Journal of Robotics, Networking and Artificial Life
Vol. 5(3); December (2018), pp. 173–179
DOI: 10.2991/jrnal.2018.5.3.7; ISSN 2405-9021 print; 2352-6386 online
https://www.atlantis-press.com/journals/jrnal

Research Article

A Study on the Lumbar Burden Evaluation of Work using
One Smartphone
Hiroki Tamura1, Keiko Sakurai1,*, Koichi Tanno2, Yasufumi Fuse3
Department of Environmental Robotics, Faculty of Engineering, University of Miyazaki, Miyazaki-shi, Miyazaki, Japan
Department of Electrical and Systems Engineering, University of Miyazaki, Miyazaki-shi, Miyazaki, Japan
3
Miyazaki Prefecture Industrial Technology Center, Miyazaki-shi, Miyazaki, Japan
1
2

ARTICLE INFOABSTRACT

Article History

In this paper, we propose the human lumbar burden evaluation method and state estimation system using a smartphone andan application on agricultural work. The proposed system consists of two functions: (1) “State estimation” has a function of
estimating posture (Stand up, Sit down, Crouch, Walk, etc.,) of the subjects, (2) “Lumbar burden estimation” has a function toestimate the angle of the waist from the angle of the subject’s upper body and calculate the lumbar burden in combination withother parameters. The aim of this paper is to get the data of the subject’s work status in the agricultural field in a simple mannerand quasi-real time and help improve the agricultural work efficiency by constructing the agricultural work burden evaluation
system were using a smartphone. In this paper, we show on the experimental results of two functions and the evaluation of theactual agricultural work.

Received 5 September 2018
Accepted 19 October 2018
Key words
Smartphonelumbar burden
gyro sensor
state estimation
acceleration sensor
JACK© 2018 The Authors. Published by Atlantis Press SARL.
This is an open access article under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

1. INTRODUCTION
In recent years, agricultural workers in Japan have decreased andaging rapidly so it is necessary to review the physical environmentof agricultural work. One solution to reduce this problem, technological developments has been made to reduce the burden of agricultural workers and automation various agricultural machinery.
On the other hand, quantitative comparison of how much newly
developed agricultural machinery reduces the burden of conventional agricultural work is difficult.
Also, it is difficult to measure whether the compressive force ofthe lumbar intervertebral disc during these agricultural workings(hereinafter, lumbar burden) exceeds the lumbar burden allowancevalue 3400 N at work labor defined by the National Institute for
Occupational Safety and Health (NIOSH). Therefore, paying attention to the burden of the lumbar area, the purpose of this thesis is
to construct a work burden evaluation system to evaluate whether
the burden reduction rate due to IT conversion of agricultural
machines and the lumbar burden during agricultural work do not
exceed 3400 N.
To construct the system, we used a smartphone equipped withvarious sensors such as acceleration sensor, gyro sensor. This paper
examines whether the proposed system can be realized with one
smartphone. As related research [1–5], there are the lumbar burdenof construction workers [1,2], walking measurement in the home[3], walking measurement and posture estimation using smartphones [4,5]. However, all of these studies are indoor studies, andin the study of construction workers it is not an estimation of the* Corresponding author. Email: sakurai.keiko.u6@cc.miyazaki-u.ac.jp

lumbar burden of a state with heavy working machines such as
agricultural work.
Also one of the burden analysis software has what is called JACK[6,7]. JACK can create a digital human being as a virtual humanby manual input on the desktop, assign work and assign dynamic
burden analysis. JACK creates a digital human in a stationary state
as shown in Figure 1, adds external force, and can evaluate theburden of that state. However, as a problem of JACK, the burden
cannot be measured either in real time or continuously, so estimation of a lumbar burden with JACK is unsuitable in agriculturalwork. For related research [8,9], there is research to estimate theburden of the waist when carrying objects using JACK. However,these studies are simple transport tasks, not simulations of complex cases such as walking with farm equipment during agriculturalwork. In this thesis, we compared it with measured value in JACK
for judging whether the fixed posture is correct.
In this paper, we investigate whether the estimation of the lumbar
burden in agricultural work is possible. To do the investigation,
we compare the case of using the automatic mowing machine withremote controller and the case of using the mowing machine. The
user’s state is judged with one smartphone. There are five kinds ofuser’s state judgment: stand, walking, crouching, sitting and rotation.
Figure 2 shows the target states of five patterns to be determined.

