=Paper=
{{Paper
|id=Vol-3248/paper3
|storemode=property
|title=Estimation of Precise Step Lengths by Segmenting the Motion of Individual Steps
|pdfUrl=https://ceur-ws.org/Vol-3248/paper3.pdf
|volume=Vol-3248
|authors=Yuichi Ikeuchi,Daichi Yoshikawa,Kota Tsubouchi,Nobuhiko Nishio
|dblpUrl=https://dblp.org/rec/conf/ipin/IkeuchiYTN22
}}
==Estimation of Precise Step Lengths by Segmenting the Motion of Individual Steps==
https://ceur-ws.org/Vol-3248/paper3.pdf
Estimation of Precise Step Lengths by Segmenting the
Motion of Individual Steps
Yuichi Ikeuchi1 , Daichi Yoshikawa1 , Kota Tsubouchi2 and Nobuhiko Nishio1
1
Ritsumeikan University, Shiga, Japan
2
Yahoo Japan Corporation, Tokyo, Japan
Abstract
Pedestrian dead reckoning (PDR) is one of the representative methods of indoor positioning, and many
researchers have developed PDR methodologies that can accurately estimate step lengths of users. We
propose a novel method to improve the accuracy of the step-length estimation by dividing up a gait
into finer movements than a single step and extracting features. To our knowledge, no other studies
have attempted to improve precision by breaking down the motion of a single step. This is not only
in the step-length estimation, but also in machine learning studies for PDR. We propose two methods
of breaking down a step: one is to segment the step data at the extreme values of the norm of the
acceleration vectors, and the other is to segment the data so that the extreme values are at the middle
points of the segmentation. The step length is estimated by extracting features from each segment. The
results of an evaluation shows that these methods improved accuracy by 10% over the compared method.
Keywords
IMU, gait, step lengths, pedestrian dead reckoning, machine learning
1. Introduction
Location information is used for various purposes such as navigation, analyses of people flow
[1, 2] and purchasing behavior in retail stores [3, 4], and productivity improvement in factories
[5]. GNSS [6] cannot be used as its signals are blocked indoors, and various methods using, e.g.,
Wi-Fi [7, 8], Ultra Wide Band (UWB) [9, 10], and Bluetooth Low Energy (BLE) [11, 12] have
been proposed instead. In particular, pedestrian dead reckoning (PDR) [13, 14, 15] has been
attracting attention because it does not require any external infrastructure.
Machine learning has been used to improve PDR[16, 17, 18, 19], for example, step-length
estimation, walking heading estimation, body heading estimation, walking motion recognition.
These machine learning models were trained using the time windows or one-step windows.
However, this does not take advantage of the characteristics of walking. Since gait is the
repetition of patterns, we thought that the accuracy of these models could be improved by
dividing each steps into smaller segments corresponding to the walking motion.
In this paper, as a first step, we propose a method of precisely estimating the step length.
Using the repetition of gait patterns as a reference, the step-length estimation model that uses
IPIN 2022 WiP Proceedings, September 5 - 7, 2022, Beijing, China
$ yuichi@ubi.cs.ritsumei.ac.jp (Y. Ikeuchi); yossi@ubi.cs.ritsumei.ac.jp (D. Yoshikawa); ktsubouc@yahoo-corp.jp
(K. Tsubouchi); nishio@is.ritsumei.ac.jp (N. Nishio)
© 2022 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
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Workshop
Proceedings
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machine learning was created by the divided gait data. We also analyzed the relationship
between acceleration and motion.
In dividing the gait into smaller units, we referred to the findings of studies on the gait cycle.
The Rancho Los Amigos study [20] is representative; it showed that gait can be divided into
cyclic movements: feet landing, taking off, swinging, and other movements during gait. (The
details are given in section 2.) We aimed to improve the step-length estimation by utilizing
machine learning with this cycle in mind. The proposed method requires an analysis of the
correspondence between the movements and sensor values obtained from devices such as
smartphones. In addition, it is necessary to obtain correct data on walking in order to estimate
the step length by using machine learning.
To deal with the above two problems, we utilized human behavior recognition technology
that has become popular in recent years. This technology is capable of acquiring time series
data on the 3D positions of body parts such as wrists, ankles, shoulders, and chest. For the
former problem, we analyzed the correspondence between body movements obtained using this
recognition technology and sensor values obtained from smartphones carried by pedestrians.
For the latter, we generated correct data on the step length by applying the foot movements
obtained from the recognition technology.
