2016 @SSU

1. Recognition of Anaerobic based on Machine Learning
using Smart Watch Sensor Data
SooHyun Cho1
, SooWon Lee1*
1
The Graduate School of Software, Soongsil University,
369 Sangdo-Ro, Dongjak-Gu,, Seoul, 156-743, Korea
{SooHyun Cho, SooWon Lee}, seanbrowncho@gmail.com
Abstract. In recent years, there has been an upsurge in research on smart watch
technology. Existing research and commercial applications for machines that
recognize user behaviors have involved measuring aerobic exercise using
physical displacement metrics rather than anaerobic exercise, which involves
recognizing and measuring user behaviors using signal processing techniques or
other instruments. In this paper, we have created a prototypical machine
learning algorithm to measure anaerobic exercise with dumbbells to improve
the recognition of physiologic markers of exercise. To do so, we have chosen
three kinds of anaerobic exercise using dumbbells -- pull-ups, side pulls, and
concentration curls -- to be monitored with a three-axis gyroscope sensor
(motion sensor of a smart watch), a three-axis acceleration sensor, and a support
vector machine (SVM) algorithm. Experimental results indicate a mean
recognition rate of 97.7% with respect to the three kinds of exercise analyzed
here.
Keywords: 3axis-gyroscope, 3axis-accelerometer, svm, machine learning,
smart watch, weightlifting, anaerobic exercise, recognition
1 Introduction
As the use of wearable devices has increased while, at the same time, the performance
of the various sensors that are embedded in smart watches has improved dramatically
in recent years, many studies have attempted to push the utility of wearable devices to
new heights. In particular, research on sensor data that can determine user behaviors,
such as a three-axis gyroscope sensor, and on sports area has also increased. Due to
their ability to make contact with users’ appendages, smart watches can recognize
minute changes in user behaviors much more accurately than other smart devices. In
this study, we utilize smart watch sensor data from a three-axis acceleration sensor
and three-axis gyroscope sensor to classify three types of anaerobic exercise that can
be done with dumbbells. In addition, we propose a classification method for these
*
Corresponding Author: Tel.:82+10-4102-3120; fax:82+2-814-8755.
E-mail address: swlee@ssu.ac.kr
Advanced Science and Technology Letters
Vol.139 (FGCN 2016), pp.112-117
http://dx.doi.org/10.14257/astl.2016.139.25
ISSN: 2287-1233 ASTL
Copyright © 2016 SERSC

2. three anaerobic exercise using support vector machines (SVM), which enact
supervised learning in the learning machine algorithm.
2 Relevant Research
Research on behavior and posture recognition that captures a user’s body movements
has been conducted in computer science literature. Relevant topics include data
mining, machine learning, and image processing, among others. At the same time,
research on behavioral pattern recognition using smart watches has comprised topics
such as signal processing and machine learning methods. In particular, there have
been many studies on behavioral pattern recognition with three-axis acceleration and
three-axis gyroscope sensors in three-dimensional space [1][2].
A previous study using a smart device with a built-in sensor showed a mean
accuracy of more than 90% with a machine learning algorithm programmed to
measure location changes such as going up and down the stairs and walking [3]. In
contrast, a recent study examined the recognition of user behaviors and postures while
biking, fast and slow walking, standing, and sitting, based on a SVM model using
smartphone and wearable sensor data. This methodology exhibited a mean
classification accuracy of 92.49% [4]. Another recent study attempted to classify
location changes by identifying various behaviors including sitting, standing, walking,
and running. To do so, the authors utilized an adaptive Naïve Bayes (A-NB)
algorithm using smartphone sensor data to expand the types of data able to be
processed with a Naïve Bayes algorithm. The average classification accuracy in this
study was 92.96% [5]. However, this study also used a smartphone rather than a
wearable device and attempted to classify only location changes.
In fact, most methods from previous studies for solving classification problems
were approached through SVM, which determines a weighted value that minimizes
the error occurring in categorization by maximizing the distance between two
categories [6]. Alternatively, excellent generalization performance of the SVM using
less learning data has enabled superior classification methods in various fields
compared to those using decision trees and artificial neural networks (ANN) [7].
Previous studies using traditional signal processing techniques rather than data mining
and machine learning monitored user behavior in real time, converted user motion
into camera image and angular velocity data, and compounded these metrics to
recognize falling [8].
3 Proposed Method
3.1 Classification of Exercises
Three exercises that could be done with dumbbells were chosen for this study. The
real time movement’s name is Pull Up, Pull Side and Concentration Curl.
Advanced Science and Technology Letters
Vol.139 (FGCN 2016)
Copyright © 2016 SERSC 113