2. PROPOSED METHOD
In this chapter, we describe the method of estimating the loads thatcan be determined by the state and the estimated lumbar burden
using a smartphone.

174

H. Tamura et al. / Journal of Robotics, Networking and Artificial Life 5(3) 173–179

2.1. Determination of Mounting Position
In this section, the installation position of the smartphone
is explained. In this research, the smartphone to be used is
attached to the chest pocket (Figure 3). There are two reasonsfor that. First, it is possible to judge the condition of walking,sitting, squatting, turning, the second is to be able to calculate
the waist angle.

2.2. Calculate Waist Angle
To evaluate the lumbar burden, the pressure of lumbar intervertebral disc calculated from the waist angle and knee flexion angle.
At this time, the waist angle can estimate from the device inclination angle (upper body angle) of the smartphone attached to thebreast pocket. But knee flexion angle cannot estimate of the smartphone attached to the breast pocket. The knee flexion angle cancalculate from the thigh inclination angle. Hence, it is necessary to
make the constant thigh inclination angle when it is determined
operation. Nevertheless, since the way of walking and crouching isdifferent depends on the person, it is impossible to determine

obviously the inclination angle of the thigh. Therefore, prior to
conducting the experiment, the thigh inclination angle in the walking, rotation, and crouching is confirmed beforehand and used as
the input information. The upper body angle is a, the waist angle isb, and the thigh inclination angle is g, which is shown in Figure 4.
The waist angle was calculated as in Eq. (1).
	  b = 180 – (a + g )(1)

The inclination angle of the device is calculated from the gravitational acceleration obtained by the acceleration sensor (Figure 5).
The device inclination angle (slope angle q ) shown in Figure 6 andits calculation formula is shown in Eq. (2). In addition, gravY andgravZ are defined as the gravitational acceleration of Y- and Z-axis.
The unit is degree.
 gravZ p	   Slope angle q = arc tan × gravY  180

Figure 4 | Three angles used for calculation of lumbar burden

Figure 1 | Digital human model (JACK version 6.1) and lumbar
compression analysis function

Figure 2 | Five kind of state judgment to be judged

Figure 5 | Axial direction of the acceleration sensor

Figure 3 | Installation position of the smartphone

Figure 6 | Tilt angle of the device in the direction of gravity(2)

H. Tamura et al. / Journal of Robotics, Networking and Artificial Life 5(3) 173–179

2.3. Lumbar Burden Estimation Method
In this section, we explain the method of estimating the lumbar
burden. The lumbar burden estimation formula is shown in Eq. (3).
(3)

Fc = U w ´ g ´ cos(Rar ) + Lw ´ g ´ cos(Rar ) - Fa + Fm

The meaning of each parameter is as follows: Fc: lumbar burden(N), Uw: weight above the waist (kg), g: gravitational acceleration(m/s2), Rar: inclination angle of the joint surface of the lumbar vertebrae and the sacrum (rad), Lw: load of object (kg), Fa: abdominalpressure (N), Fm: spinal column standing muscle force (N). The
waist angle and the knee flexion angle are calculated from the body
inclination angle previously input according to the state of the subject and the body angle calculated from the acceleration data of thesmartphone. Substitute these two angles into Eq. (3) and seek the
lumbar burden.
In the lumbar burden estimation during rotation, it is necessary
to consider not only the thigh inclination angle but also the waist
burden in the transition of rotation of the waist. Therefore, JACK
was used to calculate the transition in the lumbar burden during
rotation. Table 1 shows the change in the lumbar burden during
rotation in JACK at the average height and weight [height 172 (cm),weight 67 (kg)] of 30-year-old Japanese general male.
From the JACK measurement results, there was an increasing tendency in the transitions of the lumbar burden during rotation, but
the change was small. Therefore, the burden due to the rotation of
the waist has not been introduced.

