Journal of Robotics, Networking and Artificial Life, Vol. 4, No. 3 (December 2017) 195–200___________________________________________________________________________________________________________Human Skill Quantification for Excavator Operation using Random ForestHiromu Imaji

Graduate School of Engineering, Hiroshima University,
1-4-1, Kagamiyama, Higashihiroshima City, Hiroshima, 739-8527, Japan
E-mail: imaji-hiromu@hiroshima-u.ac.jp
http://www.hiroshima-u.ac.jp
Kazushige Koiwai, Toru Yamamoto

Academy of Science and Technology, Hiroshima University,
1-4-1, Kagamiyama, Higashihiroshima City, Hiroshima, 739-8527, Japan
E-mail: koiwaik@hiroshima-u.ac.jp, yama@hiroshima-u.ac.jp
http://www.hiroshima-u.ac.jp
Koji Ueda, Yoichiro Yamazaki

Global Engineering Center, KOBELCO Construction Machinery CO., LTD.,
2-2-1, Itsukaichikou, Saeki-ku, Hiroshima City, Hiroshima, 731-5161, JapanAbstract
In the construction field, the improvement of the work efficiency is one of important problems. However, the work
efficiency using construction equipment depends on their operation skills. Thus, in order to increase the work
efficiency, the operation skill is required to be quantitatively evaluated. In this study, the Random Forest, one ofmachine learning method, is adopted as the quantitatively evaluation for the operation skill of construction
equipment. Evaluated target is the operation on an excavation to load onto a truck for a hydraulic excavator. TheRandom Forest learns to classify some states by the pilot pressure of skilled worker’s operation. States are definedas ‘dig’, ‘lift’, ‘dump’, ‘reposition’, and ‘idle’. The Random Forest with the learning result of skilled worker isapplied to other worker’s operation. The human skill quantification is verified based on the ‘idle’ state.
Keywords: human skill, machine learning, random forest, hydraulic excavator

operation skills so that it is possible to improve theproductivity in the field of construction industry.
In previous studies, the human skill evaluation basedon the control engineering approch3 has been proposed
and the evaluation of operation skills based oninterviews has been investigated.4 However, thesemethods focus on the qualitative evaluation only and it
is difficult to utilize them to improve the productivity.
In this paper, the quantification method of operation
skills for construction machines based on classificationsof operation state using the Random Forest (RF) is

1. Introduction
In the field of construction industry, the application ofInformation and Communication Technology (ICT) fora construction equipment has been proceeded toimprove
theproductivity,
whichiscalled
1‘i-Construction’ in Japan. Automation technologies areimportant to improve the work efficiency.2 However,work efficiencies of some construction equipment still
depend on operation skills for each worker. Therefore, itis necessary to establish a method to evaluate theCopyright © 2017, the Authors. Published by Atlantis Press.
This is an open access article under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).
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Journal of Robotics, Networking and Artificial Life, Vol. 4, No. 3 (December 2017) 195–200___________________________________________________________________________________________________________proposed. The proposed method is applied to theexcavator operation.
RF is a kind of machine learning methods proposed by
Leo Breiman in 2001.5 RF is constructed by somedecision trees. Since the decision tree is an aggregate ofbranching rules, it is widely used to perform data
mining for the certain classification problems because
the learning result is more readable than other machinelearning methods.6,7 Moreover, RF has the lower
computational cost for the classification.

�𝑖 = �𝑥𝑖𝑖 �𝑗 ∈ 𝐽�𝒙

where 𝐽 is a set of 𝑚�, whose components are natural
numbers selected randomly without overlap among
1,2,3, … , 𝑚.
[Step 1-2] Determination of branching condition
𝚥̅ is defined as the selected index of feature vector
element. 𝑐 is defined as the threshold that divide thetraining data into two. The selected index, 𝚥̅, and thethreshold, 𝑐, have the following conditions:𝚥̅ ∈ 𝐽

2. Proposed methodmin(𝑥𝑖𝚥̅ ) < 𝑐 ≤ max(𝑥𝑖𝚥̅ )2.1. Classification of decision tree
The learning data for the decision tree is comprised ofa set 𝑋 and a set 𝑌. The set 𝑋 consists of featurevectors, 𝒙𝑖 , which are one-dimensional vectors
included the feature of data. The set 𝑌 consists ofnatural number, 𝑦𝑖 , which are labels for theclassification. Those formula are shown by:
𝑋 = {𝒙𝑖 |𝑖 = 1,2, … , 𝑛},𝒙𝑖 = �𝑥𝑖𝑖 �𝑗 = 1,2, … , 𝑚�𝑌 = {𝑦𝑖 |𝑖 = 1,2,3, … , 𝑛},𝑦𝑖 = 1,2,3, … , 𝐾

𝑡𝑖 = {𝒙𝑖 , 𝑦𝑖 }2(6)𝛷(𝑇, 𝑇𝑖 ) = 𝐼(𝑇) − � 𝑃(𝑇𝑘 |𝑇)𝐼(𝑇𝑘 )(7)

𝐼(𝑇) = 1 − � 𝑃2 (𝐶𝑘 |𝑇)

(8)

𝑘=1

K

(1)

𝑘=1

here 𝑃(𝐴|𝐵) is the ratio of elements 𝐴 in 𝐵. 𝐶𝑘 is aset of 𝑡𝑖 with label 𝑘. 𝚥̂ , and 𝑐̂ are obtained with thestate of minimized 𝛷(𝑇, 𝑇𝑖 ).

