IEEE Workshop on Nonlinear Circuit Networks
December 12-13, 2014

Proposal of Exercise Evaluation Method ofMusculoskeletal Disorder Sufferers by Using
Feed-Forward Neural Network
Mari Takabatake, Chihiro Ikuta, Shuichi Aono, Yoko Uwate and Yoshifumi Nishio
Dept. of Electrical and Electronic Engineering, Tokushima University,
2-1 Minami-Josanjima,Tokushima, 770-8506 JAPAN
Email: {mari, ikuta, uwate, nishio}@ee.tokushima-u.ac.jp
Multidisciplinary Pain Center, Aichi Medical University
1-1 Yazako-Karimata, Nagakute, Aichi, 480-1195, Japan
Email: aono@aichi-med-u.ac.jp

Abstract—A quantification of pain is not yet discovered. Thepain sometimes informs us about something that threatens aperson’s life. To maintain life, it is very important that weknow the pain. The study of musculoskeletal pain is later thanstudy of pain of the other parts. In this study, we evaluatea momentum and distribute the health condition by using thefeed-forward neural network (FNN). The FNN needs to optimize
parameters for the evaluation. We tune the parameters and show
the performance of the evaluation in each parameter. Finally, weevaluate the unlearned data by using the tuned FNN. And, theFNN evaluate the unlearned data.

I. I NTRODUCTION
The pain sometimes informs us about something that threatens a person’s life. To maintain life, it is very important toknow the pain. The study of musculoskeletal pain is later thanstudy of pain of the other parts[1]. Musculoskeletal disordersufferers cannot move enough even a simple motion. They
limit exercise unconsciously because of pain. Furthermore, it
leads to disuse muscle atrophy. Therefor, we consider that anevaluation of their health condition is important for increasingmotivation of treatment of disease. Furthermore, the evaluation
can be usable for an index to express condition of the disordersufferers.
In this study, we evaluate the health condition by using anacceleration sensor of iPhone and a FNN[2]. 1 We obtain theacceleration time-series data of human motions by iPhone. We
give this data to the FNN, and the FNN learns the classificationof the evaluation of the motion by a back propagation algorithm[3]. We show the FNN model in Fig. 1. By this learning,
we show that the FNN obtains an ability of the evaluation ofthe health condition.
II. P ROPOSED METHOD
In the measurement, we use a sensor monitor of the iPhone
application. Firstly, the examinee puts the iPhone in the rightpocket of pants. Secondly, we obtain the acceleration timeseries data of jump exercize in the radio exercises. We use
1 We have already reported this method in Proc. of SJCIEE, 2014

Fig. 1.

FNN model.

only a vertical component of acceleration data for the input
data. We show an example of the acceleration time-series datain Fig. 2. We prepare 120 acceleration time-series data for thesake of leaning data in three different health conditions (40health, 40 little pain, 40 intense pain). From the time-series
data, we extract only first 30 points whose variations are larger
than 0.3. These 30 points are input to the FNN, and the FNN
learns the classification of the health conditions. During thelearning, different output patterns are given as teacher patterns
for different input data.
Namely, for the input data of the health, one of the neurons
among output{1}-{40} learns 1 and all the rest learn zero.
For the input data of the little pain and the intense pain, only
one of the neurons among output{41}-{80} and output{81}{120} learns 1, respectively. As shown from the Fig. 2, theacceleration time-series data are different each.
III. S IMULATIONIn this study, we use the FNN of three layers and use asigmoid function to an activation function of the neuron. The- 43 -Fig. 3. Result of changing the number of neurons in the hidden-layer.

Acceleration time-series data.