2.4. Algorithm for State Determination
The flowchart of the smartphone that attached on the breast pocket
can be seen in Figure 7.
Subject attaches the smartphone to the breast pocket, and resting
for a few seconds in a standing state. The criterion value L (°) of
Table 1 | Transitions in the lumbar burden accompanying waist
rotation with JACK measurement
Rotation angle (°)
Lumbar burden (N)

0
384

10
387

20
386

Figure 7 | Algorithm flowchart

30
391

40
393

175

the subject’s state is determined after performing calibration. The
a is defined the moving average of the vector composite valueof acceleration X, Y, Z given by the user with 20 data. It assume
that the case where a exceeds the threshold value 0.05 is in operation. The operation is a state that waiting for determination ofwalking, rotation, crouching, and sitting. When gyro sensor Yaxis exceeds 0.06 (rad/10 ms), the state turns to the rotation. In
addition, when the angle of the device is below a certain value
during the operation. This means in a crouching or sitting state.
When it is judged that the tilt angle of the device is stabilizes after
the smartphone goes down has been measured, the case where thetilt angle S (°) of the device is equal or more than −15 (°) of
the judgment value it is assumed to be sitting, the exception is
crouching.

2.5. Identification of Walking and Rotation
In this section, we explain the method of identification of walking
and rotation. In order to identify walking and rotation, not only an
acceleration sensor but also a gyro sensor capable of recognizing
rotational motion is used (Figure 8).

2.5.1. Determination of thresholdIn this section, we describe the method of setting the thresholdvalue in the rotation determination. Since a person performs
an action involving rotation while walking, a gyro sensor indicates a reaction. Therefore, it is necessary to identify the state ofwalking and rotation. So we conducted preliminary experiments
to extract feature points from walking and rotation motion. In
this experiment, walking (three steps) and rotation (three times)
actions were performed twice on eight subjects. After completionof the experiment, check the value of the gyro sensor Y-axis used
for the rotation determination and obtain the threshold value.
Experimental results of one subject as an example are shown in
Figure 9.
From Figure 9 the average value of the gyro sensor Y-axis is about
1.0 (rad/10 ms) during walking and about 2.5 (rad/10 ms) during
the rotation determination. Therefore, the average of amplitude
values of the gyro sensor Y-axis at the initial operation of walking
and rotation determination of all 16 data is obtained. The threshold value is determined from the average of the values. The resultis shown in Figure 10.

Figure 8 | Axial direction of gyro sensor

176

H. Tamura et al. / Journal of Robotics, Networking and Artificial Life 5(3) 173–179

Figure 9 | Transition in gyro sensor Y-axis during walking androtation

Figure 11 | Value of gyro sensor in walking and rotation

Figure 12 | State determination in walking and rotation
Figure 10 | Amplitude values of walking and rotation

The average amplitude value in walking was about 0.92 (rad/10 ms),
and the average amplitude value in the rotation was about 3.72
(rad/10 ms). It was judged that the difference between the averageof these two operations was sufficiently large, and the average ofthe two values was 2.32 (rad/10 ms) as the threshold in the rotation
determination.