(2)[Step 1-3] Save training data𝚥̂, 𝑐̂ , and 𝑇 are saved at each node of 𝑁𝑙 .

[Step 1-4] End judgment
When 𝑇1 or 𝑇2 do not satisfy the following
termination condition shown below, Step 1-1 is repeatedwith 𝑇1,2 → 𝑇.
Termination condition:(i) 𝑇1 or 𝑇2 has a only single label.
(ii) The number of data in the data set is less than orequal to a certain value.
(iii) The certain depth is achieved at a branch.

(3)

here, N is donated as the number of training data. N
must be smaller than n.
2.1.1.

(5)

𝑇 is divided into a set 𝑇1 with 𝑥𝑖𝚥̅ ≤ 𝑐 and a set 𝑇2
with 𝑥𝑖𝚥̅ > 𝑐 .Then, 𝚥̅ and 𝑐 which minimize thefollowing 𝛷(𝑇, 𝑇𝑖 ) are obtained.

where 𝑛 is defined as the number of feature vectors, 𝑚
is defined as the dimensions of the feature vectors and𝐾 is defined as the number of labels. A set of training
data, 𝑇 is defined as follows:
𝑇 = {𝑡𝑖 |𝑖 = 1,2,3, … , 𝑁},(4)

Training procedure

2.1.2.

The set of initial training data is shown as 𝑇0 . The
branch part is expressed as a node 𝑁𝑙 , and the initialnode is 𝑁0 . The learning procedure is shown for eachstep below.

Classification procedure

To classify the data, the feature vector of validation is� = �𝑥̅𝑗 �𝑗 = 1,2, … , 𝑚�.
defined as 𝒙
[Step 2-1] Input the feature vector to RF� input to the first node of the trained RF.
𝒙[Step 1-1] Sampling feature vector
The sampled feature vector given as follow:

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Journal of Robotics, Networking and Artificial Life, Vol. 4, No. 3 (December 2017) 195–200___________________________________________________________________________________________________________𝑄 = 𝐿�𝑁(10)

The result of ratio can express the quantification.

3. Experiment Evaluation
3.1. Experimental device

Fig. 1The experiment focused on the quantification of theoperation skill by the hydraulic excavator. As shown inFig. 2, it is known that the general work of loading
dump of earth and sand with a hydraulic shovel isclassified into
four states of "dig", "lift","dump" and "reposition". This study classifies statesof the work into 5 states, which are above states with“idle”. The “idle” state shows no operation.
The operation of the hydraulic excavator is composedof four operations of "bucket", "arm", "boom", andScheme of Random Forest[Step 2-2] Select the node
� , 𝚥̂ and 𝑐̂ , the node is
Based on the value of 𝒙
selected until the end of node.

[Step 2-3] Output of results
The largest number of label 𝑦� in 𝑇 at the end of
node should be the result of classification of thedecision tree.
2.2. Random Forest
The scheme of RF is shown in Fig. 1. The detail of RFis explained below.
2.2.1.

Training of Random Forest

RF is composed of a plurality of decision trees.
Therefore, RF needs to generate training data ofdecision trees. It is assumed that training data is given toRF as eq. (1) and (2). Thus, a training data of thedecision tree in RF is given as follows:𝑋� = {𝒙𝑖 |𝑖 ∈ 𝐼},(9)𝑌� = {𝑦�𝑖 |𝑖 ∈ 𝐼}

Fig. 2 General hydraulic excavator excavation workwhere 𝐼 is a set of 𝑛�, whose components are natural
numbers selected randomly without overlap among
1,2,3, … , 𝑛 . The data sets expressed by eq.(9) isgenerated for the number of decision trees and used fortraining each decision tree. The overlapping between
data of decision trees is allowed.
2.2.2.