Our contributions are as follows:
• We improved the step-length estimation by analyzing gait in units finer than a single
step.
• We gained insight into useful features when segmenting gait into units finer than a single
step.
• We clarified the correspondence between movements and important features
The rest of this paper is as follows. Section 2 reviews related work. Section 3 describes an
analysis of information useful for splitting the gait and our step-length estimation using it.
Section 4 describes the results of an experiment and section 5 discusses the results. Conclusions
are presented in Section 6.
2. RELATED WORK
PDR has two main elements, estimating the walking distance and estimating the heading. The
walking distance can be estimated using the step length because it is the sum of step lengths.
Numerous methods of estimating step lengths have been proposed for PDR [21, 22, 23, 24].
In this section, we explain the walking distance estimation of previous mechanical PDR and
machine learning PDR (ML-PDR). Next, we describe the methods of collecting training data for
ML-PDR. In addition, we introduce the periodicity of gait referred to for dividing the steps in
our method.
2.1. Walking-distance Estimation
2.1.1. Mechanical PDR
In mechanical PDR, the step length is used as a walking distance of a step. In order to perform a
step-length estimation, steps have to be first detected. This is generally done using the extreme
values of the norm of the acceleration vectors. It is known that the interval from one minimum
value to the next minimum value corresponds to a single step. The step length can be estimated
in a number of ways. Early PDR methods used the average step length of a person, assuming
it to be a fixed value. However, the step length is not constant but varies. Kim et al. [25]
and Weinberg et al. [26] are representative of the approaches that go beyond this assumption.
The method of Kim et al. utilizes the average acceleration during a step, whereas the one of
Weinberg et al. uses the amplitude of the vertical acceleration during a step. Both approaches
calculate the step length mechanically. In addition, since they detect individual steps and output
a value for each step, they take more advantage of the characteristics of walking compared with
a method that uses machine learning.
2.1.2. PDR using machine learning
In ML-PDR, the walking distance is usually calculated using the walking speed and time
[16, 17, 18, 19]. One of the main reasons this is popular is the ease of creating training data.
Walking speed and time are easy to collect training data than step detection and step length
estimates. Asraf et al. [17] input acceleration and angular velocity data into a neural network by
dividing the data into fixed time windows. However, since the time windows are mechanically
segmented, their method does not consider the characteristics of human gait.
Fixed time window is adopted in other research of ML-PDR[18, 19]. In addition, it is difficult
to understand what motions are important for the walking-distance estimation. Klein et al.
[21] input acceleration and angular velocity data into a neural network by dividing them into
variable time windows at each step. Thus, their method extracts features at each step. However,
this method underutilizes the features of walking. By comparison, we focus on the cycle of
walking and use a window size smaller than one step.
2.2. Gait data collection of training for PDR model
When using machine learning for the walking-distance estimation, acceleration data are input
and the walking speed is output. Here, it is necessary to collect training data. Kang [27] et al.
collected it by utilizing GPS. In particular, location and time information obtained from GPS
were used to calculate the walking speed. Feigl [28] et al. used optical markers attached to
pedestrians’ bodies and installed 28 cameras to obtain their positions with millimeter-level
error. Their system could acquire positions at a high sampling rate of 100 Hz. Herath [18] et
al. used the Google Tango device to collect walking speed data. Google Tango can accurately
estimate the relative position of the terminal by using a camera and a depth sensor in addition
to acceleration and angular velocity. They calculated the walking speed from the trajectory of
the device and used it for training.
A few studies have used machine learning to estimate the step length. Klein [21] et al.
collected training data by dividing the total distance by the number of steps. However, the data
generated with this method is very rough. Vandermeeren [29] et al. manually created correct
step-length labels by installing a camera on the ceiling and a tape measure on the ground.
However, it is extremely difficult to obtain the necessary amount of data for training the model
because the labels need to be manually created for each step.
In contrast to the above methods, we obtain correct step-length data by using human behavior
recognition technology. The system was developed by our research team. Specifically the
technology tracks the ankle’s 3D position.
2.3. Gait cycle
The Rancho Los Amigos method [20] is representative of the methods used in studies of the gait
cycle. It splits the gait into two phases. The first phase is the stance phase. The stance phase is
when the feet are on the ground. That is, it is the period from when the feet land and the center
of gravity of a person shifts to when the feet take off. The second phase is the swing phase. The
swing phase is when the foot is in the air. That is, it is the period from the moment the foot
takes off to the moment the foot swings and lands on the ground.
