NDLI: Supervised machine learning scheme for tri-axial accelerometer-based fall detector

Content Provider	IEEE Xplore Digital Library
Author	Leone, A. Rescio, G. Siciliano, P.
Copyright Year	2013
Description	Author affiliation: Inst. for Microelectron. & Microsyst., Lecce, Italy (Leone, A.; Rescio, G.; Siciliano, P.)
Abstract	Fall events can cause trauma, disability and death among older people. Accelerometer-based devices are able to detect falls in controlled environments. The paper presents a computationally low-power approach for feature extraction and supervised clustering for people fall detection by using a 3-axial MEMS wearable accelerometer, managed by an stand-alone PC through ZigBee connection. The paper extends a previous work in which fall events were detected according to a threshold-based scheme. The proposed approach allows to generalize the detection of falls in several practical conditions, after a short period of calibration. The clustering scheme appears invariant to age, weight, height of people and relative positioning area (even in the upper part of the waist), overcoming the drawbacks of well-known threshold-based approaches in which several parameters need to be manually estimated, according to the specific features of the end-user. In order to limit the workload, the specific study on posture analysis has been avoided and a polynomial kernel function is used while maintaining high performance in terms of specificity and sensitivity. The supervised clustering step is achieved by implementing an One-Class Support Vector Machine classifier.
Starting Page	1
Ending Page	4
File Size	1030063
Page Count	4
File Format	PDF
ISBN	9781467346429
ISSN	19300395
DOI	10.1109/ICSENS.2013.6688522
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-11-03
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Acceleration Feature extraction Support vector machines Calibration Accelerometers Kernel Polynomials
Content Type	Text
Resource Type	Article
Subject	Electrical and Electronic Engineering

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

Support Vector Machine for tri-axial accelerometer-based fall detector

Supervised wearable wireless system for fall detection

Weightlessness feature — a novel feature for single tri-axial accelerometer based activity recognition

Detection of daily movements from data collected with two tri-axial accelerometers

Support Vector Machine based activity detection

Real-Time Fall Detecting System Using a Tri-axial Accelerometer for Home Care

Activity recognition from acceleration data based on discrete consine transform and SVM

A framework for human activity recognition based on accelerometer data

HMM-Based Human Fall Detection and Prediction Method Using Tri-Axial Accelerometer

Supervised machine learning scheme for tri-axial accelerometer-based fall detector

Similar Documents

Support Vector Machine for tri-axial accelerometer-based fall detector

Supervised wearable wireless system for fall detection

Weightlessness feature — a novel feature for single tri-axial accelerometer based activity recognition

Detection of daily movements from data collected with two tri-axial accelerometers

Support Vector Machine based activity detection

Real-Time Fall Detecting System Using a Tri-axial Accelerometer for Home Care

Activity recognition from acceleration data based on discrete consine transform and SVM

A framework for human activity recognition based on accelerometer data

HMM-Based Human Fall Detection and Prediction Method Using Tri-Axial Accelerometer

Supervised machine learning scheme for tri-axial accelerometer-based fall detector