Paper Info
Title
Sensor Placement for Activity Detection Using Wearable Accelerometers
Abstract
Activities of daily living are important for assessing changes in physical and behavioural profiles of the general population over time, particularly for the elderly and patients with chronic diseases. Although accelerometers are widely integrated with wearable sensors for activity classification, the positioning of the sensors and the selection of relevant features for different activity groups still pose interesting research challenges. This paper investigates wearable sensor placement at different body positions and aims to provide a framework that can answer the following questions: (i) What is the ideal sensor location for a given group of activities? (ii) Of the different time-frequency features that can be extracted from wearable accelerometers, which ones are most relevant for discriminating different activity types?
Year
DOI
Venue
2010
10.1109/BSN.2010.23
Body Sensor Networks
Keywords
Field
DocType
relevant feature,different body position,activity classification,activity detection,different activity group,wearable sensor,wearable accelerometers,wearable sensor placement,ideal sensor location,different activity type,different time-frequency feature,sensor placement,time frequency,accelerometers,intelligent sensors,feature selection,activity of daily living,computer networks,feature extraction,biomechanics,wearable computers,bayesian methods
Population,Computer vision,Activity classification,Feature selection,Wearable computer,Intelligent sensor,Accelerometer,Computer science,Activity detection,Artificial intelligence,Body positions,Embedded system
Conference
ISBN
Citations 
PageRank 
978-1-4244-5817-2
56
2.54
References 
Authors
8
4
Name
Order
Citations
PageRank
Louis Atallah134527.12
Benny Lo240337.89
Rachel King31187.35
Guang-Zhong Yang42812297.66