Journal of Robotics, Networking and Artificial Life
Vol. 7(1); June (2020), pp. 27–29
DOI: https://doi.org/10.2991/jrnal.k.200512.006; ISSN 2405-9021; eISSN 2352-6386
https://www.atlantis-press.com/journals/jrnal

Research Article

Intention Classification of a User of a Walking Assist Cart
by using Support Vector Machine
Noritaka Sato*, Tomoki Yokotani, Yoshifumi Morita
Department of Electrical and Mechanical Engineering, Nagoya Institute of Technology, Gokiso-cho, Syowa-ku, Nagoya, Aichi 466-8555, Japan

ARTICLE INFOABSTRACTArticle History

To develop better assist function for a walking assist cart, we focused on the prediction of the intention of a user. As the first stepof the research, the forces and torques to the cart from the user’s hands, and the rotational velocities of the wheels are sensing.
And the support vector machine is used for intention classification. As a result, we confirmed that our method was able to predict
the intention of the user with enough accuracy.

Received 10 November 2019
Accepted 20 February 2020
Keywords
Walking assist cart
support vector machine
intention classification
artificial intelligence© 2020 The Authors. Published by Atlantis Press SARL.
This is an open access article distributed under the CC BY-NC 4.0 license (http://creativecommons.org/licenses/by-nc/4.0/).

1. INTRODUCTION

2. DATA COLLECTION

With the improvement of medical technology and living environment, the ratio of elderly people is increasing [1]. Elderly people
have weak legs, so they take less walks and shopping, and tend
to stay home. This makes walking more difficult. Moreover, thenumber of elderly people living alone is increasing, and manyelderly people cannot get help from others. It is essential for theelderly to be able to live independently and comfortably.

In this study, we performed data collection experiments using realmachines for learning SVM. Figure 2 shows the walking vehicle
used in this study. Although this cart has an assist function, theassist function was not used in the data collection experiment.

One way to solve this problem is to use a walking assist cart. To
improve the walking assist cart, some researchers and some companies add an electric assist function to the cart [2,3]. This researchstarted as a joint research with SHINTEC HOZUMI Co., Ltd.
(Miyoshi, Aichi, Japan). Figure 1 shows the current walking assist
cart of SHINTEC HOZUMI Co., Ltd. [4]. This cart can perform
assist control such as deceleration on downhill.
However, there is a problem in the current cart that a sense ofincongruity arises because the assist according to the intentionof the user cannot be performed. In this research, the final goal
is to develop an algorithm that estimates human intention basedon sensor information on a cart and enables an assistant to feel
comfortable.
In this paper, as the first step of the research, we aim to predict thestate of the cart after 200 ms using the wheel speed and the force
on the handles of the cart. Specifically, we use a Support Vector
Machine (SVM) to create a learning model from data collected inadvance and make predictions. We intend to achieve the final goalby increasing the number of motions that can be predicted. Notethat the 200 ms were determined based on discussions with thejoint research partner.
Corresponding author. Email: sato.noritaka@nitech.ac.jp*The motors on the left and right wheels of the cart are equippedwith encoders. In this study, the inexpensive force sensor
PFS055YA251U6 made by Leptrino was attached to the left andright handles as shown in Figure 3.
The subject moved the cart forward, backward, and turn as shownin Figures 4 and 5, and we recorded the forces and torques on theleft and right handles, and the left and right wheel speeds. Theturning radius was 0.5 m. Five subjects performed each operation
three times.
Figure 6 shows an example of the acquired data. When the cart goesstraight from the stop state (between 0.2 and 0.3 s), the value of theforce sensor changes before the cart starts moving. Therefore, wethink that the state of the cart can be estimated by using the information of the current wheel speed and the force sensor.

3. PROPOSED METHOD
In this study, the state of the cart 200 ms ahead (stop, forward,reverse, right turn, or left turn) will be estimated using SVM.
Therefore, the state of the walking car at each time was determinedfrom the acquired data and the video taken during the data collection experiment. After that, the state 200 ms ahead of each time wasdetermined as the correct state for estimation by using SVM.
Figure 7 shows a scatter diagram of the force in the front-rear
direction on the left and right handles after the noise is removed

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N. Sato et al. / Journal of Robotics, Networking and Artificial Life 7(1) 27–29

Figure 1 | Walking assist cart of SHINTEC HOZUMI Co., Ltd.
Figure 5 | Turn motion in the data collection experiment.