2.5.2. Verification experiment
In this section, we explain the accuracy verification experiment ofwalking and rotation determination. To distinguish walk determination and turn determination, two subjects were wearing a smartphone in the chest pocket and performed two movements, walking(six steps) and rotation (six times), and verify the threshold value
set this time is adequate. The subject stands for 1 s between thewalking motion and the rotation motion. Take a picture of theexperiment with a video camera and confirm whether it is identified by synchronizing from the taken video and smartphone data.
The experimental result of one subject is shown in Figure 11.
Figure 12 shows the state determination. The vertical axis shows
the state judgment result, and the horizontal axis shows time.
State 0 is standing judgment, states 1, 2, and 6 are state judging,state 3 is walking judgment, state 4 is sitting judgment, state 5 iscrouching judgment, and state 7 judged rotation. The actual operation time by the subject is the time in Figures 11 and 12, thewalking time is 1–5 s, 13–18 s, the rotation time is 6–12, 19–25 s.
When the value of the gyro sensor Y-axis exceeds the thresholdvalue 2.32 (rad/10 ms) from the result of Figure 12, it is understood that the rotation determination is performed. As a result of
the experiment, it is possible to distinguish walking and rotation.

3. EXPERIMENTAL CONDITIONIn this chapter, we describe the two methods. There are the experiments verification, the performance verification of the state determination and accuracy verification of the compression force of thelumbar intervertebral disc.

3.1. Performance Experiment ofState Determination
In this section, we describe the experimental method of the performance verification of the state determination to estimate
lumbar burden. The state determination is necessary for estimating lumbar burden is performed using the thigh inclination angle
in each state measured on each subject. This experiment was conducted three times on eight subjects. Subjects work in the order ofstanding, walking (six steps), rotation (six times), stumbling (onetime), sitting (one time). The interval between each motion is 3 s.
The motion at that time was recorded with a video camera, and the
motion in the movie was taken as a true value. The analysis method
compares the state determination (smartphone value) determinedby the application installed in the smartphone with the true value,H. Tamura et al. / Journal of Robotics, Networking and Artificial Life 5(3) 173–179

and calculates the identification rate when the determination ismade. The calculation method of the identification rate is shownin Eq. (4).
Identification rate (%) =Smartphone value (s)True value (s)× 100 (4)3.2. Estimation Accuracy of Lumbar
Intervertebral Pressing ForceExperimental tools are one smartphone, a mowing machine(5.5 kg), and an automatic mowing machine (80.0 kg). Automatic
mowing machine operates by the remote controller (0.5 kg). The
method is to wear a smartphone in the breast pocket of the subject and get them to do farm work freely. We record that motion
with a video camera and confirm the moving state from this video
data. In this experiment, the burden analysis software JACK 6.1
version (JACK) was use for comparison. JACK is the software thatcan create virtual human on the desktop manually, allocate work tothat virtual human, and can analyze its dynamic burden. We createa digital human model with the agricultural attitude, the load of thesubject in the movie taken during the experiment, and calculate thelumbar burden. The calculated value taken is a true value.

4. RESULTS
In this chapter, we describe the result verification method. There
are the experiments verification, the performance verification of
the state determination and accuracy verification of the compression force of the lumbar intervertebral disc.

177

4.2. Result of Lumbar Intervertebral
Pressing ForceIn this section, we describe the experimental result of the verification accuracy estimation of the lumbar intervertebral disc pressing
force. The lumbar burden estimation result during using mowing
machine indicated in Figure 13a and automatic mowing machineindicated in Figure 13b The vertical axis represents the lumbar
burden (N), and the horizontal axis represents time (min).
From the results in Figure 13a, the maximum value was 3278 N,
the minimum value was 749 N, and the average value was 1704 N.
During using the automatic mowing machine, the maximum value
was 2820 N, the minimum value was 484 N, and the average value
was 882 N. The reason why the lumbar burden was increasedaround 5 min in Figure 13b was because the condition judgment
came in the Crouch as the subject performed the setting of theautomatic mowing machine. The reason that the lumbar burden
is increased around 9 min is because the smartphone shakes when
taking out the smartphone from the subject’s chest pocket.

