Authors:
Pekka Siirtola
and
Juha Röning
Affiliation:
University of Oulu, Finland
Keyword(s):
Accelerometer, Sensor Fusion, Activity Recognition, Machine Learning, Mobile Phones.
Related
Ontology
Subjects/Areas/Topics:
Applications
;
Cardiovascular Imaging and Cardiography
;
Cardiovascular Technologies
;
Classification
;
Computer Vision, Visualization and Computer Graphics
;
Feature Selection and Extraction
;
Health Engineering and Technology Applications
;
Human-Computer Interaction
;
Methodologies and Methods
;
Motion and Tracking
;
Motion, Tracking and Stereo Vision
;
Pattern Recognition
;
Physiological Computing Systems
;
Sensors and Early Vision
;
Signal Processing
;
Software Engineering
;
Theory and Methods
Abstract:
In this study, a novel user-independent method to recognize activities accurately in situations where traditional
accelerometer based classification contains a lot of uncertainty is presented. The method uses two recognition
models: one using only accelerometer data and other based on sensor fusion. However, as a sensor fusionbased
method is known to consume more battery than an accelerometer-based, sensor fusion is only used
when the classification result obtained using acceleration contains uncertainty and, therefore, is unreliable.
This reliability is measured based on the posterior probabilities of the classification result and it is studied in
the article how high the probability needs to be to consider it reliable. The method is tested using two data
sets: daily activity data set collected using accelerometer and magnetometer, and tool recognition data set
consisting of data from accelerometer and gyroscope measurements. The results show that by applying the
presented method, t
he recognition rates can be improved compared to using only accelerometers. It was noted
that all the classification results should not be trusted as posterior probabilities under 95% cannot be considered
reliable, and by replacing these results with the results of sensor fusion -based model, the recognition accuracy
improves from three to six percentage units.
(More)