Patient’s Body Motion Study using Multimodal RGBDT Videos

Publikation: Bidrag til bog/antologi/rapport/konference proceedingKonferenceartikel i proceedingForskningpeer review

Abstract

Automatic analysis of body movement to identify physical activity of patients who are at bed rest is crucial for treatment or rehabilitation purposes. Existing methods of physical activity analysis mostly focused on the detection of primitive motion/non-motion states in unimodal video data captured by either RGB or depth or thermal sensor. In this paper, we propose a multimodal visionbased approach to classify body motion of a person lying on a bed. We mimicked a real scenario of ’patient on bed’ by recording multimodal video data from healthy volunteers in a hospital room in a neurorehabilitation center. We first defined a taxonomy of possible physical activities based on observations of patients with acquired brain injuries. We then investigated different motion analysis and machine learning approaches to classify physical activities automatically. A multimodal database including RGB, depth and thermal videos was collected and annotated with eight predefined physical activities. Experimental results show that we can achieve moderately high accuracy (77.68%) to classify physical activities by tracking the body motion using an optical flow-based approach. To the best of our knowledge this is the first multimodal RGBDT video analysis for such application.
Luk

Detaljer

Automatic analysis of body movement to identify physical activity of patients who are at bed rest is crucial for treatment or rehabilitation purposes. Existing methods of physical activity analysis mostly focused on the detection of primitive motion/non-motion states in unimodal video data captured by either RGB or depth or thermal sensor. In this paper, we propose a multimodal visionbased approach to classify body motion of a person lying on a bed. We mimicked a real scenario of ’patient on bed’ by recording multimodal video data from healthy volunteers in a hospital room in a neurorehabilitation center. We first defined a taxonomy of possible physical activities based on observations of patients with acquired brain injuries. We then investigated different motion analysis and machine learning approaches to classify physical activities automatically. A multimodal database including RGB, depth and thermal videos was collected and annotated with eight predefined physical activities. Experimental results show that we can achieve moderately high accuracy (77.68%) to classify physical activities by tracking the body motion using an optical flow-based approach. To the best of our knowledge this is the first multimodal RGBDT video analysis for such application.
OriginalsprogEngelsk
TitelAdvances in Visual Computing : 13th International Symposium, ISVC 2018, Las Vegas, NV, USA, November 19 – 21, 2018, Proceedings
Antal sider12
ForlagSpringer
Publikationsdato2018
Sider552-564
ISBN (Trykt)978-3-030-03800-7
ISBN (Elektronisk)978-3-030-03801-4
DOI
StatusUdgivet - 2018
PublikationsartForskning
Peer reviewJa
BegivenhedInternational Symposium on Visual Computing, ISVC 2018 - Las Vegas, USA
Varighed: 19 nov. 201821 nov. 2018
Konferencens nummer: 13

Konference

KonferenceInternational Symposium on Visual Computing, ISVC 2018
Nummer13
LandUSA
ByLas Vegas
Periode19/11/201821/11/2018
NavnLecture Notes in Computer Science
Volume/Bind11241
ISSN0302-9743

Download-statistik

Ingen data tilgængelig
ID: 288882484