We divide up the gait into periods based only on IMU sensor data. As such, a division as
above is not always possible. Moreover, the Rancho Los Amigos method does not necessarily
give an accurate step-length estimation. Therefore, we searched for a way to divide up a single
step on the basis of acceleration data.
3. Proposed Method
Fig. 1 shows the flow of our method. We thought that using a window, which is finer than
a single step, corresponding to the walking motion leads to improving accuracy of machine
learning models. As a first step, we propose a method of precisely estimating the step length.
Fine Grain Machine Learning Current Research Focus
Angular Velocity Acceleration acceleration
Sec. 3.A
Step Section
Sensor Value Segmentation Recognition
Sec. 3.B
Machine learning Step
Segmentation
Sec. 3.C
Motion Recognition Feature
Extraction
Step Length
Body Heading Sec. 3.D
Step Length
Estimation Model
Walking Heading
Step Length
Figure 1: Proposed fine grain machine learning for PDR and our method of the step length estimation
which is current research focus.
First, step sections are recognized using the extreme values of the norm of the acceleration
vectors. Second, steps are divided into segments smaller than a single step. Third, features are
extracted from these segments. Finally, the machine learning model outputs the step length.
Fig. 2 shows the key idea of our method: feature extraction in every phase. By linking data in
every phase to walking movement, we can get features which we have not extracted.
Figure 2: Dividing a single step into smaller segments.
We put smartphone in the chest pocket to refrain from shaking it. Fig. 3 shows the holding
position of a smartphone and acceleration coordinate system.
Figure 3: Holding position of a smartphone and acceleration coordinate system.
3.1. Step Section Recognition
To divide up a single step, we need to recognize the step sections. Here, we used the x-, y-, and
z-axis acceleration values (𝑥, 𝑦, 𝑧) collected by a smartphone and recognized the sections by
using the minimum values of the norm of the acceleration vectors. The norm of the acceleration
vectors (𝑓𝑐 ) is given as: √︀
𝑓𝑐 = 𝑥2 + 𝑦 2 + 𝑧 2 . (1)
Here, a step section is from one minimum value to the next.
3.2. Dividing Up a Single Step
First, we describe the information that can be used for segmentation. Then, we describe how
the gait is segmented using them.
We analyzed acceleration in units finer than a single step to find an optimal segmentation
method. We did not apply the segmentation method based on gait cycle [20]. This is because we
have not been able to find characteristics of acceleration that correspond to the segmentation
boundaries. In addition, splitting up the gait as per this gait cycle does not necessarily lead to
an accurate estimation of the step length.
3.2.1. Clarification of Information for Gait Segmentation
We examined the x, y, and z-axis acceleration values (𝑥, 𝑦, 𝑧) collected by a smartphone, the
norm of the acceleration vectors, and the horizontal component of acceleration.
The horizontal acceleration (𝑓ℎ ) is given as:
√︀
𝑓ℎ = 𝑥2 + 𝑧 2 . (2)
We found that the extreme values of the norm of the acceleration vectors were useful for
splitting up the gait, as they satisfy the following criteria.
The first criterion is whether the information used for segmentation corresponds to the body
motion. If it occurs at various times, we cannot map the information to a motion even if it is
detected. Therefore, we can’t use such information for segmentation.
The second criterion is the reproducibility of detection. Without a correct detection, it is not
possible to accurately segment a gait. This causes a significant decrease in the accuracy of the
step-length estimation. Therefore, it is necessary to find features with high reproducibility.
We used human behavior recognition technology to determine features that satisfy these
criteria. Fig. 4 shows the analysis of body movements obtained using this technology and
acceleration data to find a segmentation method with high detection reproducibility.
Figure 4: Analysis of body movements obtained using skeleton tracking and acceleration.
The analysis can acquire the 3D positions of ankles, knees, chest, wrists, etc., at about 20 Hz.
We analyzed the correspondence between the body movements and the acceleration. We used
Network Time Protocol (NTP) to synchronize the smartphone that acquires the acceleration
data with the human behavior recognition device. Since walking motion corresponds to foot
motion, we analyzed the correspondence between foot motion and acceleration. We used the
x-axis and z-axis components of the acceleration, the norm of the acceleration vectors, and the
horizontal component acceleration, as shown in Fig. 5. The positions of the ankles are also
shown. We didn’t use the y-axis (vertical) component of the acceleration because it is similar to
the norm of the acceleration vectors.
