NDLI: Skeleton-based human activity recognition for elderly monitoring systems

Content Provider	IET Digital Library
Author	Hbali, Youssef Hbali, Sara Ballihi, Lahoucine Sadgal, Mohammed
Abstract	There is a significantly increasing demand for monitoring systems for elderly people in the health-care sector. As the aging population increases, patient privacy violations and the cost of elderly assistance have driven the research community toward computer vision and image processing to design and deploy new systems for monitoring the elderly in the authors’ society and turning their living houses into smart environments. By exploiting recent advances and the low cost of three-dimensional (3D) depth sensors such as Microsoft Kinect, the authors propose a new skeleton-based approach to describe the spatio-temporal aspects of a human activity sequence, using the Minkowski and cosine distances between the 3D joints. We trained and validated their approach on the Microsoft MSR 3D Action and MSR Daily Activity 3D datasets using the Extremely Randomised Trees algorithm. The results are very promising, demonstrating that the trained model can be used to build a monitoring system for the elderly using open-source libraries and a low-cost depth sensor.
Starting Page	16
Ending Page	26
Page Count	11
ISSN	17519632
Volume Number	12
e-ISSN	17519640
Issue Number	Issue 1, Feb (2018)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-cvi/12/1
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-cvi.2017.0062
Journal	IET Computer Vision
Publisher Date	2017-08-08
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	3D Depth Sensors Aging Population Aids For The Handicapped Computer Assistance For Persons with Handicaps Computer Vision Computer Vision And Image Processing Technique Cosine Distance Elderly Monitoring System Extremely Randomised Trees Algorithm Handicapped Aids Home Computing Human Activity Sequence Image Processing Image Recognition Image Sequence Image Sonsor Low-cost Depth Sensor Microsoft Kinect Microsoft MSR 3D Action Dataset Minkowski Distance MSR Daily Activity 3D Dataset Open-source Libraries Skeleton-based Approach Skeleton-based Human Activity Recognition
Content Type	Text
Resource Type	Article
Subject	Computer Vision and Pattern Recognition Software

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

Skeleton-Based Human Action Recognition via Screw Matrices

Action recognition using vague division DMMs

Generation of synthetic Kinect depth images based on empirical noise model

Human pose estimation method based on single depth image

Learning to recognise 3D human action from a new skeleton-based representation using deep convolutional neural networks

Mixing body-parts model for 2D human pose estimation in stereo videos

DTW-based kernel and rank-level fusion for 3D gait recognition using Kinect

Vision skeleton trajectory based motion assessment system for healthcare rehabilitation

Maintenance robot motion control based on Kinect gesture recognition

Skeleton-based human activity recognition for elderly monitoring systems

Similar Documents

Skeleton-Based Human Action Recognition via Screw Matrices

Action recognition using vague division DMMs

Generation of synthetic Kinect depth images based on empirical noise model

Human pose estimation method based on single depth image

Learning to recognise 3D human action from a new skeleton-based representation using deep convolutional neural networks

Mixing body-parts model for 2D human pose estimation in stereo videos

DTW-based kernel and rank-level fusion for 3D gait recognition using Kinect

Vision skeleton trajectory based motion assessment system for healthcare rehabilitation

Maintenance robot motion control based on Kinect gesture recognition

Skeleton-based human activity recognition for elderly monitoring systems