Document Detail


Evaluation of neural networks to identify types of activity using accelerometers.
MedLine Citation:
PMID:  20473226     Owner:  NLM     Status:  In-Process    
Abstract/OtherAbstract:
PURPOSE: To develop and evaluate two artificial neural network (ANN) models based on single-sensor accelerometer data and an ANN model based on the data of two accelerometers for the identification of types of physical activity in adults.
METHODS: Forty-nine subjects (21 men and 28 women; age range = 22-62 yr) performed a controlled sequence of activities: sitting, standing, using the stairs, and walking and cycling at two self-paced speeds. All subjects wore an ActiGraph accelerometer on the hip and the ankle. In the ANN models, the following accelerometer signal characteristics were used: 10th, 25th, 75th, and 90th percentiles, absolute deviation, coefficient of variability, and lag-one autocorrelation.
RESULTS: The model based on the hip accelerometer data and the model based on the ankle accelerometer data correctly classified the five activities 80.4% and 77.7% of the time, respectively, whereas the model based on the data from both sensors achieved a percentage of 83.0%. The hip model produced a better classification of the activities cycling, using the stairs, and sitting, whereas the ankle model was better able to correctly classify the activities walking and standing still. All three models often misclassified using the stairs and standing still. The accuracy of the models significantly decreased when a distinction was made between regular versus brisk walking or cycling and between going up and going down the stairs.
CONCLUSIONS: Relatively simple ANN models perform well in identifying the type but not the speed of the activity of adults from accelerometer data.
Authors:
Sanne I De Vries; Francisca Galindo Garre; Luuk H Engbers; Vincent H Hildebrandt; Stef Van Buuren
Related Documents :
18967466 - Simultaneous spectrophotometric determination of fe and ni with xylenol orange using pr...
10458666 - Modified method for closure of transverse pyelotomy incision in gil-vernet pyelolithoto...
17524616 - Nonlinear analog predictor analysis: a coupled neural network/analog model for climate ...
18272286 - Application of principal component-artificial neural network models for simultaneous de...
24350286 - A systems' biology approach to study microrna-mediated gene regulatory networks.
23175426 - Hardness potential derivatives and their relation to fukui indices.
Publication Detail:
Type:  Journal Article; Research Support, Non-U.S. Gov't    
Journal Detail:
Title:  Medicine and science in sports and exercise     Volume:  43     ISSN:  1530-0315     ISO Abbreviation:  Med Sci Sports Exerc     Publication Date:  2011 Jan 
Date Detail:
Created Date:  2011-04-19     Completed Date:  -     Revised Date:  -    
Medline Journal Info:
Nlm Unique ID:  8005433     Medline TA:  Med Sci Sports Exerc     Country:  United States    
Other Details:
Languages:  eng     Pagination:  101-7     Citation Subset:  IM; S    
Affiliation:
Department of Health Promotion, TNO Quality of Life, Leiden, The Netherlands. sanne.devries@tno.nl
Export Citation:
APA/MLA Format     Download EndNote     Download BibTex
MeSH Terms
Descriptor/Qualifier:

From MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine


Previous Document:  Age and Gender Interactions in Ultra-Endurance Performance: Insight from Triathlon.
Next Document:  Musculoskeletal adaptations to training with the advanced resistive exercise device.