(a) Sample of X-axis acceleration. It was diffi- (b) Sample of Z-axis acceleration. It was diffi-
cult to detect features closely related to the cult to detect features closely related to the
motion. motion.
(c) Sample of the norm of the acceleration vec-
tors. There was a relationship between the (d) Sample of horizontal component of accel-
movement of the feet and the extreme val- eration. It was difficult to detect features
ues. There was also the reproducibility of closely related to the motion.
the extreme value detection.
Figure 5: Analysis of the correspondence between foot motion and acceleration.
From Fig. 5c, the extreme values of the norm of the acceleration vectors satisfy the two
criteria, so we concluded that it was valid. There is a relationship between the movement of
the feet and the extreme values. We can see that maximal values are reached when one foot is
stationary and the other foot is changing velocity rapidly. Also, we can see that minimum values
are reached just after one foot stops moving and just before the other one starts moving. Thus,
the relationship between the norm of the acceleration vectors and the walking motion is clear.
Fig. 6 shows their correspondence. After examining these results, we considered it appropriate
to use the extreme values of the norm of the acceleration vectors for gait segmentation. On the
other hand, it was difficult to detect features closely related to the motion except for the norm
of the acceleration vectors.
Figure 6: Relationship between the norm of the acceleration vectors and walking motion.
3.2.2. Gait Segmentation Using Extreme Values
In the previous section, we described that the extreme values of the norm of the acceleration
vectors are useful for gait segmentation. In this section, we describe the methods of dividing up
the gait by using these values. Specifically we use them as the segmentation points or middle
points of the segmentations. Fig. 7 shows three methods (A, B, and B’) of doing so.
Method A Method B Method Bʼ
One Step One Step One Step
Figure 7: Gait segmentation using extreme values. (Method A: A step is divided into two phases.
Method B and B’: A step is divided into three phases.)
In method A, the data are segmented with the maximal values and minimum values. A step
is divided into two phases. The minimum values occur between the landing and take-off of the
foot. The maximal values occur at the moment when the foot swings and overtakes the other
foot. Therefore, the two phases mean the following. The first phase is the interval where the
foot swings and overtakes the other foot from the timing near where the foot takes off. The
second phase is the interval from when the foot overtakes the other foot until the foot lands.
In method B, the data are divided up so that the maximal values and minimum values are
at the middle points of the segmentations. A step is divided into three phases. The first phase
is from a little before the previous step lands until a little after the foot takes off. The second
phase is the interval when the foot swings past the other foot. It spans a short time after the
foot takes off to a short time before the foot lands. The third phase is the interval from a little
before the foot lands until a little after the foot takes off to take the next step. In method A, the
step is split when the foot is swinging and when the foot is landing. By comparison, method B
treats when the foot is swinging and when the foot is landing as one phase. It also includes the
end of the previous step and the beginning of the next step.
In method B’, the data are split up at the minimum values. Additionally, the data are divided
up so that the maximal values are at the middle points of the segmentations. Compared with
method B, this method does not include the features of the previous and next steps since it uses
the minimum values to divide up the data.
3.3. Feature Extraction
We extracted features that the machine learning would use to make the estimation model of
the step length. We extracted the features from the raw acceleration (x, y, z-axis), the norm
of the acceleration vectors, and the horizontal component acceleration values. We also used
general features in PDR, specifically, maximum, minimum, and mean value, variance, kurtosis,
and skewness. In addition, we devised our own features, i.e., the time taken for each phase and
a slope value. We supposed that the time taken for each phase, such as the time from when the
foot takes off to when the foot passes by the other foot, might be useful because we thought
that it might affect the step length. The "slope" is the difference between the maximum value
and the minimum value divided by the time (𝑡).
In method B and B’, the central phase has two slopes. The front part is from the beginning
of the central phase to the maximum value. The back part is from the maximum value to the
end of the central phase. Fig. 8 shows that the slope of the norm of the acceleration vectors
obtained by the segmentation would be an effective feature.
Figure 8: Slope of the norm of the acceleration vectors obtained by our method (method B example).
In the case of the first phase or the front part of the central phase, the slopes are given as:
𝑚𝑎𝑥 − 𝑚𝑖𝑛
𝑠= . (3)
𝑡
In the case of the last phase or the back part of the central phase, the slopes are given as:
𝑚𝑖𝑛 − 𝑚𝑎𝑥
𝑠= . (4)
𝑡
3.4. Step-length Estimation Model
We used multiple regression, Xgboost, and support vector machine (SVM) as the machine
learning models. These models were trained with the extracted features described in the
previous section.