4.3. Comparison with JACKIn comparison with the JACK, JACK measurement used Figure 14
as a digital human model.
To compare with JACK, we first create a digital human with thesubject of this experiment as a model. Manually reproduce thea4.1. Result of State Determination
In this section, we describe the experimental results of the performance verification of state determination. Table 2 indicatesthe action (Action) actually performed by the subject, the motion
estimation (Estimation) by the proposed method, and its identification rate.
From Table 2 we can see that the overall identification rate was
71.9%. The identification rate of walking and sitting state judgment
was bad, and the identification rate of the rotation and crouching
judgment was good.

b

Table 2 | Distinguish rate of state determination
ActionStanding Walking Rotation Crouching Sitting
Estimation (%)
Standing
100
Walking0
Rotation0
Crouching0
Sitting
0
Else
0
Total

14.9
61.2
0
0
0
31.3

0
0
86.3
0
8.4
13.3

2.8
0
0
85.3
0
16.1

29.5
0
5.6
4.5
26.9
40.7
71.9

Figure 13 | (a) Lumbar burden estimation graph (mowing machine).
(b) Lumbar burden estimation graph (automatic mowing machine)

H. Tamura et al. / Journal of Robotics, Networking and Artificial Life 5(3) 173–179

178

subject’s posture while using the automatic mowing machine andwhile using the mowing machine for the created digital human.
The external force exerted on the subject is added to the humanmodel to analyze the lumbar burden in JACK and compare withthe proposed method. Figure 14a indicates that the image whichusing the automatic mowing machine and Figure 14b shows thatthe image which using the mowing machine. In addition, Figure 14cshows a digital human model created with Figure 14a and d showsa digital human model created with Figure 14b.
The arrow in the image represents the direction of the force. At thetime of remote control operation, 0.5 kg force applied to the hand in
the direction of gravity, and it is necessary to distribute the weight of
5.5 kg during mowing operation. Therefore, we are applying 4 kg to theshoulder and 1.5 kg to the arm. The momentary lumbar burden calculated under the above conditions, and the result showed in Table 3.

a

b

c

Table 3 | Comparison the result of smartphone and JACK

Automatic mowing machine
Mowing machine

Smartphone
(N)

JACK
(N)

Error Error(N) rate (%)

549
1202

526
1394

23
192

4.37
13.77

Although the error between smartphone measurement result
and the JACK measurement result which using the remote controller was small, the error in the result of the mowing machine
was large.

5. CONCLUSION
In this thesis, verification of the state determination estimation andthe lumbar burden estimation method in agricultural work wereconducted using one smartphone.
Regarding the state determination, identifying the state judgment
estimate of five patterns with 71.9% is possible. However, the identification rate average result of walking and sitting determination
got worse. Walking determination depended on state determination about 30%. The reason for this worsening is that since the state
determination is performed using the moving average of 20 data in
the identification of the walking determination, a time lag occurs.
Also, in the sitting determination, there was a lot of misrecognition as the standing determination and the state during determination. We consider that the reason is because the tilt angle used forstanding and sitting determination is calculated with a close value.
In addition, based on the estimation result of the lumbar burdenof agricultural work by the proposed method, we found that the
lumbar burden with the automatic mowing machine was about
800 N lower than that of one with the mowing machine. Hence,
the lumbar burden with the automatic mowing machine burden
was reduced.
Moreover, we also found that the lumbar burden does not exceed
the allowable value 3400 N of the lumbar burden at the time ofagricultural work defined by NIOSH. From this result, we can saythat there is a possibility that the proposed method can be used forevaluating the lumbar region burden of agricultural work.

dFigure 14 | (a) Automatic mowing machine (JACK). (b) Mowing machine(JACK). (c) Automatic mowing machine. (d) Moving machine

According to the comparison result by JACK, the error between
the smartphone measurement value and the JACK value at the timeof using the automatic mowing machine was small. The error
between the smartphone measurement value and the JACK
value at the time of using the mowing machine became large.
This is because the proposed method differs from the JACK
measurement calculation method in that the skeleton of thesubject of the JACK measurement is different from the proposed
method. Although the proposed method models the skeletonof the Japanese, JACK measurement models the skeleton of
the American, so the measurement result is considered to bedifferent.
As a future task, considering a method of calculating the lumbar
burden in an action that takes into consideration the vector direction of motion at the time of agricultural work such as pullingan object and pulling a target object like a mowing machine isconceivable.

