for comparison, for a total of 800 sequences performed by 20 adults. The system showed an accuracy of 98.6% and only one false positive.http://www.mdpi.com/1424-8220/18/6/1754fall detectionADLsKinectdepth frameprivacymachine learningelderly people |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Manola Ricciuti Susanna Spinsante Ennio Gambi |
spellingShingle |
Manola Ricciuti Susanna Spinsante Ennio Gambi Accurate Fall Detection in a Top View Privacy Preserving Configuration Sensors fall detection ADLs Kinect depth frame privacy machine learning elderly people |
author_facet |
Manola Ricciuti Susanna Spinsante Ennio Gambi |
author_sort |
Manola Ricciuti |
title |
Accurate Fall Detection in a Top View Privacy Preserving Configuration |
title_short |
Accurate Fall Detection in a Top View Privacy Preserving Configuration |
title_full |
Accurate Fall Detection in a Top View Privacy Preserving Configuration |
title_fullStr |
Accurate Fall Detection in a Top View Privacy Preserving Configuration |
title_full_unstemmed |
Accurate Fall Detection in a Top View Privacy Preserving Configuration |
title_sort |
accurate fall detection in a top view privacy preserving configuration |
publisher |
MDPI AG |
series |
Sensors |
issn |
1424-8220 |
publishDate |
2018-05-01 |
description |
Fall detection is one of the most investigated themes in the research on assistive solutions for aged people. In particular, a false-alarm-free discrimination between falls and non-falls is indispensable, especially to assist elderly people living alone. Current technological solutions designed to monitor several types of activities in indoor environments can guarantee absolute privacy to the people that decide to rely on them. Devices integrating RGB and depth cameras, such as the Microsoft Kinect, can ensure privacy and anonymity, since the depth information is considered to extract only meaningful information from video streams. In this paper, we propose an accurate fall detection method investigating the depth frames of the human body using a single device in a top-view configuration, with the subjects located under the device inside a room. Features extracted from depth frames train a classifier based on a binary support vector machine learning algorithm. The dataset includes 32 falls and 8 activities considered for comparison, for a total of 800 sequences performed by 20 adults. The system showed an accuracy of 98.6% and only one false positive. |
topic |
fall detection ADLs Kinect depth frame privacy machine learning elderly people |
url |
http://www.mdpi.com/1424-8220/18/6/1754 |
work_keys_str_mv |
AT manolaricciuti accuratefalldetectioninatopviewprivacypreservingconfiguration AT susannaspinsante accuratefalldetectioninatopviewprivacypreservingconfiguration AT enniogambi accuratefalldetectioninatopviewprivacypreservingconfiguration |
_version_ |
1725192100644913152 |