Authors:
Sebastian Baumbach
1
;
Arun Bhatt
2
;
Sheraz Ahmed
3
and
Andreas Dengel
1
Affiliations:
1
German Research Center for Artificial Intelligence (DFKI), University of Kaiserslautern and Germany, Germany
;
2
University of Kaiserslautern and Germany, Germany
;
3
German Research Center for Artificial Intelligence (DFKI), Germany
Keyword(s):
Human Activity Recognition, Sport Activities, Machine Learning, Deep Learning, LSTM.
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
;
Neural Networks
;
Neurocomputing
;
Neurotechnology, Electronics and Informatics
;
Pattern Recognition
;
Physiological Computing Systems
;
Sensor Networks
;
Signal Processing
;
Soft Computing
;
Symbolic Systems
;
Theory and Methods
Abstract:
Human activity recognition has emerged as an active research area in recent years. With the advancement
in mobile and wearable devices, various sensors are ubiquitous and widely available gathering data a broad
spectrum of peoples’ daily life activities. Research studies thoroughly assessed lifestyle activities and are
increasingly concentrated on a variety of sport exercises. In this paper, we examine nine sport and fitness
exercises commonly conducted with sport equipments in gym, such as abdominal exercise and lat pull. We
collected sensor data of 23 participants for these activities, for which smartphones and smartwatches were
used. Traditional machine learning and deep learning algorithms were applied in these experiments in order
to assess their performance on our dataset. Linear SVM and Naive Bayes with Gaussian kernel performs best
with an accuracy of 80 %, whereas deep learning models outperform these machine learning techniques with
an accuracy of 92 %.