Figure 2 | Assist cart used in the data collection experiment.

Figure 6 | Example of acquired data.

Figure 3 | Attaching method of the force sensor.

Figure 7 | Scatter diagram of the force in the front-rear direction on theleft and right handles.

Figure 4 | Forward and backward motion in the data collection experiment.

by the low-pass filter. Here, 0 and −1 mean stop and go straightafter 200 ms, respectively. It was determined that the point in a certain state was within a certain range, and that it could be estimated
200 ms ahead by clustering with SVM. In Figure 7, a graph was
drawn using two representative values, but in actuality, estimationby using SVM was performed using 14 data, including the values ofthe left and right 6-axis force sensors and the left and right wheel
speeds, as inputs.

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more comfortable. The movements were classified by SVM basedon wheel speed and force/torque on the handle. We created an
SVM program using Python library named scikit-learn and applied
it to the collected data. The results showed that the correct answer
rate of 98.4% was obtained as a whole, and that the state of the cart
could be accurately estimated.
Figure 8 | Confusion matrix classified by SVM.

“scikit-learn”, which is the Python library is used for the calculationof the SVM in this study.

In the future, the prediction accuracy at the time of state switching
will be improved, and a predictor that can be used in practical use
will be developed.

CONFLICTS OF INTEREST
4. RESULTS

The authors declare they have no conflicts of interest.

Cross-validation was performed on the data divided into 15 pieces.
Figure 8 shows a confusion matrix classified by SVM. The numberson the label are −1 for forward, 0 for stop, 1 for left turn, 2 for rightturn, and 3 for backward. The numbers in the matrix represent how
many points were output as which labels.
Using this value, the correct answer rate for each cart condition iscalculated as 98.9% for forward, 99.3% for stop, 91.6% for left turn,
89.7% for right turn, and 99.1% for backward. The overall correct
answer rate is 98.4%. From the above, it is understood that the stateof the cart 200 ms ahead can be accurately predicted by the proposed method.

5. CONCLUSION
The final goal of this study was to develop an algorithm that predicts human intentions and assists a cart that makes people feel

REFERENCES[1] Cabinet Office, Government of Japan, White paper on traffic
safety in Japan, 2006, p. 11. Available from: http://www8.cao.
go.jp/koutu/taisaku/h18kou_haku/index_pdf.html.
[2] T. Kurinobu, E.T. Harada, T. Daimon, A. Tomito, S. Iwashita,T. Nomura, et al., The effects of electric walking assistance to awalking cart on safety feeling of older adults, Proceedings of theJapanese Society for Cognitive Psychology, Nishi-ku, Fukuoka,Japan, 2015, p. 91 (in Japanese).
[3] RT-Works Corporation. Available from: http://www.rtworks.co.jp/.
[4] T. Yokotani, N. Sato, T. Shimizu, T. Kato, Technique of visualizing walking motion: identification of a part enabling discrimination of motion of a walking assist device user, Proceedings ofthe Japan Society for System Control Information Conference,Sakyo-ku, Kyoto, Japan, 2018, pp. 263–265 (in Japanese).

AUTHORS INTRODUCTION
Dr. Noritaka Sato
He received Doctor of Engineering
degree from The University of ElectroCommunications in 2009. He was aprogram-specific Assistant Professor ofKyoto University in 2009–2011, and is
an assistant professor of Nagoya Instituteof Technology since 2011. His researchinterests include human robot interaction for rescue robots,rehabilitation-assist robots and industrial robots. He is amember of the IEEE, RSJ and SICE.
Mr. Tomoki Yokotani
He received Master of Engineering degreefrom Nagoya Institute of Technology in 2019.
His research interests include autonomous
control system of rescue robots and humanrobot interaction of robotic system.

Prof. Yoshifumi Morita
He is a professor in the Department ofElectrical and Mechanical Engineeringat Nagoya Institute of Technology. He
received the PhD degree from Nagoya
Institute of Technology in 1998. His current research interests are rehabilitation
support robot/device and robot teaching.