NDLI: Multi-modal Convolutional Neural Networks for Activity Recognition

Content Provider	IEEE Xplore Digital Library
Author	Sojeong Ha Jeong-Min Yun Seungjin Choi
Copyright Year	2015
Description	Author affiliation: Dept. of Comput. Sci. & Eng., Pohang Univ. of Sci. & Technol., Pohang, South Korea (Sojeong Ha; Jeong-Min Yun; Seungjin Choi)
Abstract	Convolutional neural network (CNN), which comprises one or more convolutional and pooling layers followed by one or more fully-connected layers, has gained popularity due to its ability to learn fruitful representations from images or speeches, capturing local dependency and slight-distortion invariance. CNN has recently been applied to the problem of activity recognition, where 1D kernels are applied to capture local dependency over time in a series of observations measured at inertial sensors (3-axis accelerometers and gyroscopes). In this paper we present a multi-modal CNN where we use 2D kernels in both convolutional and pooling layers, to capture local dependency over time as well as spatial dependency over sensors. Experiments on benchmark datasets demonstrate the high performance of our multi-modal CNN, compared to several state of the art methods.
Starting Page	3017
Ending Page	3022
File Size	733414
Page Count	6
File Format	PDF
e-ISBN	9781479986972
DOI	10.1109/SMC.2015.525
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-10-09
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Kernel Convolution Sensor phenomena and characterization Neural networks Intelligent sensors Feature extraction deep learning Activity recognition convolutional neural networks
Content Type	Text
Resource Type	Article

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

Convolutional Neural Networks for human activity recognition using mobile sensors

Natural scene recognition based on Convolutional Neural Networks and Deep Boltzmannn Machines

Multi-attributes gait identification by convolutional neural networks

Image recognition based on deep learning

Learning Deep Trajectory Descriptor for action recognition in videos using deep neural networks

Deep convolutional neural networks as generic feature extractors

Driving posture recognition by convolutional neural networks

New Sensor Data Structuring for Deeper Feature Extraction in Human Activity Recognition

Convolutional deep belief networks for feature extraction of EEG signal

Multi-modal Convolutional Neural Networks for Activity Recognition

Similar Documents

Convolutional Neural Networks for human activity recognition using mobile sensors

Natural scene recognition based on Convolutional Neural Networks and Deep Boltzmannn Machines

Multi-attributes gait identification by convolutional neural networks

Image recognition based on deep learning

Learning Deep Trajectory Descriptor for action recognition in videos using deep neural networks

Deep convolutional neural networks as generic feature extractors

Driving posture recognition by convolutional neural networks

New Sensor Data Structuring for Deeper Feature Extraction in Human Activity Recognition

Convolutional deep belief networks for feature extraction of EEG signal

Multi-modal Convolutional Neural Networks for Activity Recognition