3. 3.2 Data Collection
The data used in this study was collected using Samsung Galaxy Gear smartwatches.
Specifically, 20 adults over 20 years of age wore smartwatches and downloaded a
smartwatch app that could collect data. Subjects were asked to randomly select
different models of smartwatches (Gear 2, Gear S, or Gear S2) to reduce confounds
from device-related errors. We obtained the raw data from a total of 200 sets for each
exercise by prompting each subject to perform each of the three types of exercise 10
times.
Data collected from the three-axis acceleration sensors and three-axis gyroscope
sensors in the smartwatches were parsed into six streams: a X-axis acceleration
sensor, Y-axis acceleration sensor, Z-axis acceleration sensor, X-axis gyroscope
sensor, Y-axis gyroscope sensor, and Z-axis gyroscope sensor.
3.3 Preprocessing and Feature Extraction
With respect to the six categories of data collected from the smartwatches, we
extracted specific features by calculating and analyzing the maximum, minimum,
average, variance, standard deviation, median, and root mean square (RMS) of each
dataset. In terms of preprocessing, we implemented an I/O for the data collected
through a Java-based program and created a specific feature-set for each exercise. The
extracted features are shown in Fig. 3. A total of 42 dimensional features were created
for one exercise. We then identified and labelled the appropriate class for each feature
(class1 = “pullup”; class2 = “sidepull”; class3 = “concentrationcurl”).
Before the machine learned the SVM model, however, we reduced the feature-set
to a low number using a dimensionality reduction technique for transforming high-
dimensional data into low-dimensional data. We conducted experiments using two
different dimension reduction (DR) methods, principal component analysis (PCA)
and linear discriminant analysis (LDA). The range of dimension reduction was from
one to five dimensions, and both a linear-kernel as well as a rbf-kernel were used in
these experiments. Each kernel comprises a technique for adjusting the hyperplane of
the SVM model according to the distribution and dimension of the input data. Using
each of the two-dimensional reduction techniques – the five dimensions and two
kernels –we evaluated the accuracy of the 20 different experimental models (2 x 5 x
2).
3.4 Experimental Data
There were 200 features extracted per exercise. We conducted experiments by parsing
a total of 600 data points into learning data and verification data in a ratio of 8:2.
3.5 Experimental Environment
The learned classifier, including the SVM, preprocessing module, and dataset -- were
built on a web server, which communicated with the smartwatches using a HTTP
Advanced Science and Technology Letters
Vol.139 (FGCN 2016)
114 Copyright © 2016 SERSC

4. protocol to send and receive data. The smartwatches were linked to the smartphones
via Bluetooth and communicated with each other. The experimental environment is
shown in Table 1.
Table 1. Experimental Environment
Environment
Operating
System
Details
Client
tizen 2.3.1 Samsung Galaxy Gear 2, Gear S, Gear S2
android 4.4 Samsung Galaxy S4
windows 7 java SE7
Server ubuntu 14.04
AWS EC2, nginx 1.4.6, mariadb 5.5.44, python
2.7, flask 0.9, uwsgi 1.9.17.1, sqlalchemy 0.15
3.6 Model Building
We repeated training and testing 10 times for each exercise by parsing the 600
extracted data points into learning and verification data in a ratio of 8:2. Therefore,
the performance of the built model indicated the average value. The performance of
the model is shown in Table 2.
)()()()()()()()()(
)()()(
jihgfecba
cba
Accuracy


 (1)
Table 2. Classification Matrix
E
L
Exercise 1 Exercise 2 Exercise 3
Exercise 1 (a) (e) (f)
Exercise 2 (g) (b) (h)
Exercise 3 (i) (j) (c)
*E: experiment, L: correct answer
Advanced Science and Technology Letters
Vol.139 (FGCN 2016)
Copyright © 2016 SERSC 115

5. 3.7 Experimental Results
In this study, we tested the accuracy of 20 different machine learning models using a
SVM algorithm, various dimension reduction techniques, PCA and LDA kernels, and
dimension reduction techniques. When the SVM model was learned through a dataset
that reduced high-dimensional features to two-dimensional features by using a linear
kernel and PCA algorithm (PCA-SVM-linear; 2 dimensions), this model exhibited a
mean classification accuracy of 97.7%. When comprehensively interpreting these
experiments using the outlined conditions from this study, the linear kernel was
superior to the rbf kernel. In terms of dimensional reduction techniques, the PCA was
more useful than the LDA, given that it exhibited the best classification accuracy
when reducing high-dimensional data to two dimensions. These experimental results
are outlined in Table 3.
Table 3. Experimental Results
Dimension
Reduction
Kernel Dimension Accuracy (%)
PCA linear
1 0.9333
2 0.9777
3 0.8666
4 0.8666
5 0.9000
PCA rbf
1 0.3666
2 0.8333
3 0.2333
4 0.2333
5 0.2333
LDA linear
1 0.4444
2 0.6888
3 0.5555
4 0.0222
5 0.0222
LDA rbf
1 0.2666
2 0.3555
3 0.3111
4 0.3111
5 0.3111
Advanced Science and Technology Letters
Vol.139 (FGCN 2016)
116 Copyright © 2016 SERSC

6. 4 Conclusion
In this study, a mean accuracy of 99.7% was shown when users performed three types
of anaerobic exercises while wearing smartwatches in which a SVM classifier – a
classification model with a supervised learning algorithm for machine learning – was
embedded. Depending on the dimensional reduction technique and kernels utilized to
handle the high-dimensional features used as an input, reporting accuracy greatly
fluctuated. Therefore, we believe that the configuration of features to be used as
learning data when troubleshooting user behavior recognition using a SVM algorithm,
appropriate kernels and dimensional reduction techniques are crucial. Future studies
should attempt to classify more types of anaerobic exercises with different machine
learning algorithms, extract the appropriate variables for classification, and improve
the overall reporting accuracy. Although the SVM algorithm is expected to perform
well using relatively little data, it is necessary to collect more data for future
experiments with other algorithms.
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