NDLI: Estimation of feature importance for food intake detection based on Random Forests classification

Content Provider	IEEE Xplore Digital Library
Author	Fontana, J.M. Farooq, M. Sazonov, E.
Copyright Year	2013
Description	Author affiliation: Dept. of Electr. & Comput. Eng., Univ. of Alabama, Tuscaloosa, AL, USA (Fontana, J.M.; Farooq, M.; Sazonov, E.)
Abstract	Selection of the most representative features is important for any pattern recognition system. This paper investigates the importance of time domain (TD) and frequency domain (FD) features used for automatic food intake detection in a wearable sensor system by using Random Forests classification. Features were extracted from signals collected using 3 different sensor modalities integrated into the Automatic Ingestion Monitor (AIM): a jaw motion sensor, a hand gesture sensor and an accelerometer. Data was collected from 12 subjects wearing AIM in free-living for a 24-hr period where they experienced unrestricted intake. Features from the sensor signals were used to train the Random Forests classifier that estimated the importance of each feature as part of the training process. Results indicated that FD features from the jaw motion signal and TD features from the accelerometer signal were the most relevant features for food intake detection.
Starting Page	6756
Ending Page	6759
File Size	529217
Page Count	4
File Format	PDF
ISBN	9781457702167
ISSN	1557170X
DOI	10.1109/EMBC.2013.6611107
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-07-03
Publisher Place	Japan
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Feature extraction Monitoring Accelerometers Legged locomotion Entropy Frequency-domain analysis Biomedical monitoring
Content Type	Text
Resource Type	Article
Subject	Signal Processing Biomedical Engineering Health Informatics Computer Vision and Pattern Recognition

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

Quantifiable fitness tracking using wearable devices

Energy expenditure estimation using triaxial accelerometry and barometric pressure measurement

Estimation of feature importance for food intake detection based on random forests classification.

Gait feature extraction in Parkinson's disease using low-cost accelerometers

Introducing a modular activity monitoring system

Energy estimation of treadmill walking using on-body accelerometers and gyroscopes

Monitoring Mobility Assistive Device Use in Post-Stroke Patients

Wearable diet monitoring through breathing signal analysis

Dynamic activity classification based on automatic adaptation of postural orientation

Estimation of feature importance for food intake detection based on Random Forests classification

Similar Documents

Quantifiable fitness tracking using wearable devices

Energy expenditure estimation using triaxial accelerometry and barometric pressure measurement

Estimation of feature importance for food intake detection based on random forests classification.

Gait feature extraction in Parkinson's disease using low-cost accelerometers

Introducing a modular activity monitoring system

Energy estimation of treadmill walking using on-body accelerometers and gyroscopes

Monitoring Mobility Assistive Device Use in Post-Stroke Patients

Wearable diet monitoring through breathing signal analysis

Dynamic activity classification based on automatic adaptation of postural orientation

Estimation of feature importance for food intake detection based on Random Forests classification