平成 26 年度電気関係学会四国支部連合大会 講演論文集 （2014 徳島大学）
2014 SHIKOKU-SECTION JOINT CONVENTION RECORD OF THE INSTITUTES OF ELECTRICAL AND RELATED ENGINEERS (TOKUSHIMA)

1-24
Momentum Evaluation of Musculoskeletal Suﬀers
by Using Feed-Forward Neural Network
Mari TAKABATAKE1
Chihiro IKUTA1
Shuichi AONO2
1
Yoko UWATE
Yoshifumi NISHIO1(Tokushima University1 , Aichi Medical University2 )1. Introduction

and x is a net value of the neuron. In this study, we decidethat the number of neurons in the hidden-layer is 20 andthat the slant coeﬃcient (a) of a sigmoid function is 0.5.
After learning, we input the unlearned data of three different health conditions to the FNN and obtain the responseof each neuron output in the output-layer. In Fig. 2, weshow the neuron output in the output-layer for the unlearned data of three diﬀerent health condition.

Musculoskeletal suﬀers cannot move enough even a simple motion. For the suﬀers, we consider that an evaluationof their health condition is important for increasing motivation of a healing of the disease. In this study, we evaluate the health condition by using an acceleration sensorof iPhone and a feed-forward neural network (FNN) . We
obtain the acceleration time-series data of human motions
by iPhone. We give this data to the FNN and the FNN
learns the classification of the evaluation of the motion by aback propagation algorithm. By this learning, we show that
the FNN obtains an ability of the evaluation of the healthcondition.

2. Proposed method
In the measurement, we use a sensor monitor of theiPhone application. Firstly, the examinee puts the iPhonein the right pocket of pants. Secondly, we obtain the acceleration time-series data by jumping with both feet in theradio exercises. We show an example of the acceleration
time-series data of the healthy person in Fig. 1.

Figure 2: Neuron outputs in diﬀerent health conditions’data.
The vertical axis of this graph shows a value of the output.
Figures 2 (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 output[1]output[120].
Table 1 shows the maximum of this output in the corresponding health conditions. The true classification shouldbe the health (1, 0, 0), the little pain (0, 1, 0), and theintense pain (0, 0, 1). The maximum of the output for unlearned data shows a similar trend to the true classification.

Figure 1: Acceleration time-series data.
We prepare 120 acceleration time-series data for the sakeof leaning data in three diﬀerent health condition (40 health,40 little pain, 40 intense pain).
From the time-series data, we extract only first 30 points
whose variations are larger than 3.0. These 30 points are
inputted to the FNN and the FNN learns the classification of
the health conditions. During the learning, diﬀerent output
patterns are given as teacher patterns for diﬀerent input
data. Namely, for the input data of the health, one of theneurons 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] andoutput[81]-[120] learns 1, respectively.

Table 1: output of unlearned data.
Max. of output
HealthLittle pain
Intense pain

[1]-[40]0.059967
0.3431050.214168[41]-[80]0.272177
0.461883
0.208361[81]-[120]0.150053
0.106716
0.386552

4. Conclusions

3. Simulation

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.

In this study, we use the FNN of three layers and use asigmoid function to an activation function of the neuron.
The sigmoid function is described by Eq. (1).
1,
(1)
Ok =
1 + e−ax
where O is an output of the neuron, a is a slant coeﬃcient,24