Authors:
Anzah H. Niazi
1
;
Delaram Yazdansepas
1
;
Jennifer L. Gay
1
;
Frederick W. Maier
1
;
Lakshmish Ramaswamy
1
;
Khaled Rasheed
1
and
Matthew Buman
2
Affiliations:
1
University of Georgia, United States
;
2
Arizona State University, United States
Keyword(s):
Actigraph g3x+, Analysis of Variance, Body-worn Accelerometers, Data Mining, Human Activity Recognition, Random Forests, Sampling Rate, Weighted Least Squares, WEKA, Window Size.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Biomedical Engineering
;
Biomedical Signal Processing
;
Data Mining
;
Databases and Information Systems Integration
;
Devices
;
Distributed and Mobile Software Systems
;
Enterprise Information Systems
;
Health Engineering and Technology Applications
;
Health Information Systems
;
Human-Computer Interaction
;
Mobile Technologies
;
Mobile Technologies for Healthcare Applications
;
Neural Rehabilitation
;
Neurotechnology, Electronics and Informatics
;
Pattern Recognition and Machine Learning
;
Physiological Computing Systems
;
Sensor Networks
;
Signal Processing
;
Soft Computing
;
Software Engineering
;
Wearable Sensors and Systems
Abstract:
Accelerometers are the most common device for data collection in the field of Human Activity Recognition
(HAR). This data is recorded at a particular sampling rate and then usually separated into time windows before
classification takes place. Though the sampling rate and window size can have a significant impact on the
accuracy of the trained classifier, there has been relatively little research on their role in activity recognition.
This paper presents a statistical analysis on the effect the sampling rate and window sizes on HAR data classification.
The raw data used in the analysis was collected from a hip-worn Actigraphy G3X+ at 100Hz from 77
subjects performing 23 different activities. It was then re-sampled and divided into windows of varying sizes
and trained using a single data classifier. A weighted least squares linear regression model was developed and
two-way factorial ANOVA was used to analyze the effects of sampling rate and window size for different activity
types and
demographic categories. Based upon this analysis, we find that 10-second windows recorded
at 50Hz perform statistically better than other combinations of window size and sampling rate.
(More)