Fig. 2.

sigmoid function is described by Eq. (1)[4].
Ok =1,
1 + e−ax(1)where O is an output of the neuron, a is a slant coefficient,and x is a net value of the neuron.
A. Parameter setting
We change the number of neurons in the hidden-layer andthe slant coefficient (a) of a sigmoid function. Figure 3 and4 show the output data when the number of neurons in thehidden-layer is changed and when the slant coefficient (a) ofa sigmoid function is changed, respectively. The vertical axisof this graph shows a value of the output, and the cross axle
expresses a neuron of the output layer.
We evaluate the unlearned data by the value of the output
data nearing 1 or 0. In Fig. 3, as the number of neurons in
the hidden-layer is increased, the value of the output becomes
small. The number of neurons in the hidden-layer at the time of
10, the value of the output is too inconsistent. It shows that thelearning is insufficient. The number of neurons in the hiddenlayer at the time of 30 or more, the value of the output is toosmall. It shows that the FNN cannot evaluate the unlearning
data by individual difference. In Fig. 4, as the slant coefficient(a) of a sigmoid function is increased, the value of the output
becomes small. The slant coefficient (a) of a sigmoid function
at the time of 1, 2, or more, the value of the output is too

Fig. 4.

Result of changing the slant coefficient (a) of a sigmoidfunction.

small. it shows that the FNN cannot evaluate the unlearning
data by individual difference. From the above, in this study,
we decide that the number of neurons in the hidden-layer is
20 and that the slant coefficient (a) of a sigmoid function is0.5.
B. Health classification of unlearned data
After learning, we input the unlearned data of three different
health conditions to the FNN and obtain the response of eachneuron output in the output-layer. In Fig. 5, we show theneuron output in the output-layer for the unlearned data ofthree different health condition.
Figures 5 (a), (b), and (c) show the output data for healthy
person, little pain person, and intense pain person, respectively.
The horizontal axis of this graph shows output1-output120.
Table 1 shows the maximum of this output in the corresponding health conditions. The true classification should be
the health (1, 0, 0), the little pain (0, 1, 0), and the intense
pain (0, 0, 1). The maximum of the output for unlearned datashows a similar trend to the true classification.
As shown from Fig. 4 and Table 1, the health conditions of
the person are same as expectation at the time of little pain- 44 -IV. C ONCLUSION
In this study, we have proposed the evaluation method of
the health conditions by using the FNN. We succeeded theevaluation of health conditions in unlearned data. However,the difference between the result of the FNN and the trueclassification was still large. In the future work, we will
increase the number of acceleration time-series data and willfind better parameters of the FNN.
ACKNOWLEDGMENTS
This work was partly supported by MEXT/JSPS Grant-inAid for JSPS Fellows (24·10018).
R EFERENCES
Fig. 5.

[1] A.D. Woolf and B. Pfleger, “Burden of Major Musculoskeletal Conditions,” Bull World Health Organ, vol.81, pp.646-656, 2003.
[2] J. Ilonen, J.K. Kamarainen and J. Lampinen, “Differential Evolution
Training Algorithm for Feed-Forward Neural Networks,” Neural Processing Letters, vol.17, pp.93-105, 2003.
[3] D.E. Rumelhart, G.E. Hinton and R.J. Williams, “Learning Representations by Back-Propagation Errors,” vol. 323-9 pp. 533-536 1986.
[4] G. Hiromu, U. Yoshihiro, K. Takeshi and T. Nobuyuki, “Relations Between Sigmoid Function’s Polarity and Convergence in Back Propagation
Learning,” Tecnical Report of IEICE, NC93-ll5, pp25-32, Mar. 1994.

Neuron outputs in different health conditions’ data(1).
TABLE IOUTPUT OF UNLEARNED DATA (1).

Max. of output
HealthLittle pain
Intense pain{1}-{40}0.059967
0.272177
0.150053{41}-{80}0.343105
0.461883
0.106716{81}-{120}0.214168
0.208361
0.386552

and intense pain.
Next, we show the output of the different person in Fig. 6.

Fig. 6.

Neuron outputs in different health conditions’ data(2).

TABLE IIOUTPUT OF UNLEARNED DATA (2).

Max. of output
HealthLittle pain
Intense pain{1}-{40}0.222047
0.102815
0.073592{41}-{80}0.023339
0.096372
0.079239{81}-{120}0.110481
0.059966
0.070356

As shown from Fig. 6, the health conditions of the person
are same as expectation at the time of Health.

- 45 -