4. Evaluation
Here, we explain the experimental conditions, the accuracy of the step-length estimation model
we proposed, the importance of the features for machine learning, and the features of high
importance. These high importance features were used for the analysis of the correspondence
between features and walking motion.
4.1. Experimental Conditions
The subject walked toward the installed LiDAR. To collect the acceleration values, he had
smartphone in his chest pocket and walked.
A Google pixel 3 was used to collect the acceleration values, while LiDAR (Intel RealSense)
was used to collect the training labels for the step length. The skeletal recognition software,
Cubemos, was used to recognize the human skeleton. The sampling rates were about 400 Hz
for the pixel3 and about 30 Hz for the LiDAR. We collected 1065 step-length data ranging from
30 cm to 70 cm from one person.
Step
length
Step
length
Figure 9: Estimated step length using LiDAR.
Here, we describe how the training labels for the step lengths were collected. Point cloud data
were obtained from the LiDAR. Then, the data were transformed into a skeleton by Cubemos.
We utilized the skeleton coordinates of both ankles. Fig. 9 shows the estimated step lengths.
Since the feet stop alternately in walking, we took the distance between the coordinates of the
ankles to be the correct step length.
4.2. Evaluation of Step-length Estimation
We evaluated the accuracy of the step-length estimation by creating a model using the features
obtained by our step-division method. We evaluated four methods, A, B, B’ and all three (Mixed),
in comparison with a one-step segmentation method (One Step). One Step extracted features
every single step and did not divide a step into smaller segments.
We calculated the errors between the correct step length collected by LiDAR and the estimated
step length using the accelerometer. We conducted a 10-fold cross-validation. The evaluation
function was the mean squared error (MSE).
We compared multiple regression, Xgboost, and SVM. Table I shows the errors of the step-
length estimation using Xgboost, which had the highest accuracy.
Mixed improved accuracy by about 10% over that of the compared method. A p value less than
0.01 was considered statistically significant. In addition, all of our segmentation methods were
more accurate than the compared method. We consider that the accuracy improved because
the segmentation of the gait allowed us to extract features finely. In summary, the proposed
method improved the accuracy of step-length estimation; thus, we expect that it can improve
the performance of PDR.
Table 1
Mean error of step-length estimation [cm]
Our methods Compared method
Method A Method B Method B’ Mixed One Step
mean error 10.1 10.2 10.1 9.7 10.8
4.3. Valid features
Fig. 10 show the features that had high importance for Xgboost. Here, importance refers to
the “gain” evaluation metric. In particular, the X-axis acceleration was the most important
feature for both the compared method and the proposed method. In the compared method, the
maximum, minimum, and average values of the x-axis acceleration and the time required for
one step were the most important features. The importance of the time required for one step
was high, as expected. This is because the longer the step length is, the longer the time required
for one step would be.
Similarly, the x-axis acceleration and the time taken for each phase were found to be important
to the proposed method. The y- and z-axis were also found to be important. It seems that valid
features were extracted by dividing the gait into units smaller than a single step.
On the other hand, importance of the "slope" features was not very high. This is likely
because the x-axis acceleration and the time taken for each phase were sufficient information
for estimating the step length. However, we expect that the slope features would become
more important when dealing with more complex behavior such as turning while walking and
stepping sideways.
maximum value of x-axis
minimum value of x-axis
mean value of x-axis maximum value of x-axis, the first phase
minimum value of x-axis, the first phase
variance of x-axis
Time taken for one step Time taken for the first phase
Features
skewness of x-axis mean value of x-axis, the first phase
Time taken for the last phase
kurtosis of x-axis
Features
maximum value of y-axis variance of x-axis, the first phase
a slope value of x-axis mean value of z-axis, the first phase
maximum value of z-axis, the first phase
maximum value of z-axis
skewness of z-axis, the first phase
minimum value of z-axis, the first phase
Feature Importance
(a) Feature importance of the compared method Feature Importance
(One Step). (b) Feature importance of Method A.