Quantification

𝑁 is defined as the total number of decision trees. 𝐿
is defined as the number of decision trees outputting thespecific label. The ratio of 𝐿 in 𝑁 is shown as 𝑄,
below:

Fig. 3 Typical hydraulic system on a excavator

197

Journal of Robotics, Networking and Artificial Life, Vol. 4, No. 3 (December 2017) 195–200___________________________________________________________________________________________________________"swing". These input signals of operations wereacquired as pilot pressures. The pilot pressure is theinput pressure to the valve corresponding to the lever
operation amount. A simple configuration diagram of
the hydraulic system is shown in Fig. 3. In this paper,eight types of pilot pressures, the bucket dig, the bucket
dump, the boom up, the boom down, the arm pull, thearm push, the right swing and the left swing are adoptedas elements of the feature vector to be used for RF.

are shown in Fig. 4. Here, values of pilot pressure arenormalized at the 100% of the maximum value. By
using feature vectors and labels, RF learns labels of 5
states by pilot pressures of a skilled worker. Table 1
shows parameters of the proposed method.
[Step 3]
Each operation data of the unskilled worker, thegeneral worker, and the skilled worker are extracted toclassified by RF. The extracted data of the skilledworker does not include the training data. Moreover,extracted data are almost one cycle of Step 1. Using theResult of Step 2, each extracted data of the unskilledworker, the general worker, and the skilled worker areclassified. At this time, the ratio of the predicted label isobtained. Those extracted data and results ofclassification are shown in Fig. 5, Fig. 6 and Fig. 7
respectively.

3.2. Experimental procedure and result
The experimental procedure in this paper is shown
below.
[Step 1]
Subjects, which are the skilled worker, the general
worker and the unskilled worker, operate the excavator
to perform the excavation and the mimic loading onto adump truck shown in Fig. 2 with several cycles. Theirdata of pilot pressure are obtained.
[Step 2]Training data of pilot pressure as feature vectors areextracted from the data of skilled worker. The extracted
training data is one cycle only. Labels of 5 states, whichare "dig", "lift", "dump", "reposition" and "idle", for the
training to RF is set by the manual processing of data tocheck the change of pilot pressure. Those training data

Table 1 parameters of experiment
Step of training data
Step of verification data
Number of decision trees
Number of sampling data forone decision tree
Number of sampling featurefor one decision tree

4309
4000311436
3Fig. 5 Learning result of skilled worker using RF

Fig. 4 Training data for RF198

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Table 2 Ratio of idleworker
Area Ratio[%]

SkilledGeneral

Unskilled

0.09

5.32

13.7

Skilled
0.15
2.92
× 10−50.26
5.00
× 10−2

General
3.45
1.48
× 10−2
6.74
1.70

Unskilled
13.3
2.06
× 10−2
15.9
10.5

Table 3 Verification result of RF significanceworkeraverage
Variance
MaxMinworker and the unskilled worker have some of the "idle"
state in spite of working. Therefore, the state "idle" isquantified. Table 2 shows the result of quantification,that is the ratio of decision trees with the "idle" state.
Moreover, Table 3 shows the result of training with 50
times using the same input / output data. This resultshows the validity of RF. The ratio of decision trees
with "idle" is getting bigger from the skilled worker tothe unskilled worker in Table 2 and Table 3.
The dump operation that have the larger difference ofthe "idle" state, therefore around dump motions are
extracted in Fig. 8, Fig. 9 and Fig. 10 respectively. In
Fig. 9, the "idle" state appears around 1900 step of thegeneral worker. To see the pilot pressure, the operationof the general worker is only single operation of the arm
push at the appearing "idle" state. The skilled worker
has the almost three operations at the same period. InFig. 10, the unskilled worker performs one or two
operations only. Moreover, no operation is there around3000 steps when a state changes form the dump to thereposition. Those less operation compared with theskilled operator is expressed by the amount of the "idle"
state. Thus, the ratio of "idle" state represents the lack ofoperation in the work.

Fig. 6 Learning result of general worker using RFFig. 7 Learning result of unskilled worker using RF

3.3. Skill quantification

4. Conclusion

Predict labels in Fig. 5, Fig. 6, and Fig. 7 areconsidered as the skill quantification. The skilledworker data is clear to classify each states without the"idle" state. However, predict labels of the general

The human skill quantification for the excavator
operation using the random forest has been proposed.
The random forest learns the data of the skilled operator
as the feature vector and labels of work state. Based on199

Journal of Robotics, Networking and Artificial Life, Vol. 4, No. 3 (December 2017) 195–200___________________________________________________________________________________________________________Fig. 8

the learned random forest, work statements areclassified by using data of each worker, which are theskilled operator, the general operator, and the unskilledoperator. Results of classifications have been considered
and it is clear that the amount of the "idle" state isdifferent. The "idle" state seem to express the lack ofoperation compared with the skilled operator. As thefuture work, other states are classified by using internal
state and output. Moreover, the relation of any modelsuch as the Hidden Markov Model will be researched.
Furthermore, the method can be utilized to improve thework efficiency.

Part of pilot pressure by skilled worker

References

Fig. 9Part of pilot pressure by general worker

Fig. 10

Part of pilot pressure by unskilled worker

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