H. Tamura et al. / Journal of Robotics, Networking and Artificial Life 5(3) 173–179

ACKNOWLEDGMENT
Thank you for Mr. Maiguma, Mr. Nonaka and Tamura Laboratorythat has supported us in this study.

REFERENCES[1] H. Masafumi, A. Ismail, Proposal of Authentication Method for
Smartphone Using Accelerometer and Gyro Sensor, Research
Report Ubiquitous Computing System (UBI), 2014, pp. 1–8.
[2] N.D. Nath, R. Akhavian, A.H. Behzadan, Ergonomic analysis ofconstruction worker’s body postures using wearable mobile sensors, Appl. Ergon. 62 (2017), 107–117.
[3] S. Mellone, C. Tacconi, L. Schwickert, J. Klenk, C. Becker, L.
Chiari, Smartphone-based solutions for fall detection and prevention: the FARSEEING approach, Z. Gerontol. Geriatr. 45(2012), 722–727.
[4] S. Nishiguchi, M. Yamada, K. Nagai, S. Mori, Y. Kajiwara,
T. Sonoda, et al. Reliability and validity of gait analysis by179

android-based smartphone. Telemed. J E Health. 18 (2012),
292–296.
[5] H. Kashihara, H. Shimizu, H. Houchi, M. Yoshimi, T. Yoshinaga,
H. Irie, A real-time gait improvement tool using a smartphone,
Proceedings of the 4th Augmented Human International
Conference, Stuttgart, Germany, 2013, p. 243.
[6] Jack and Process Human Simulate, Available from: https://www.
plm.automation.siemens.com/en_us/products/tecnomatix/manu­f acturing-simulation/human-ergonomics/jack.shtml(accessed 19 September 2018).
[7] P. Blanchonette, Jack human modelling tool: a review, Air
Operations Division, Defense Science and Technology Organi­zation, Australian Government, Department of Defense, 2010.
[8] W. Zhou, M. Reed, Validation of the human motion simulation
framework: posture prediction for standing object transfer tasks,
Society of Automotive Engineers (SAE) Technical Paper (200901-2284), 2009, pp. 1–10.
[9] J. Santos, J.M. Sarriegi, N. Serrano, J.M. Torres, Using ergonomic
software in non-repetitive manufacturing processes: a case study.
Int. J. Ind. Ergon. 37 (2007), 267–275.

Authors Introduction
Dr. Hiroki Tamura

Dr. Keiko Sakurai
He received his PhD from Kyushu
University, Japan in 2006. He is
a Professor in the Department ofEnvironmental Robotics, Miyazaki
University, Japan. His main researchinterests are Neural Networks
and Optimization Problems. In
recent years, He has the interest in
Biomedical Signal Processing using
Soft Computing.

Dr. Koichi Tanno

She received her PhD from Inter­disciplinary Graduate School of Agri­culture and Engineering, Miyazaki
University, Japan in 2018. She works as
a researcher in Faculty of Engineering
at Miyazaki University. Her main
research interests are biological signal
measurement systems, especially gazeestimation system.

Mr. Yasufumi FuseHe received his PhD from Graduate
School of Science and Technology,
Kumamoto University, Kumamoto,Japan in 1999. He is a Professor in
the Department of Electrical andSystems Engineering. His main
research interests are in analog
integrated circuit design and multiple-valued logic circuit design.
Dr. Tanno is a member of IEEE.

He graduated from Nihon University
School of Science and Engineering in
1991. Since 2001, he is an Engineerof Miyazaki Prefecture Industrial
Technology Center. In 2008 Master’scourse in Miyazaki University graduated. The main fields of research
are Mechanical Engineering, Welfare
Engineering, Ergonomics.