maximum value of x-axis, the first phase maximum value of x-axis, the first phase
minimum value of x-axis, the first phase minimum value of x-axis, the first phase
mean value of x-axis, the first phase mean value of x-axis, the first phase
Time taken for the first phase Time taken for the first phase
Features
variance of x-axis, the first phase Time taken for the last phase
Features
Time taken for the central phase variance of x-axis, the first phase
maximum value of y-axis, the first phase kurtosis of x-axis, the first phase
Time taken for the last phase maximum value of y-axis, the first phase
maximum value of z-axis, the first phase maximum value of z-axis, the first phase
minimum value of z-axis, the first phase skewness of x-axis, the first phase
Feature Importance Feature Importance
(c) Feature importance of Method B. (d) Feature importance of Method B’.
maximum value of x-axis, the first phase of method A
minimum value of x-axis, the first phase of method A
mean value of x-axis, the first phase of method A
variance of x-axis, the first phase of method A
Time taken for the first phase of method B
Features
maximum value of z-axis, the first phase of method A
maximum value of y-axis, the first phase of method A
Time taken for the last phase of method A
Time taken for the first phase of method A
kurtosis of x-axis, the first phase of method A
Feature Importance
(e) Feature importance of Method Mixed.
Figure 10: Feature importance.
5. DISCUSSION
5.1. Correspondence between movements and important features
Let us describe the correspondence between the important features determined in the previous
section and movements. Among the segmented features, the interval from the minimum value
to the maximum value is found to be particularly important for method A. This indicates that the
period from the start of kicking to the time when the foot overtakes the other foot is important.
Similarly, the other segmentation patterns show that the features after the foot takes off are
important.
5.2. Application
The result of our method of the step length estimation can adapt to PDR. Fig 11 shows the
estimated trajectory of the conventional PDR, PDR adapted to our method (Our PDR), both
ankles estimated by human behavior recognition technology. The algorithm of the conventional
PDR is adapted to the method proposed by Yoshimi et al. [30]. Our PDR is estimated by result
of their heading estimation and proposed our step length estimation.
m m m
m m m
(a) Conventional PDR (fixed step (b) PDR adapted to our method (c) Correct trajectory of both
length) ankles
Figure 11: The estimated trajectory.
5.3. Limitations
PDR estimates the walking distance and heading when it updates the present position. In
this study, we improved the accuracy of the step-length estimation for updating the walking
distance. However, recognizing the step section correctly needs a highly accurate step detection.
Moreover, we have to deal with various carrying positions of the measuring device and non-
straightforward motions, for example, walking while turning and complex behaviors such as
stepping sideways.
5.3.1. Improving the accuracy of step detection
Step-section recognition will not work unless the steps are detected correctly. In our method, it
worked well because the data were collected while the person was walking. However, if steps
are misdetected, e.g., when the user stops at a location, the step-section recognition will fail.
5.3.2. Change in Heading
In this study, we focused on walking in a straight line. However, in the real world, a pedestrian’s
direction of motion will vary. Therefore, it will be necessary to analyze the angular velocity. In
addition, there is a possibility that the acceleration data may have different characteristics from
that of straight-line motion. Therefore, we will have to examine if we can apply our method to
changes in heading.
5.3.3. Irregularities in Walking
Besides changing heading often, people in the real world walk irregularly, such as by stepping
sideways. Such irregularities in walking have not been well studied, and our method does
not address with this problem. To do so, it will be necessary to distinguish irregular walking
behavior from normal walking behavior.
5.3.4. Various Carrying Positions
We evaluated the accuracy of the step-length estimation in a circumstance where the subject
had a smartphone in his chest pocket. However, people carry smart phones in various locations,
such as in hand bags, so it is necessary to deal with such situations. It is possible to obtain
similar the norm of the acceleration vectors even if the carrying positions are different as long
as the smartphone is approximately fixed relative to the motion of the user, thereby making it
possible to segment the gait. However, this is difficult to do when the hand carrying the phone
is swinging because the norm of the acceleration vectors will not reflect the gait characteristics.
It will be necessary to recognize such situations and deal with them.
6. CONCLUSION
We improved the accuracy of the step-length estimation by extracting features from segments
of steps. We referred to the characteristics of the motion and periodicity of gaits for clues on
how to divide up a step. We used human behavior recognition technology to map acceleration
data to walking motion and create labels for the step length. The method we developed could
extract features in units finer than a step. In particular, it improved accuracy by 10% over the
conventional step-length estimation when Xgboost was used as the machine learning method.
The features that were useful were understandable. In particular, the x-axis acceleration from
when the foot takes off to when the foot swings and overtakes the other foot had a significant
influence. In the future, we will have to deal with heading estimation and non-straightforward
motion.
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