Authors:
Sebastian Baumbach
and
Andreas Dengel
Affiliation:
German Research Center for Artificial Intelligence and University of Kaiserslautern, Germany
Keyword(s):
Sensor Data, Spatial-temporal Data, Data Mining, Naive Bayes.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Biomedical Engineering
;
Biomedical Signal Processing
;
Computational Intelligence
;
Data Manipulation
;
Data Mining
;
Databases and Information Systems Integration
;
Enterprise Information Systems
;
Evolutionary Computing
;
Health Engineering and Technology Applications
;
Human-Computer Interaction
;
Knowledge Discovery and Information Retrieval
;
Knowledge-Based Systems
;
Machine Learning
;
Methodologies and Methods
;
Neurocomputing
;
Neurotechnology, Electronics and Informatics
;
Pattern Recognition
;
Physiological Computing Systems
;
Sensor Networks
;
Signal Processing
;
Soft Computing
;
Symbolic Systems
Abstract:
The trend of mobile activity monitoring using widely available technology is one of the most blooming concepts
in the recent years. It supports many novel applications, such as fitness games or health monitoring. In
these scenarios, activity recognition tries to distinguish between different types of activities. However, only
little work has focused on qualitative recognition so far: How exactly is the activity carried out? In this paper,
an approach for supervising activities, i.e. qualitative recognition, is proposed. The focus lied on push-ups as
a proof of concept, for which sensor data of smartphones and smartwatches were collected. A user-dependent
dataset with 4 participants and a user-independent dataset with 16 participants were created. The performance
of Naive Bayes classifier was tested against normal, kernel and multivariate multinomial probability distributions.
An accuracy of 90.5% was achieved on the user-dependent model, whereas the user-independent model
sc
ored with an accuracy of 80.3%.
(More)